Publications

This paper is aimed to develop ultrananoporous polarized hydroxyapatite (HAp) catalyst and evaluate its per-formance in transforming CO2 into useable ethanol considering three different scenarios: 1) a batch reaction using a mixture of CO2 and CH4 as feeding gas; 2) a batch reaction using as reactant exhaust gases captured from the fumes of diesel vehicles; and 3) a continuous flow reaction using pure CO2 as feeding gas. Ultrananoporous HAp scaffolds were prepared using a four-step process: 1) as prepared HAp powder was mixed with 60% wt. of a commercial hydrogel at low-temperature; 2) the resulting paste was shaped at low temperature to reduce the adhesion between the metallic tools and the mixture, enhancing the homogeneity of the sample; 3) the shaped paste was calcined in air by applying 1000 oC during 2 h to eliminate the hydrogel; and 4) an external DC electric field of 3 kV/cm was imposed at 1000 oC during 1 h to the calcined scaffold. The resulting polarized scaffolds both ultrananoporosity and catalytic activation. Thus, the mass: volume ratio of the ultrananoporous catalyst was much lower than that of conventional HAp catalyst (718 vs 5093 g/L. Furthermore, the ethanol yield was much higher (up to a factor of x21.4) for the ultrananoporous catalyst than for the compact one, allowing us to conclude that ultrananoporous polarized HAp catalyst is a promising technology for transforming CO2 into valuable chemical products from highly polluted gases, especially those coming from road, sea and air transport.

JTD Keywords: A: ceramics, Air pollution, Automotives, Batch reactions, Calcination, Carbon, Carbon dioxide, Co2 fixation, Co2 reduction, Desig, Electric fields, Environmental process, Ethanol, Exhaust gases, Feeding gas, Fumes, Hydrogels, Hydroxyapatite, Lows-temperatures, Nano-catalyst, Nanocatalysts, Polarized catalys, Polarized catalyst, Scaffolds, Temperature, ]+ catalyst

The mutual interaction between reactive species generated by non-thermal plasma and biopolymers in solution causes oxidative modifications that can have an impact in biomedical applications.

JTD Keywords: atmospheric plasma, cellulose, dftb3, gas, oxidation, parameterization, simulations, water, Biopolymers, Hydrogen peroxide, Molecular dynamics simulation, Molecular-dynamics, Nitrites, Reactive oxygen species

JTD Keywords: coverage path planning (cpp), gas distribution map (gdm), gas source localization (gsl), metal oxide semiconductor sensor, quality, robot, system, Nano-quadcopter, Source localization

Lafora disease is a rare disorder caused by loss of function mutations in either the EPM2A or NHLRC1 gene. The initial symptoms of this condition are most commonly epileptic seizures, but the disease progresses rapidly with dementia, neuropsychiatric symptoms, and cognitive deterioration and has a fatal outcome within 5–10 years after onset. The hallmark of the disease is the accumulation of poorly branched glycogen in the form of aggregates known as Lafora bodies in the brain and other tissues. Several reports have demonstrated that the accumulation of this abnormal glycogen underlies all the pathologic traits of the disease. For decades, Lafora bodies were thought to accumulate exclusively in neurons. However, it was recently identified that most of these glycogen aggregates are present in astrocytes. Importantly, astrocytic Lafora bodies have been shown to contribute to pathology in Lafora disease. These results identify a primary role of astrocytes in the pathophysiology of Lafora disease and have important implications for other conditions in which glycogen abnormally accumulates in astrocytes, such as Adult Polyglucosan Body disease and the buildup of Corpora amylacea in aged brains.

JTD Keywords: abnormal glycogen, accumulation, aggregation, bodies, branching enzyme deficiency, corpora-amylacea, epilepsy, glycogen, lafora disease, mice, mouse model, neurodegeneration, neuroinflammation, progressive myoclonus epilepsy, ubiquitin ligase, Glycogen, Neuroinflammation, Polyglucosan body disease

Permanently polarized hydroxyapatite (p-HAp) has been reported as a feasible green alternative to conventional catalysts for the selective conversion of CO2 into highly valuable chemical products. However, structural control and enhanced electrical properties achieved on p-HAp clearly contrast with other reported catalytic systems, where hydroxyapatite mainly acts as a support receiving much less attention. In this work we take advantage of the knowledge obtained on p-HAp to develop an HAp-based catalytic system composed of TiO2 nanoparticles deposited on p-HAp. It is important to stress that p-HAp is not only considered as a mechanical support but has been put in the spotlight for catalyst preparation and as an active catalytic part. Therefore, the use of p-HAp in this system has unveiled exceptional synergies with TiO2 attributed to the enhanced electrical properties of p-HAp, capable of attracting the photo-electrons generated in TiO2 nanoparticles avoiding electron-hole recombination. CO2 fixation reactions carried out under mild conditions (120 degrees C, 6 bar and under UV exposure) result in complete selectivity control of the C2 products, shifting from ethanol (201 mu mol g(catalyst)(-1)) for p-HAp alone to acetic acid (381 mu mol g(catalyst)(-1)) when TiO2 nanoparticles are loaded in the system. Considering the challenging CO2 activation energy and the high control of the selectivity achieved, we do believe that this novel approach can be considered as a starting point to explore other systems and reactions where control of the crystal structure and the enhanced electrical properties of HAp can play a crucial role in the final products, reaction conditions, yields and selectivities.

JTD Keywords: Behavior, Cobalt, Conversion, Methane, Ni, Oxidation, Performance, Reduction, Syngas production, Tio2

JTD Keywords: copper, emissions, ethanol productions, greenhouse gases, solar energy, sustainable catalysts, Environmental pollution, Reduction

Mesenchymal condensation is a prevalent morphogenetic transition that is essential in chondrogenesis. However, the current understanding of condensation mechanisms is limited. In vivo, progenitor cells directionally migrate from the surrounding loose mesenchyme towards regions of increasing matrix adherence (the condensation centers), which is accompanied by the upregulation of fibronectin. Here, we focused on the mechanisms of cell migration during mesenchymal cell condensation and the effects of matrix adherence. Dendrimer-based nanopatterns of the cell-adhesive peptide arginine-glycine-aspartic acid (RGD), which is present in fibronectin, were used to regulate substrate adhesion. We recorded collective and single-cell migration of mesenchymal stem cells, under chondrogenic induction, using live-cell imaging. Our results show that the cell migration mode of single cells depends on substrate adhesiveness, and that cell directionality controls cell condensation and the fusion of condensates. Inhibition experiments revealed that cell-cell interactions mediated by N-cadherin (also known as CDH2) are also pivotal for directional migration of cell condensates by maintaining cell-cell cohesion, thus suggesting a fine interplay between cell-matrix and cell-cell adhesions. Our results shed light on the role of cell interactions with a fibronectin-depositing matrix during chondrogenesis in vitro, with possible applications in regenerative medicine. This article has an associated First Person interview with the first author of the paper.© 2022. Published by The Company of Biologists Ltd.

