Publications

Arborescent (dendrigraft) polymers are high-molecular-weight dendritic macromolecules with a regular, multilevel branched topology and a high density of functional end groups in their periphery. Their well-defined architecture, devoid of cross-links or loops, imparts a particle-macromolecule duality that becomes particularly pronounced at interfaces. However, the underlying mechanisms governing their interfacial behavior remain largely unexplored. Here, we elucidate how the unique topology dictates the interfacial organization of water-soluble arborescent polymers. Using an iterative grafting-from approach via single-electron transfer living radical polymerization, we synthesized narrowly dispersed polymers with controlled branching and ultra-high molecular weight of 6.2 x 106 g mol-1. These polymers transition from spherical rigid particles in solution, to highly flexible, two-dimensional conformations upon interfacial adsorption. At solid interfaces, increasing segment density shifts surface morphologies from quasi-2D discs to fried-egg-like structures, as observed by atomic force microscopy and corroborated by dissipative particle dynamics simulations. At liquid-liquid interfaces, the absence of substrate constraints facilitates complete spreading into uniform 2D discs, driven by the energy gain due to polymer-segment adsorption. Furthermore, we uncover that macromolecular crowding and topological constraints inherent to the arborescent architecture dictate the response to compression of the adsorbed polymer layer, contrasting sharply with the behavior of conventional flexible linear or star polymers. The combination of high interfacial activity, spatially adaptable end groups, and extreme molecular flexibility will enable arborescent polymers to adapt to complex interfaces, acting as versatile platforms for multivalent and superselective interactions. These properties open new avenues for designing multivalent nanocarriers and adaptive interfacial materials with cooperative binding effects.

JTD Keywords: Angle neutron-scattering, Architectur, Graft, Microgels, Polymers, Polystyrene-graft-poly(2-vinylpyridine) copolymers, Polystyrenes, Radical polymerization, Set, Unimolecular micelles

Electrically polarized amorphous silica (aSiO2) is demonstrated to be an efficient and viable metal-free heterogeneous catalyst for the conversion of CO2 into valuable chemical products. The catalyst was prepared applying a thermoelectric polarization process in air to commercially available aSiO2 nanoparticles. Four polarization temperatures were assayed (150, 500, 800 and 1000 degrees C), the larger structural and chemical changes induced by the polarization treatment being observed at 150 and 500 degrees C. The polarization at such temperatures reduced considerably the electrical resistance of calcined aSiO2, while no significant change was detected at 800 and 1000 degrees C. Polarized aSiO2 was tested as heterogeneous catalysts for the reaction of CO2 with water at mild reaction conditions (120 degrees C, 6 bar of CO2, 40 mL of water, 72 h). The highest catalytic activity was observed with aSiO2 polarized at 150 degrees C, which was attributed to the structural defects induced during the thermoelectric polarization treatment. Thus, CO2 was converted into a mixture of formic acid (39.9%), acetic acid (44.4%) and dioxane (15.7%). Although the catalytic process was not selective, the yields were not only very high but also allowed obtaining a significant amount of dioxane, a product with four carbon atoms, which is very unusual in processes catalyzed by polarized ceramics. In summary, polarized aSiO2 can be used as a sustainable and low-cost raw material to prepare metal-free catalysts by means of a thermoelectric polarization process at 150 degrees C. This catalyst is capable of capturing CO2 to produce valuable chemical products by applying mild reaction conditions.

JTD Keywords: Alloy, Atom, Basis-sets, Bone, Catalysts, Hydroxyapatite, Nanocomposites, Nanoparticles, Oxidation, Performance

JTD Keywords: alpha-tricalcium phosphate, cement, crystallinity, hydrolysis, hydroxyapatite, particle-size, powders, relative humidity, setting reaction, stability, tcp, vapor sorption, Calcium-phosphate, Moisture

We report a green solvent-to-polymer upgrading transformation of chemicals of the lactic acid portfolio into water-soluble lower critical solution temperature (LCST)-type acrylic polymers. Aqueous Cu(0)-mediated living radical polymerization (SET-LRP) was utilized for the rapid synthesis of N-substituted lactamide-type homo and random acrylic copolymers under mild conditions. A particularly unique aspect of this work is that the water-soluble monomers and the SET-LRP initiator used to produce the corresponding polymers were synthesized from biorenewable and non-toxic solvents, namely natural ethyl lactate and BASF's Agnique (R) AMD 3L (N,N-dimethyl lactamide, DML). The pre-disproportionation of Cu(I) Br in the presence of tris[2-(dimethylamino)ethyl]amine (Me6TREN) in water generated nascent Cu(0) and Cu(II) complexes that facilitated the fast polymerization of N-tetrahydrofurfuryl lactamide and N,N-dimethyl lactamide acrylate monomers (THFLA and DMLA, respectively) up to near-quantitative conversion with excellent control over molecular weight (5000 < M-n < 83 000) and dispersity (1.05 < D < 1.16). Interestingly, poly(THFLA) showed a degree of polymerization and concentration dependent LCST behavior, which can be fine-tuned (T-cp = 12-62 degrees C) through random copolymerization with the more hydrophilic DMLA monomer. Finally, covalent cross-linking of these polymers resulted in a new family of thermo-responsive hydrogels with excellent biocompatibility and tunable swelling and LCST transition. These illustrate the versatility of these neoteric green polymers in the preparation of smart and biocompatible soft materials.