JTD Keywords: alpha-v-beta-3, arginine-glycine-aspartic acid, chondrogenesis, dynamics, expression, fibronectin, gastrulation, involvement, mechanisms, mesenchymal condensation, model, nanopatterned substrates, rgd, Arginine-glycine-aspartic acid, Cell migration, Chondrogenesis, Mesenchymal condensation, N-cadherin, Nanopatterned substrates, Rgd

Hydrogels have been recently proposed as suitable materials to generate reactive oxygen and nitrogen species (RONS) upon gas-plasma treatment, and postulated as promising alternatives to conventional cancer therapies. Acting as delivery vehicles that allow a controlled release of RONS to the diseased site, plasma-treated hydrogels can overcome some of the limitations presented by plasma-treated liquids in in vivo therapies. In this work, we optimized the composition of a methylcellulose (MC) hydrogel to confer it with the ability to form a gel at physiological temperatures while remaining in the liquid phase at room temperature to allow gas-plasma treatment with suitable formation of plasma-generated RONS. MC hydrogels demonstrated the capacity for generation, prolonged storage and release of RONS. This release induced cytotoxic effects on the osteosarcoma cancer cell line MG-63, reducing its cell viability in a dose-response manner. These promising results postulate plasma-treated thermosensitive hydrogels as good candidates to provide local anticancer therapies.

JTD Keywords: Case-control studies, Cellulose, Hydrogels, Methylcellulose, Phase-separation, Plasma gases, Reactive oxygen species, Stability, Substituent, Temperature, Thermoreversible gelation

We introduce a novel mesoscopic computational model based on a multiphase-multicomponent lattice Boltzmann method for the simulation of self-phoretic particles in the presence of liquid-liquid interfaces. Our model features fully resolved solvent hydrodynamics, and, thanks to its versatility, it can handle important aspects of the multiphysics of the problem, including particle wettability and differential solubility of the product in the two liquid phases. The method is extensively validated in simple numerical experiments, whose outcome is theoretically predictable, and then applied to the study of the behavior of active particles next to and trapped at interfaces. We show that their motion can be variously steered by tuning relevant control parameters, such as the phoretic mobilities, the contact angle, and the product solubility. Published under an exclusive license by AIP Publishing.

JTD Keywords: Colloids, Equation, Gas, Numerical simulations, Particulate suspensions

Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

The impact of diet and digestive disorders in flatus composition remains largely unexplored. This is partially due to the lack of standardized sampling collection methods, and the easy atmospheric contamination. This paper describes a method to quantitatively determine the major gases in flatus and their application in a nutritional intervention. We describe how to direct sample flatus into Tedlar bags, and simultaneous analysis by gas chromatography–thermal conductivity detection (GC–TCD). Results are analyzed by univariate hypothesis testing and by multilevel principal component analysis. The reported methodology allows simultaneous determination of the five major gases with root mean measurement errors of 0.8% for oxygen (O2), 0.9% for nitrogen (N2), 0.14% for carbon dioxide (CO2), 0.11% for methane (CH4), and 0.26% for hydrogen (H2). The atmospheric contamination was limited to 0.86 (95% CI: [0.7–1.0])% for oxygen and 3.4 (95% CI: [1.4–5.3])% for nitrogen. As an illustration, the method has been successfully applied to measure the response to a nutritional intervention in a reduced crossover study in healthy subjects. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

JTD Keywords: breath, colonic microbiota, diet effect on flatus, disorders, evacuation, excretion, flatulence, hydrogen gas, major flatus gas components, multilevel principal component analysis, rectal gas collection, systems, volume, Atmospheric contamination, Carbon dioxide, Conductivity detection, Diet effect on flatus, Gas chromatography, Gas collections, Gas component, Gases, Major flatus gas component, Major flatus gas components, Multilevel principal component analyse, Multilevel principal component analysis, Multilevels, Nitrogen, Nutrition, Oxygen, Principal component analysis, Principal-component analysis, Rectal gas collection, Volatile organic-compounds

Despite tremendous progress in the understanding of COVID-19, mechanistic insight into immunological, disease-driving factors remains limited. We generated maVie16, a mouse-adapted SARS-CoV-2, by serial passaging of a human isolate. In silico modeling revealed how only three Spike mutations of maVie16 enhanced interaction with murine ACE2. maVie16 induced profound pathology in BALB/c and C57BL/6 mice, and the resulting mouse COVID-19 (mCOVID-19) replicated critical aspects of human disease, including early lymphopenia, pulmonary immune cell infiltration, pneumonia, and specific adaptive immunity. Inhibition of the proinflammatory cyto-kines IFN? and TNF substantially reduced immunopathology. Importantly, genetic ACE2-deficiency completely prevented mCOVID-19 development. Finally, inhalation therapy with recombinant ACE2 fully protected mice from mCOVID-19, revealing a novel and efficient treatment. Thus, we here present maVie16 as a new tool to model COVID-19 for the discovery of new therapies and show that disease severity is determined by cytokine-driven immunopathology and critically dependent on ACE2 in vivo. © Gawish et al.

JTD Keywords: covid-19 mouse model, covid-19 therapy, cytokine storm, immunology, inflammation, mavie16, mouse, mouse-adapted sars-cov-2, program, recombinant soluble ace2, tmprss2, Adaptive immunity, Angiotensin converting enzyme 2, Angiotensin-converting enzyme 2, Animal, Animal cell, Animal experiment, Animal model, Animal tissue, Animals, Apoptosis, Article, Bagg albino mouse, Breathing rate, Bronchoalveolar lavage fluid, C57bl mouse, Cell composition, Cell infiltration, Controlled study, Coronavirus disease 2019, Coronavirus spike glycoprotein, Covid-19, Cytokeratin 18, Cytokine production, Dipeptidyl carboxypeptidase, Disease model, Disease models, animal, Disease severity, Drosophila-melanogaster, Enzyme linked immunosorbent assay, Expression vector, Flow cytometry, Gamma interferon, Gene editing, Gene expression, Gene mutation, Genetic engineering, Genetics, Glycosylation, High mobility group b1 protein, Histology, Histopathology, Immune response, Immunocompetent cell, Immunology, Immunopathology, Interferon-gamma, Interleukin 2, Metabolism, Mice, inbred balb c, Mice, inbred c57bl, Mouse-adapted sars-cov-2, Myeloperoxidase, Neuropilin 1, Nonhuman, Nucleocapsid protein, Pathogenicity, Peptidyl-dipeptidase a, Pyroptosis, Recombinant soluble ace2, Renin angiotensin aldosterone system, Rna extraction, Rna isolation, Sars-cov-2, Severe acute respiratory syndrome coronavirus 2, Spike glycoprotein, coronavirus, T lymphocyte activation, Trabecular meshwork, Tumor necrosis factor, Virology, Virus load, Virus replication, Virus transmission, Virus virulence

Under intrauterine growth restriction (IUGR), abnormal attainment of the nutrients and oxygen by the fetus restricts the normal evolution of the prenatal causing in many cases high morbidity being one of the top-ten causes of neonatal death. The current gold standards in hospitals to detect this relevant problem is the clinical observation by echography, cardiotocography and Doppler. These qualitative techniques are not conclusive and requires risky invasive fetal scalp blood testing and/or amniocentesis. We developed micro-implantable multiparametric electrochemical sensors for measuring ischemia in real time in fetal tissue and vascular. This implantable technology is designed to continuous monitoring for an early detection of ischemia to avoid potential fetal injury. Two miniaturized electrochemical sensors were developed based on oxygen and pH detection. The sensors were optimized in vitro under controlled concentration, to assess the selectivity and sensitivity required. The sensors were then validated in vivo in the ewe fetus model, by means of their insertion in the muscle leg and inside the iliac artery of the fetus. Ischemia was achieved by gradually obstructing the umbilical cord to regulate the amount of blood reaching the fetus. An important challenge in fetal monitoring is the detection of low levels of oxygen and pH changes under ischemic conditions, requiring high sensitivity sensors. Significant differences were observed in both; pH and pO(2) sensors under changes from normoxia to hypoxia states in the fetus tissue and vascular with both sensors. Herein, we demonstrate the feasibility of the developed sensors for future fetal monitoring in medical applications.

JTD Keywords: electrochemical biosensor, implantable sensor, in vivo validation, ischemia detection, tissue and vascular monitoring, Animal experiment, Animal model, Animal tissue, Article, Blood-gases, Brain, Classification, Controlled study, Diagnosis, Doppler, Early diagnosis, Electrochemical analysis, Electrochemical biosensor, Ewe, Feasibility study, Female, Fetus, Fetus disease, Fetus monitoring, Gestational age, Hypoxemia, Iliac artery, Implantable sensor, In vivo validation, Intrauterine growth restriction, Intrauterine growth retardation, Ischemia detection, Leg muscle, Management, Nonhuman, Oxygen consumption, Ph, Ph and oxygen detection, Ph measurement, Process optimization, Sheep, Tissue and vascular monitoring, Umbilical-cord occlusion

Quantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.© 2021 The Author(s).

JTD Keywords: biofiltration, calibration transfer, chemical sensor arrays, chemistry, drift compensation, engineering, environmental chemical engineering, h2s, model, oxide gas sensors, removal, sensor, system, Chemistry, Engineering, Environmental chemical engineering, Sensor, Sensor system, Variable selection methods

The importance of PEG architecture on nanoparticle (NP) functionality is known but still difficult to investigate, especially at a single particle level. Here, we apply DNA Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT), a super-resolution microscopy (SRM) technique, to study the surface functionality in poly(lactide-co-glycolide)-poly(ethylene glycol) (PLGA-PEG) NPs with different PEG structures. We demonstrated how the length of the PEG spacer can influence the accessibility of surface chemical functionality, highlighting the importance of SRM techniques to support the rational design of functionalized NPs.

JTD Keywords: chain-length, density, plga, surface, systems, Chain-length, Density, Dna, Microscopy technique, Nanoparticles, Nanoscale topography, Paint, Peg spacers, Plga, Poly lactide-co-glycolide, Poly-lactide-co-glycolide, Polyethylene glycols, Polylactide-co-glycolide, Single-particle, Super-resolution microscopy, Superresolution microscopy, Surface, Surface chemicals, Surface functionalities, Systems

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can equally offer protection from harm and bring enjoyment in equal measure. It is the system’s ability to detect and analyze complex odors, without the need for specialized infra-structure, that is the envy of many scientists. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the 20th Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the 21st Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late 21st Century.

JTD Keywords: air-quality, breath analysis, calibration transfer, chemical sensor arrays, chemosensor arrays, drift compensation, electronic nose, gas sensors, headspace sampling, machine learning, machine olfaction, odor detection, plume structure, voc analysis, Artificial olfaction, Electrodes, Electronic nose, Electronic nose technology, Headspace sampling, Instruments, Machine learning, Machine olfaction, Monitoring, Odor detection, Olfactory, Sensor phenomena and characterization, Sensors, Temperature sensors, Voc analysis

Cold atmospheric plasma (CAP) is a potential anticancer therapy. CAP has cytotoxic effects when applied either directly to cancer cell cultures or indirectly through plasma-conditioned liquids. This protocol describes how to treat adherent cultures of human cancer cell lines with CAP or plasma-conditioned medium and determine cell viability following treatment. The protocol also includes details on how to quantify the reactive oxygen and nitrogen species present in medium following CAP treatment, using chemical probes using UV-visible or fluorescence spectroscopy. CAP treatment takes ~30 min, and 3 h are required to complete quantification of reactive oxygen and nitrogen species. By providing a standardized protocol for evaluation of the effects of CAP and plasma-conditioned medium, we hope to facilitate the comparison and interpretation of results seen across different laboratories. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

JTD Keywords: bacteria, decontamination, jet, skin, surface, Cell line, tumor, Humans, Neoplasms, Physical plasma, Plasma gases

Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-toreach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement.

JTD Keywords: air pollution, environmental monitoring, gas sensors, industrial emissions, mapping, odour, uav, Air pollution, Drone, Environmental monitoring, Gas sensors, Industrial emissions, Mapping, Odour, Sensors, Uav

Bioinspired hybrid soft robots that combine living and synthetic components are an emerging field in the development of advanced actuators and other robotic platforms (i.e., swimmers, crawlers, and walkers). The integration of biological components offers unique characteristics that artificial materials cannot precisely replicate, such as adaptability and response to external stimuli. Here, we present a skeletal muscle–based swimming biobot with a three-dimensional (3D)–printed serpentine spring skeleton that provides mechanical integrity and self-stimulation during the cell maturation process. The restoring force inherent to the spring system allows a dynamic skeleton compliance upon spontaneous muscle contraction, leading to a cyclic mechanical stimulation process that improves the muscle force output without external stimuli. Optimization of the 3D-printed skeletons is carried out by studying the geometrical stiffnesses of different designs via finite element analysis. Upon electrical actuation of the muscle tissue, two types of motion mechanisms are experimentally observed: directional swimming when the biobot is at the liquid-air interface and coasting motion when it is near the bottom surface. The integrated compliant skeleton provides both the mechanical self-stimulation and the required asymmetry for directional motion, displaying its maximum velocity at 5 hertz (800 micrometers per second, 3 body lengths per second). This skeletal muscle–based biohybrid swimmer attains speeds comparable with those of cardiac-based biohybrid robots and outperforms other muscle-based swimmers. The integration of serpentine-like structures in hybrid robotic systems allows self-stimulation processes that could lead to higher force outputs in current and future biomimetic robotic platforms. Copyright © 2021 The Authors, some rights reserved;

JTD Keywords: actuators, design, fabrication, mechanics, mems, myotubes, platform, tissue, 3d printers, Agricultural robots, Biological components, Biomimetic processes, Electrical actuation, Geometrical stiffness, Intelligent robots, Liquefied gases, Liquid-air interface, Mechanical integrity, Mechanical stimulation, Muscle, Muscle contractions, Phase interfaces, Robotics, Serpentine, Springs (components), Threedimensional (3-d)

© 2020 Wiley-VCH GmbH In recent years, magnetism has gained an enormous amount of interest among researchers for actuating different sizes and types of bio/soft robots, which can be via an electromagnetic-coil system, or a system of moving permanent magnets. Different actuation strategies are used in robots with magnetic actuation having a number of advantages in possible realization of microscale robots such as bioinspired microrobots, tetherless microrobots, cellular microrobots, or even normal size soft robots such as electromagnetic soft robots and medical robots. This review provides a summary of recent research in magnetically actuated bio/soft robots, discussing fabrication processes and actuation methods together with relevant applications in biomedical area and discusses future prospects of this way of actuation for possible improvements in performance of different types of bio/soft robots.