JTD Keywords: Acid, Ethyl lactate, Living radical polymerization, Monomers, Pnipam, Reductive amination, Ruthenium nanoparticles, Set-lrp, Single, Thermoresponsive polymers

Biobased epoxy-derived raw materials will be essential for future coating and adhesive designs in industry. Here, a facile approach is reported towards the incorporation of limonene into an epoxy-functionalized polycarbonate and its crosslinking with a polyamine curing agent to obtain a thermoset material. For the first time, a solvent-borne adhesive with excellent film-forming, mechanical and adhesion strength properties is described.

JTD Keywords: adhesives, biobased epoxies, limonene, polycarbonate, Adhesives, Biobased epoxies, Biobased epoxy, Carbon-dioxide, Curing agents, Design in industries, Epoxides, Epoxy, Epoxy resins, Film adhesion, Film-forming, Functionalized, Limonene, Mechanical, Monomer, Monoterpenes, Oil, Oxide, Performance, Polyamines, Polycarbonate, Polycarbonates, Terpenes, Thermoset materials, Thermosets

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity. © 2022 American Physical Society.

JTD Keywords: brain, entropy, epileptogenic networks, functional connectivity, hilbert transform, seizure onset, surrogate data, synchronization, time-series, Biomedical signal processing, Brain areas, Brain dynamics, Dynamics, Electroencephalographic signals, Electroencephalography, Electrophysiology, Intracranial eeg signals, Localisation, Neurological disorders, Neurology, Phase based, Phase coherence, Signal detection, Simple++, Univariate, Velocity, World population

In the area of coating development, it is extremely difficult to find a substitute for bisphenol A diglycidyl ether (DGEBA), the classical petroleum-based raw material used for the formulation of epoxy thermosets. This epoxy resin offers fast curing reaction with several hardeners and the best thermal and chemical resistance properties for applications in coatings and adhesive technologies. In this work, a new biobased epoxy, derived from poly(limonene carbonate) oxide (PLCO), was combined with polyetheramine and polyamineamide curing agents, offering a spectrum of thermal and mechanical properties, superior to DGEBA-based thermosets. The best formulation was found to be a combination of PLCO and a commercial curing agent (Jeffamine) in a stoichiometric 1:1 ratio. Although PLCO is a solid due to its high molecular weight, it was possible to create a two-component partially biobased epoxy paint without the need of volatile organic compounds (i.e., solvent-free formulation), intended for use in coating technology to partially replace DGEBA-based thermosets.

JTD Keywords: acid, adhesion, epoxy thermoset, mechanical properties, monomer, polycarbonates, polymers, protection, resins, solvent-free paint, thermal properties, Adhesives, Biobased epoxy, Bisphenol-a-diglycidyl ethers, Carbonation, Coating development, Coating technologies, Curing, Curing agents, Epoxy coatings, Epoxy resins, Epoxy thermoset, Epoxy thermosets, Limonene oxide, Mechanical properties, Monoterpenes, Paint, Poly(limonene carbonate) oxide, Solvent free, Solvent-free paint, Thermal properties, Thermosets, Volatile organic compounds

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

Plaques of the amyloid beta (A beta) peptide are a pathological hallmark of Alzheimer's disease (AD), the most common form of dementia. Mutations in A beta also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of A beta. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in A beta, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in A beta.