JTD Keywords: capsule endoscope, controlled propulsion, conventional gastroscopy, digital microfluidics, guided capsule, liquid-metal, magnetic drug delivery, magnetic microrobots, magnetically guided capsule endoscopy, magnetotactic bacteria, nanoscribe ip-dip, navigation system, Gallium-indium egain, Magnetic bioinspired micromanipulation, Magnetic drug delivery, Magnetic microrobots, Magnetically guided capsule endoscopy, Magnetotactic bacteria

© 2020 The Author(s) The use of oxidative stress generated by Cold Atmospheric Plasma (CAP) in oncology is being recently studied as a novel potential anti-cancer therapy. However, the beneficial effects of CAP for treating osteosarcoma have mostly been demonstrated in 2-dimensional cultures of cells, which do not mimic the complexity of the 3-dimensional (3D) bone microenvironment. In order to evaluate the effects of CAP in a relevant context of the human disease, we developed a 3D tissue-engineered model of osteosarcoma using a bone-like scaffold made of collagen type I and hydroxyapatite nanoparticles. Human osteosarcoma cells cultured within the scaffold showed a high capacity to infiltrate and proliferate and to exhibit osteomimicry in vitro. As expected, we observed significantly different functional behaviors between monolayer and 3D cultures when treated with Cold Plasma-Activated Ringer's Solution (PAR). Our data reveal that the 3D environment not only protects cells from PAR-induced lethality by scavenging and diminishing the amount of reactive oxygen and nitrogen species generated by CAP, but also favours the stemness phenotype of osteosarcoma cells. This is the first study that demonstrates the negative effect of PAR on cancer stem-like cell subpopulations in a 3D biomimetic model of cancer. These findings will allow to suitably re-focus research on plasma-based therapies in future.

JTD Keywords: 3d tumor model, cancer stem-like cells, cold atmospheric plasma, osteosarcoma, oxidative stress, plasma activated liquids, reactive oxygen and nitrogen species, 3d tumor model, Bone neoplasms, Cancer stem-like cells, Cell line, tumor, Cold atmospheric plasma, Humans, Neoplastic stem cells, Osteosarcoma, Oxidative stress, Plasma activated liquids, Plasma gases, Reactive oxygen and nitrogen species, Tumor microenvironment

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

JTD Keywords: Unmanned aircraft systems, Remotely piloted aircraft systems, Chemical sensors, Gas sensors, Environmental monitoring, Industrial emission monitoring

This paper describes the design of a linear phase low-pass differentiator filter with a finite impulse response (FIR) for extracting transient features of gas sensor signals (the so-called “bouts”). The detection of these bouts is relevant for estimating the distance of a gas source in a turbulent plume. Our current proposal addresses the shortcomings of previous ‘bout’ estimation methods, namely: (i) they were based in non-causal digital filters precluding real time operation, (ii) they used non-linear phase filters leading to waveform distortions and (iii) the smoothing action was achieved by two filters in cascade, precluding an easy tuning of filter performance. The presented method is based on a low-pass FIR differentiator, plus proper post-processing, allowing easy algorithmic implementation for real-time robotic exploration. Linear phase filters preserve signal waveform in the bandpass region for maximum reliability concerning both ‘bout’ detection and amplitude estimation. As a case study, we apply the proposed filter to predict the source distance from recordings obtained with metal oxide (MOX) gas sensors in a wind tunnel. We first perform a joint optimization of the cut-off frequency of the filter and the bout amplitude threshold, for different wind speeds, uncovering interesting relationships between these two parameters. We demonstrate that certain combinations of parameters can reduce the prediction error to 8 cm (in a distance range of 1.45 m) improving previously reported performances in the same dataset by a factor of 2.5. These results are benchmarked against traditional source distance estimators such as the mean, variance and maximum of the response. We also study how the length of the measurement window affects the performance of different signal features, and how to select the filter parameters to make the predictive models more robust to changes in wind speed. Finally, we provide a MATLAB implementation of the bout detection algorithm and all analysis code used in this study.

JTD Keywords: Gas sensors, Differentiator, Low pass filter, Metal oxide semiconductor, MOX sensors, Signal processing, Feature extraction, Gas source localization, Robotics

The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

JTD Keywords: Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping

Metal Oxide (MOX) gas sensors rely on chemical reactions that occur efficiently at high temperatures, resulting in too-demanding power requirements for certain applications. Operating the sensor under a Pulsed-Temperature Operation (PTO), by which the sensor heater is switched ON and OFF periodically, is a common practice to reduce the power consumption. However, the sensor performance is degraded as the OFF periods become larger. Other research works studied, generally, PTO schemes applying waveforms to the heater with time periods of seconds and duty cycles above 20%. Here, instead, we explore the behaviour of PTO sensors working under aggressive schemes, reaching power savings of 99% and beyond with respect to continuous heater stimulation. Using sensor sensitivity and the limit of detection, we evaluated four Ultra Low Power (ULP) sensors under different PTO schemes exposed to ammonia, ethylene, and acetaldehyde. Results show that it is possible to operate the sensors with total power consumption in the range of microwatts. Despite the aggressive power reduction, sensor sensitivity suffers only a moderate decline and the limit of detection may degrade up to a factor five. This is, however, gas-dependent and should be explored on a case-by-case basis since, for example, the same degradation has not been observed for ammonia. Finally, the run-in time, i.e., the time required to get a stable response immediately after switching on the sensor, increases when reducing the power consumption, from 10 minutes to values in the range of 10–20 hours for power consumptions smaller than 200 microwatts.

JTD Keywords: Robot sensing systems, Temperature sensors, Heating systems, Gas detectors, Power demand, Sensitivity, Electronic nose, gas sensors, low-power operation, machine olfaction, pulsed-temperature operation, temperature modulation

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.

JTD Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines

The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.

JTD Keywords: Gas detectors, Chemical sensors, Signal processing, Machine learning, Time series analysis

This paper proposes the application of a low-cost gas sensor array in an assistant personal robot (APR) in order to extend the capabilities of the mobile robot as an early gas leak detector for safety purposes. The gas sensor array is composed of 16 low-cost metal-oxide (MOX) gas sensors, which are continuously in operation. The mobile robot was modified to keep the gas sensor array always switched on, even in the case of battery recharge. The gas sensor array provides 16 individual gas measurements and one output that is a cumulative summary of all measurements, used as an overall indicator of a gas concentration change. The results of preliminary experiments were used to train a partial least squares discriminant analysis (PLS-DA) classifier with air, ethanol, and acetone as output classes. Then, the mobile robot gas leak detection capabilities were experimentally evaluated in a public facility, by forcing the evaporation of (1) ethanol, (2) acetone, and (3) ethanol and acetone at different locations. The positive results obtained in different operation conditions over the course of one month confirmed the early detection capabilities of the proposed mobile system. For example, the APR was able to detect a gas leak produced inside a closed room from the external corridor due to small leakages under the door induced by the forced ventilation system of the building.

JTD Keywords: Metal-oxide semiconductor, Gas sensor, Gas leak detection, Assistant personal robot, Mobile robot

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

JTD Keywords: Robotics, Signal processing, Electronics, Gas source localization, Gas distribution mapping, Gas sensors, Drone, UAV, MOX sensor, Quadcopter

This paper explores which signal features of a gas sensor are optimum for assessing the proximity to a gas source in an open environment. Specifically, we compare three statistical descriptors of the signal (mean, variance and maximum response) against the 'bout' frequency, a feature computed in the derivative of the response. The experimental setup includes a generator of turbulent plumes and a sensing board composed of three metal oxide (MOX) sensors of different types. The main conclusion is that the maximum response is the most robust feature across the three sensors. The 'bout' frequency can be very sensitive to an additional parameter (the noise threshold).

JTD Keywords: Feature extraction, Gas plume, Gas sensors, Gas source localization, MOX, Signal processing

The low bandwidth of metal oxide semiconductor (MOX) sensors (<0.1 Hz) is a major hurdle to gas source localization (GSL) in turbulent environments where detection of intermittent odor patches is key. We present a fast-response miniaturized electronic nose (Fast-eNose) composed of four naked MOX sensors and a digital band-pass filter that can boost the bandwidth of the system close to 1 Hz. The device was attached to a fast photo-ionization detector (330 Hz) to quantify the response time during exposure to turbulent gas plumes. The results indicate that the digital filter can improve the response time by at least a factor of 4, bringing new possibilities to mobile robot olfaction.

JTD Keywords: CFD, Gas plume, Gas sensors, MOX, Response time, Signal processing

Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models.

JTD Keywords: Gas sensor array, MOX sensor, Robust calibration, Calibration transfer, Machine olfaction

The limit of detection (LOD) is a key figure of merit in chemical sensing. However, the estimation of this figure of merit is hindered by the non-linear calibration curve characteristic of semiconductor gas sensor technologies such as, metal oxide (MOX), gasFETs or thermoelectric sensors. Additionally, chemical sensors suffer from cross-sensitivities and temporal stability problems. The application of the International Union of Pure and Applied Chemistry (IUPAC) recommendations for univariate LOD estimation in non-linear semiconductor gas sensors is not straightforward due to the strong statistical requirements of the IUPAC methodology (linearity, homoscedasticity, normality). Here, we propose a methodological approach to LOD estimation through linearized calibration models. As an example, the methodology is applied to the detection of low concentrations of carbon monoxide using MOX gas sensors in a scenario where the main source of error is the presence of uncontrolled levels of humidity.

JTD Keywords: Semiconductor gas sensors, Metal-oxide sensors, Limit of detection, Non-linear, Humidity interference, Temperature modulation

A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.

JTD Keywords: Gas sensor array, MOX sensors, Resolving Power, Sensor resolution, Dimensionality reduction, Machine olfaction

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

JTD Keywords: Fire detection, Gas sensor, Pattern recognition, Sensor fusion, Machine learning, Toxicants, Carbon monoxide, Hydrogen cyanide, Standard test fires, Transducers, Smoke

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

JTD Keywords: Bitter orange essential oil, Headspace gas chromatography–mass spectrometry, Artificial neural network, Foodomics, Chemometrics, Feature selection

Mobile applications based on gas sensing present new opportunities for low-cost air quality monitoring, safety, and healthcare. Metal oxide semiconductor (MOX) gas sensors represent the most prominent technology for integration into portable devices, such as smartphones and wearables. Traditionally, MOX sensors have been continuously powered to increase the stability of the sensing layer. However, continuous power is not feasible in many battery-operated applications due to power consumption limitations or the intended intermittent device operation. This work benchmarks two low-power, duty-cycling, and on-demand modes against the continuous power one. The duty-cycling mode periodically turns the sensors on and off and represents a trade-off between power consumption and stability. On-demand operation achieves the lowest power consumption by powering the sensors only while taking a measurement. Twelve thermally modulated SB-500-12 (FIS Inc. Jacksonville, FL, USA) sensors were exposed to low concentrations of carbon monoxide (0–9 ppm) with environmental conditions, such as ambient humidity (15–75% relative humidity) and temperature (21–27 ◦C), varying within the indicated ranges. Partial Least Squares (PLS) models were built using calibration data, and the prediction error in external validation samples was evaluated during the two weeks following calibration. We found that on-demand operation produced a deformation of the sensor conductance patterns, which led to an increase in the prediction error by almost a factor of 5 as compared to continuous operation (2.2 versus 0.45 ppm). Applying a 10% duty-cycling operation of 10-min periods reduced this prediction error to a factor of 2 (0.9 versus 0.45 ppm). The proposed duty-cycling powering scheme saved up to 90% energy as compared to the continuous operating mode. This low-power mode may be advantageous for applications that do not require continuous and periodic measurements, and which can tolerate slightly higher prediction errors.

JTD Keywords: Smartphone, Metal-oxide semiconductor, Gas sensor, Low power, Temperature-modulation, Interferences

Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

JTD Keywords: MOX, Metal oxide, Flow visualization, Gas sensors, Gas distribution mapping, Sensor grid, 3D, Gas source localization, Indoor

Embryo-scale morphogenesis arises from patterned mechanical forces. During Drosophila gastrulation, actomyosin contractility drives apical constriction in ventral cells, leading to furrow formation and mesoderm invagination. It remains unclear whether and how mechanical properties of the ectoderm influence this process. Here, we show that Neuralized (Neur), an E3 ubiquitin ligase active in the mesoderm, regulates collective apical constriction and furrow formation. Conversely, the Bearded (Brd) proteins antagonize maternal Neur and lower medial–apical contractility in the ectoderm: in Brd-mutant embryos, the ventral furrow invaginates properly but rapidly unfolds as medial MyoII levels increase in the ectoderm. Increasing contractility in the ectoderm via activated Rho similarly triggers furrow unfolding whereas decreasing contractility restores furrow invagination in Brd-mutant embryos. Thus, the inhibition of Neur by Brd in the ectoderm differentiates the mechanics of the ectoderm from that of the mesoderm and patterns the activity of MyoII along the dorsal–ventral axis.

JTD Keywords: Drosophila, Gastrulation, Morphogenesis

Conventional fire alarms are based on smoke detection. Nevertheless, in some fire scenarios volatiles are released before smoke. Fire detectors based only on chemical sensors have already been proposed as they may provide faster response, but they are still prone to false alarms in the presence of nuisances. These systems rely heavily on pattern recognition techniques to discriminate fires from nuisances. In this context, it is important to test the systems according to international standards for fires and testing the system against a diversity of nuisances. In this work, we investigate the behavior of a gas sensor array coupled to sensor fusion algorithms for fire detection when exposed to standardized fires and several nuisances. Results confirmed the ability to detect fires (97% Sensitivity), although the system still produces a significant rate of false alarms (35%) for nuisances not presented in the training set.