JTD Keywords: aggregation, kinetics, oligomers, onset, rates, state, Aggregation, Alzheimer disease, Alzheimer's, Amyloid, Amyloid beta-peptides, Computational biology, Deep mutagenesis, Dna mutational analysis, Genetics, Genomics, High-throughput nucleotide sequencing, Kinetics, Mutation, Nucleation, Oligomers, Onset, Plasmids, Precursor protein, Rates, S. cerevisiae, Saccharomyces cerevisiae, State, Systems biology

Parasite proteins exported to the surface of Plasmodium falciparum-parasitized red blood cells (pRBCs) have a major role in severe malaria clinical manifestation, where pRBC cytoadhesion and rosetting processes have been strongly linked with microvascular sequestration while avoiding both spleen filtration and immune surveillance. The parasite-derived and pRBC surface-exposed PfEMP1 protein has been identified as one of the responsible elements for rosetting and, therefore, considered as a promising vaccine candidate for the generation of rosette-disrupting antibodies against severe malaria. However, the potential role of anti-rosetting antibodies as targeting molecules for the functionalization of antimalarial drug-loaded nanovectors has never been studied. Our manuscript presents a proof-of-concept study where the activity of an immunoliposomal vehicle with a dual performance capable of specifically recognizing and disrupting rosettes while simultaneously eliminating those pRBCs forming them has been assayed in vitro. A polyclonal antibody against the NTS-DBL1α N-terminal domain of a rosetting PfEMP1 variant has been selected as targeting molecule and lumefantrine as the antimalarial payload. After 30 min incubation with 2 μM encapsulated drug, a 70% growth inhibition for all parasitic forms in culture (IC50: 414 nM) and a reduction in ca. 60% of those pRBCs with a rosetting phenotype (IC50: 747 nM) were achieved. This immunoliposomal approach represents an innovative combination therapy for the improvement of severe malaria therapeutics having a broader spectrum of activity than either anti-rosetting antibodies or free drugs on their own.

JTD Keywords: Combination therapy, Immunoliposomes, Malaria, Nanomedicine, Rosetting, Targeted drug delivery

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on metal oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of ethanol, ethylene, carbon monoxide, or methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available.

JTD Keywords: Calibration transfer, Chemical sensors, Direct Standardization, Electronic nose, MOX sensors, Public dataset

The aim of the present work was to analyze the influence of the setting reaction on the injectability of tricalcium phosphate (TCP) pastes. Even if the injection was performed early after mixing powder and liquid, powder reactivity was shown to play a significant role in the injectability of TCP pastes. Significant differences were observed between the injection behavior of non-hardening β-TCP pastes and that of self-hardening α-TCP pastes. The differences were more marked at low liquid-to-powder ratios, using fine powders and injecting through thin needles. α-TCP was, in general, less injectable than β-TCP and required higher injection loads. Moreover, clogging was identified as a mechanism hindering or even preventing injectability, different and clearly distinguishable from the filter-pressing phenomenon. α-TCP pastes presented transient clogging episodes, which were not observed in β-TCP pastes with equivalent particle size distribution. Different parameters affecting powder reactivity were also shown to affect paste injectability. Thus, whereas powder calcination resulted in an increased injectability due to lower particle reactivity, the addition of setting accelerants, such as hydroxyapatite nanoparticles, tended to reduce the injectability of the TCP pastes, especially if adjoined simultaneously with a Na2HPO4 solution. Although, as a general trend, faster-setting pastes were less injectable, some exceptions to this rule were found. For example, whereas in the absence of setting accelerants fine TCP powders were more injectable than the coarse ones, in spite of their shorter setting times, this trend was inverted when setting accelerants were added, and coarse powders were more injectable than the fine ones.

JTD Keywords: Calcium phosphate cement, Hydroxyapatite, Injectability, Setting reaction, Tricalcium phosphate

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

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1_st and 2_nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO₂ and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO₂, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

JTD Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

JTD Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern

The Multiple Sleep Latency Test (MSLT) is a standard test to objectively evaluate patients with excessive daytime sleepiness. Sleep onset latencies are determined by visual analysis, which is costly and time-consuming. The aim of this study was to implement and test a single automatic algorithm to detect the sleep onset in the MSLT on the basis of electroencephalographic (EEG) signals. The designed algorithm computed the relative EEG spectral powers in the occipital area and detected the sleep onset corresponding to the intersection point between the lower and alpha frequencies. The algorithm performance was evaluated by comparing the sleep latencies computed automatically by the algorithm and by a sleep specialist using MSLT recordings from a total of 19 patients (95 naps). The mean difference in sleep latency between the two methods was 0.025 min and the limits of agreement were ± 2.46 min (Bland-Altman analysis). Moreover, the intra-class correlation coefficient showed a considerable inter-rater reliability (0.90). The algorithm accurately detected the sleep onset in the MSLT. The devised algorithm can be a useful tool to support and speed up the sleep specialist’s work in routine clinical MSLT assessment.

JTD Keywords: Automatic Algorithm, Drowsiness, Electroencephalography, Multiple Sleep Latency Test, Polysomnography, Sleep onset