JTD Keywords: Fire alarm, Gas sensor array, Machine Olfaction, Multisensor system, Sensor fusion

Abstract Shifts in working temperature are an important issue that prevents the successful transfer of calibration models from one chemical instrument to another. This effect is of special relevance when working with gas sensor arrays modulated in temperature. In this paper, we study the use of multivariate techniques to transfer the calibration model from a temperature modulated gas sensor array to another when a global change of temperature occurs. To do so, we built 12 identical master sensor arrays composed of three different types of commercial Figaro sensors and acquired a dataset of sensor responses to three pure substances (ethanol, acetone and butanone) dosed at 7 concentrations. The master arrays are then shifted in temperature (from −50 to 50 °C, ΔT = 10 °C) and considered as slave arrays. Data correction is performed for an increasing number of transfer samples with 4 different calibration transfer techniques: Direct Standardization, Piece-wise Direct Standardization, Orthogonal Signal Correction and Generalized Least Squares Weighting. In order to evaluate the performance of the calibration transfer, we compare the Root Mean Square Error of Prediction (RMSEP) of master and slave arrays, for each instrument correction. Best results are obtained from Piece-wise Direct standardization, which exhibits the lower RMSEP values after correction for the smaller number of transfer samples.

JTD Keywords: Calibration transfer, Gas sensor array, MOX, Temperature modulation

Abstract Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.

JTD Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Respiration, Sniffing

Abstract The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of low-frequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique suitable in early detection scenarios. The full data set is made publicly available to the community.11 http://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation.

JTD Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Sniffing

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

JTD Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array for different distributions of sensor failures across sensor types. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: (1) 12 replicates of each sensor type for a total of 96 sensors and (2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. We perform two experiments to determine the performance degradation of the array with increasing number of damaged sensors in two different scenarios of sensor faults distributions across sensor types. In the first experiment, we characterize the diversity and redundancy of the array for increasing number of damaged sensors. To measure diversity and redundancy, we proposed a functional definition based on clustering of sensor features. The second experiment is devoted to determine the performance degradation of the array for the effect of faulty sensors. To this end, the system is trained to separate ethanol, acetone and butanone at different concentrations using a PCA–LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the performance of the array we used the Fisher score as a measure of odour separability. Our results show that to exploit to the utmost the redundancy of the sensor array faulty sensory units have to be distributed uniformly across the different sensor types.

JTD Keywords: Gas sensor arrays, Sensor redundancy, Sensor diversity, Sensor faults aging, Sensor damage, MOX sensors, Large sensor arrays

Abstract Blind Source Separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on Ion Mobility Spectrometry, being Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and Multivariate Curve Resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography - ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

JTD Keywords: Blind Source Separation, Multivariate Curve Resolution, Ion Mobility Spectrometry, Gas Chromatography, Hyphenated instrumentation, SIMPLISMA, co-elution

The use of an autonomous mobile robot to locate gas-leaks and air quality monitoring in indoor environments are promising tasks that will avoid risky human operations. However, these are challenging tasks due to the chaotic gas profile propagation originated by uncontrolled air flows. This paper proposes the localization of an acetone gas-leak in a 44 m-length indoor corridor with a mobile robot equipped with a PID sensor. This paper assesses the influence of the mobile robot velocity and the relative height of the PID sensor in the profile of the measurements. The results show weak influence of the robot velocity and strong influence of the relative height of the PID sensor. An estimate of the gas-leak location is also performed by computing the center of mass of the highest gas concentrations.

JTD Keywords: Gas source detection, LIDAR sensor, Mobile robot, PID sensor, SLAM, Acetone, Air quality, Gases, Indoor air pollution, Mobile robots, Robots, Air quality monitoring, Autonomous Mobile Robot, Gas sources, Indoor environment, Leak localization, LIDAR sensors, Profile propagation, SLAM, Ionization of gases

An array with all-solid-state, potentiometric, miniaturized sensors for pH and potassium was developed to be introduced into the stomach or other sectors of the digestive tract by means of flexible endoscopy. These sensors perform continuous and simultaneous measurement of extracellular pH and potassium. This detection seeks to sense ischemia in the gastric mucosa inside the stomach, an event indicative of local microvascular perfusion and tissue oxygenation status. Our array is proposed as a medical tool to identify the occurrence of the ischemia after gastrointestinal or gastroesophageal anastomosis. The stability and feasibility of the miniaturized working and reference electrodes integrated in the array were studied under in vitro conditions, and the behavior of the potassium and pH ion-selective membranes were optimized to work under acidic gastric conditions with high concentrations of HCl. The array was tested in vivo in pigs to measure the ischemia produced by clamping the blood flow into the stomach. Our results indicate that ischemic and reperfusion states can be sensed in vivo and that information on tissue damage can be collected by this sensor array. The device described here provides a miniaturized, inexpensive, and mass producible sensor array for detecting local ischemia caused by unfavorable anastomotic perfusion and will thus contribute to preventing anastomotic leakage and failure caused by tissue necrosis.

JTD Keywords: Endoscopy, Surgery, Tissue, Gastric anastomosis, Gastric conditions, Ion selective sensors, Ischemia, pH detection, Reference electrodes, Simultaneous measurement, Tissue oxygenation, Sensors

Abstract Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

JTD Keywords: Limit of detection, Information theory, Mutual information, Heteroscedasticity, False positive/negative errors, Gas discrimination and quantification

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

JTD Keywords: Environmental monitoring, Gas discrimination, Gas distribution mapping, Service robots, Open sampling systems, PID, Metal oxide sensors

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.

JTD Keywords: Gas chromatography, Instruments, Radioactivity measurement, Signal processing, Analytical instrument, Analytical Instrumentation, Asymmetric least squares, Baseline estimation, Baseline removal, Gas chromatography-mass spectrometries (GC-MS), Instrumental techniques, Noise levels, Iterative methods

Although Metal Oxide (MOX) sensors are predominant choices to perform fundamental tasks of chemical detection, their use has been mainly limited to relatively controlled scenarios where a gas sensor array is first exposed to a reference, then to the gas sample, and finally to the reference again to recover the initial state. In this paper we propose the use of MOX sensors along with Reservoir Computing algorithms to identify chemicals of interest. Our approach allows continuous gas monitoring in simple experimental setups without the requirement of acquiring recovery transient of the sensors, thereby making the system specifically suitable for online monitoring applications.

JTD Keywords: Chemical sensing, Reservoir computing, Gas sensors, Dynamic gas mixtures, Electronic nose

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array as the number of faulty sensors increases. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: 1) 12 replicates of each sensor type for a total of 96 sensors, and 2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. The system is trained to identify ethanol, acetone and butanone using a PCA-LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the tolerance of the array against sensor failure, the Fisher Score is used as a figure of merit for the corrupted test set samples projected on the PCA-LDA model.

JTD Keywords: Gas ensor arrays, sensor redundancy, MOX sensors, large sensor arrays.

This paper presents an autonomous agent for gas leak source detection. The main objective of the robot is to estimate the localization of the gas leak source in an indoor environment without any human intervention. The agent implements an SLAM procedure to scan and map the indoor area. The mobile robot samples gas concentrations with a gas and a wind sensor in order to estimate the source of the gas leak. The mobile robot agent will use the information obtained from the onboard sensors in order to define an efficient scanning path. This paper describes the measurement results obtained in a long corridor with a gas leak source placed close to a wall.

JTD Keywords: Gas detection, Mobile robot agent, Laser sensor, Self-localization

This manuscript introduces a software tool that allows for the design of synthetic experiments in machine olfaction. The proposed software package includes both, a virtual sensor array that reproduces the diversity and response of a polymer array and tools for data generation. The synthetic array of sensors allows for the generation of chemosensor data with a variety of characteristics: unlimited number of sensors, support of multicomponent gas mixtures and full parametric control of the noise in the system. The artificial sensor array is inspired from a reference database of seventeen polymeric sensors with concentration profiles for three analytes. The main features in the sensor data, like sensitivity, diversity, drift and sensor noise, are captured by a set of models under simplified assumptions. The generator of sensor signals can be used in applications related to test and benchmarking of signal processing methods, neuromorphic simulations in machine olfaction and educational tools. The software is implemented in R language and can be freely accessed.

JTD Keywords: Gas Sensor Array, Conducting Polymers, Electronic Nose, Sensor Simulation, Synthetic Dataset, Benchmark, Educational Tool

The sensitivity and selectivity of metal oxide (MOX) gas sensors change significantly when the sensors operate at different temperatures. While previous investigations have presented systematic approaches to optimize the operating temperature of a single MOX sensor, in this paper we present a methodology to select the optimal operating temperature of all the MOX sensors constituent of a gas sensor array based on the multivariate response of all the sensing elements. Our approach estimates a widely used Information Theory measure, the so-called Mutual Information (MI), which quantifies the amount of information that the state of one random variable (response of the gas sensor array) can provide from the state of another random variable representing the gas quality. More specifically, our methodology builds sensor models from experimental data to solve the technical problem of populating the joint probability distribution for the MI estimation. We demonstrate the relevance of our approach by maximizing the MI and selecting the best operating temperatures of a four-sensor array sampled at 94 different temperatures to optimize the discrimination task of ethanol, acetic acid, 2-butanone, and acetone. In addition to being applicable in principle to sensor arrays of any size, our approach gives precise information on the ability of the system to discriminate odors according to the temperature of the MOX sensors, for either the optimal set of temperatures or the temperatures that may render inefficient operation of the system itself.

JTD Keywords: MOX gas sensor, Temperature optimization, Limit of detection, Mutual Information, E-nose, Sensor array, Information Theory, Chemical sensing

We propose the use of multi-way methods to analyze the contribution of diversity and redundancy to odor identification and concentration estimation in a large chemical sensor array. We use a chemical sensing system based on a large array of metal oxide sensors (MOX) and inspired on the diversity and redundancy of the olfactory epithelium. In order to analyze the role of diversity (different sensor type and temperature modulation) and redundancy (replicates of sensors and different load resistors) in odor quantification and discrimination tasks, we have acquired two datasets and modeled the data using multi-way techniques.

JTD Keywords: Artificial Olfaction, Large array, MOX gas sensor, Multi-way methods

The potential use of a micromachined thermopile based sensor device for analyzing natural gas is explored. The sensor consists of a thermally isolated hotplate which is heated by the application of a sequence of programmed voltages to an integrated heater. Once the hotplate reaches a stationary temperature, the thermopile provides a signal proportional to the hotplate temperature. These signals are processed in order to determine different natural gas properties. Sensor response is mainly dependent on the thermal conductivity of the surrounding gas at different temperatures. Seven predicted properties (normal density, Superior Heating Value, Wobbe index and the concentrations of methane, ethane, carbon dioxide and nitrogen) are calibrated against sensor signals by using multivariate regression, in particular Partial Least Squares. Experimental data have been used for calibration and validation. Results show property prediction capability with reasonable accuracy except for prediction of carbon dioxide concentration. A detailed uncertainty analysis is provided to better understand the metrological limits of the system. These results imply for the first time the possibility of designing unprecedented low-cost natural gas analyzers. The concept may be extended to other constrained gas mixtures (e.g. of a known number of components) to enable low-cost multicomponent gas analyzers.

JTD Keywords: Gas sensor, Natural gas, MEMS, Superior Heating Value, density, PLS

The off-flavor of “tainted wine” is attributed mainly to the presence of 2,4,6-trichloroanisole (2,4,6-TCA) in the wine. In the present study the atmospheric pressure gas-phase ion chemistry, pertaining to ion mobility spectrometry, of 2,4,6-trichloroanisole was investigated. In positive ion mode the dominant species is a monomer ion with a lower intensity dimer species with reduced mobility values (K0) of 1.58 and 1.20 cm2 V−1 s−1, respectively. In negative mode the ion with K0 = 1.64 cm2 V−1 s−1 is ascribed to a trichlorophenoxide species while the ions with K0 = 1.48 and 1.13 cm2 V−1 s−1 are attributed to chloride attachment adducts of a TCA monomer and dimer, respectively. The limit of detection of the system for 2,4,6-TCA dissolved in dichloromethane deposited on a filter paper was 2.1 ug and 1.7 ppm in the gas phase. In ethanol and in wine the limit of detection is higher implying that pre-concentration and pre-separation are required before IMS can be used to monitor the level of TCA in wine.

JTD Keywords: 2,4,6-Trichloroanisole, Gas phase ion chemistry, Ion mobility spectrometry, "Tainted wine"

In this work, a new methodology to analyze spectra time-series obtained from ion mobility spectrometry (IMS) has been investigated. The proposed method combines the advantages of multivariate curve resolution-alternating least squares (MCR-ALS) for an optimal physical and chemical interpretation of the system (qualitative information) and a multivariate calibration technique such as polynomial partial least squares (poly-PLS) for an improved quantification (quantitative information) of new samples. Ten different concentrations of 2-butanone and ethanol were generated using a volatile generator based on permeation tubes. The different concentrations were measured with IMS. These data present a non-linear behaviour as substance concentration increases. Although MCR-ALS is based on a bilinear decomposition, non-linear behaviour can be modelled adding new components to the model. After spectral pre-processing, MCR-ALS was applied aiming to get information about the ionic species that appear in the drift tube and their evolution with the analyte concentration. By resolving the IMS data matrix, concentration profiles and pure spectra of the different ionic species have been obtained for both analytes. Finally, poly-PLS was used in order to build a calibration model using concentration profiles obtained from MCR-ALS for ethanol and 2-butanone. The results, with more than 99% of explained variance for both substances, show the feasibility of using MCR-ALS to resolve IMS datasets. Furthermore, similar or better prediction accuracy is achieved when concentration profiles from MCR-ALS are used to build a calibration model (using poly-PLS) compared to other standard univariate and multivariate calibration methodologies.

JTD Keywords: Ion Mobility Spectrometry, Multivariate Curve Resolution, Gas phase ion chemistry, Multivariate calibration

The roles of different ribonucleotide reductases (RNRs) in bacterial pathogenesis have not been studied systematically. In this work we analyzed the importance of the different Pseudomonas aeruginosa RNRs in pathogenesis using the Drosophila melanogaster host-pathogen interaction model. P. aeruginosa codes for three different RNRs with different environmental requirements. Class II and III RNR chromosomal mutants exhibited reduced virulence in this model. Translational reporter fusions of RNR gene nrdA, nrdJ, or nrdD to the green fluorescent protein were constructed to measure the expression of each class during the infection process. Analysis of the P. aeruginosa infection by flow cytometry revealed increased expression of nrdJ and nrdD and decreased nrdA expression during the infection process. Expression of each RNR class fits with the pathogenicities of the chromosomal deletion mutants. An extended understanding of the pathogenicity and physiology of P. aeruginosa will be important for the development of novel drugs against infections in cystic fibrosis patients.

JTD Keywords: Broad-host-range, Anaerobic growth, Drosophila-melanogaster, Bacterial biofilms, Escherichia-coli, Cystic-fibrosis, Model host, Virulence, Promoter, Vectors

The potential of a vanguard technique as is the Ion Mobility Spectrometry with Ultraviolet ionization (UV-IMS) coupled to a Continuous Flow System (CFS) have been demonstrated in this work by using a Gas Phase Separator (GPS). This vanguard system (CFS-GPS-UV-IMS) has been used for the analysis of different types of white wines to obtain a characteristic profile for each type of wine and their posterior classification using different chemometric tools. Precision of the method was 3.1% expressed as relative standard deviation. A deep chemometric study was carried out for the classification of the four types of wines selected. The best classification performance was obtained by first reducing the data dimensionality by Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) and finally using a K-Nearest Neighbour (kNN) classifier. The classification rate in an independent validation set were 92.0% classification rate value with confidence interval [89.0%, 95.0%] at P = 0.05 confidence level. The same white wines analyzed by using CFS-GPS-UV-IMS were analyzed by using Gas Chromatography with a Flame Detector (GC-FID) as conventional technique. The chromatographic method used for the determination of superior alcohols in wine samples shown in the Regulation CEE 1238/1992 was selected to carry out the analysis of the same samples set and later the classification using appropriate chemometric tools. In this case, strategies PCA-LDA and kNN classifier were also used for the correct classification of the wine samples. This combination showed similar results to the ones obtained with the proposed method.

JTD Keywords: Classification, White wines, Ultraviolet-Ion Mobility Spectrometry, Gas Phase Separate, Vanguard method, Continuous Flow System, Chemometric analysis.

A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a Principal Component Analysis (PCA) of a reference gas. The proposed new method - employing no specific reference gas, but information from all gases -has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7-months including three gases at different concentrations for an array of 17 polymeric sensors.

JTD Keywords: Gas sensor array, Drift, Common principal component, Analysis, Component correction, Electronic nose

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage process for sea bream (Sparus aurata) is explored. It is shown that a single sensor under temperature modulation is able to find a correlation with the fish spoilage process. Results are obtained in real frigorific storage conditions: that is, at low measurement temperatures with variations of relative humidity.

JTD Keywords: Gas sensors, Electronic nose, Spoilage process, Temperature modulation, Bream sparus-aurata, Electronic nose, Freshness, Quality, Sardines, Storage

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

JTD Keywords: Gas sensor array, Drift, Orthogonal signal correction, Component correction, Cross-validation, Electronic nose, Data shift

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p<0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

JTD Keywords: Practical, Experimental/ bioelectric phenomena, Diseases, Electrocardiography, Medical signal, Processing/ QRS loop, Vectorcardiogram, Cardiac depolarization vector, Myocardial damage, Chagas disease, Complementary diagnosis technique, High-resolution electrocardiogram

Statistical methods like Principal Components Analysis (PCA) or Partial Least Squares (PLS) and multiscale approaches, have been reported to be very useful in the task of fault diagnosis of malfunctioning sensors for several types of faults. In this work, we compare the performance of PCA and Multiscale-PCA on a fault based on a change of sensor sensitivity. This type of fault affects chemical gas sensors and it is one of the effects of the sensor poisoning. These two methods will be applied on a dataset composed by the signals of 17 conductive polymer gas sensors, measuring three analytes at several concentration levels during 10 months. Therefore, additionally to performance's comparison, both method's stability along the time will be tested. The comparison between both techniques will be made regarding three aspects; detection, identification of the faulty sensors and correction of faulty sensors response.

JTD Keywords: Fault diagnosis, Gas sensors, Principal component analysis

This paper presents a chemical sensing system that takes inspiration from the combination of sensory diversity and redundancy at the olfactory epithelium to enhance the chemical information obtained from the odorants. The system is based on commercial MOS sensors and achieves, first, diversity trough different types of MOS along with modulation of their temperatures, and second redundancy including 12 MOS sensors for each type (12×8) combined with a high-speed multiplexing system that allows connecting 16 load resistors with each and every one of the 96 sensors in about two seconds. Exposition of the system to ethanol, ammonia, and acetone at different concentrations shows how the system is able to capture a large amount of information of the identity and the concentration of the odorant.

JTD Keywords: Gas sensor array, Biologically inspired system, Redundancy, Diversity, MOX sensors, Temperature modulation

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage process for sea bream (Sparus Aurata) is explored. It is shown that a single sensor under temperature modulation is able to find a correlation with the fish spoilage process

JTD Keywords: Gas sensors, Electrochemical sensors, Chromatography

Data from a Gas Sensor based Chromatography instrument is used in order to replicate output from a human panel and the estimation of the total solvent amount measured by and FID device in a packaging application. The system is trained on different packaging sample properties and validated with unseen combinations of materials, varnishes and production processes. This contribution will show the difficulties on the prediction of the output of the human panel, and the success on the prediction of the total amount of solvent in the sample

JTD Keywords: Gas sensors, Solvent prediction

Natural gas may show significant changes in its chemical composition depending on its origin. Typically, natural gas analysis is carried out using process gas chromatography. However, other methods based on the evaluation of physical properties have recently been reported. Thermal conductivity sensors are currently used in the analysis of binary mixtures of dissimilar gases. In contrast, natural gas is a complex mixture of mainly hydrocarbons, plus other residual gases as carbon dioxide and nitrogen. In this work, the response of a micromachined sensor integrating a heater and a thermopile is studied, regarding its potential use for natural gas analysis. A finite element thermal model of the device is described, and thermal operation simulations as well as a preliminary sensitivity analysis are reported. Experimental data has been collected and compared with simulated data, showing very good agreement. Results show that small variations in the gas mixture composition can be clearly detected. The sensor appears as a good candidate to be included in low-cost natural gas property analysis and quality control systems.

JTD Keywords: Natural gas, Thermopile, MEMS, Thermal conductivity, Modeling, FEM simulation

T Multivariate data analysis techniques have been used for the first time in thermoelectric MEMS sensors in order to determine the composition of natural gas mixtures. Experimental measurements with different thermoelectric devices have been performed, the gathered data have been used to calibrate the sensor responses to four main components of natural gas: CH4, C2H6, N2 and CO2. Presence of the three first components was predicted with good accuracy while CO2 prediction was poor. Presented results indicate that thremoelectric sensors operated at different heater temperatures open the possibility of low-cost natural gas analysis.

JTD Keywords: Natural gas, Multivariate calibration, Thermal conductivity, Thermal sensor