Publications

Complementary split ring resonators (CSRRs) have been extensively studied as planar sensors in the last two decades. However, their practical use remains limited to controlled environments and classification problems. Their performance relies on high-end vector network analyzers (VNAs), highly repeatable laboratory conditions, and special sample holders or microfluidic circuits hinders its regular use in chemistry laboratories as an analytical tool. Temperature drifts and humidity variations during measuring, uncertainties in the electromagnetic properties of the sample containers, and careless sample handling introduce significant uncertainties in measurements, leading to unreliable results. Therefore, the prediction of target compounds concentration in samples has been out of the research focus up to now. Machine learning (ML) algorithms can help to mitigate these uncertainties and open the applicability of CSRR sensors to quantification problems, where it is necessary to determine the amount of a substance in a liquid (or solid) sample. This work presents a novel approach that tackles this issue, combining a CSRR sensor with well-stabilized ML algorithms that enhance its quantification performance. For illustration purposes, a low-cost, benchtop CSRR-based system is proposed to predict ethanol concentration in water solutions. Ethanol samples from 10% to 96% concentration were prepared in commercial vials, generating 450 randomized measurements. Principal component analysis (PCA) was employed for data exploration, while a partial least squares (PLS) regression model, tuned with leave-one-group-out cross validation (LOGO-CV), was trained for ethanol concentration prediction. No feature extraction technique or noise reduction strategy was applied. Although this straightforward workflow is well known in the chemical sensing field, it has not been applied to data acquired with CSRR sensors. The trained model achieved a root mean square error in prediction (RMSEP) of 3.7% . Compared with 23.4% RMSEP when using univariate calibration at optimized frequencies, it presents a prediction performance reduced by a factor of 6. No evidence of underfitting or overfitting was observed during the test of the trained model. The low RMSEP achieved by the presented setup demonstrates the potential of CSRR-based sensors when combined with ML techniques for concentration prediction working in realistic, uncontrolled conditions. This pushes forward the applicability of CSRR sensors in the chemical analysis field, which might lead to benchtop, low-cost, and reliable analysis devices for many laboratories.

JTD Keywords: Chemical analysis, Chemical sensors, Complementary, Complementary split ring resonator (csrr) sensors, Concentration prediction, Design, Electronic mail, Ethanol, Feature extraction, Filters, Machine learning (ml), Metamaterials, Principal component analysis, Resonators, Rf sensors, Sensor phenomena and characterization, Sensors, Split-ring resonators, Temperature measurement, Transmission, Transmission line measurements, Uncertainty, Variable selection

Supramolecular chemistry is the quintessential backbone of all biological processes. It encompasses a wide range from the metabolic network to the self-assembled cytoskeletal network. Combining the chemical diversity with the plethora of functional depth that biological systems possess is a daunting task for synthetic chemists to emulate. The only route for approaching such a challenge lies in understanding the complex and dynamic systems through advanced analytical techniques. The supramolecular complexity that can be successfully generated and analyzed is directly dependent on the analytical treatment of the system parameters. In this review, we illustrate advanced analytical techniques that have been used to investigate various supramolecular systems including complex mixtures, dynamic self-assembly, and functional nanomaterials. The underlying theme of such an overview is not only the exceeding detail with which traditional experiments can be probed but also the fact that complex experiments can now be attempted owing to the analytical techniques that can resolve an ensemble in astounding detail. Furthermore, the review critically analyzes the current state of the art analytical techniques and suggests the direction of future development. Finally, we envision that integrating multiple analytical methods into a common platform will open completely new possibilities for developing functional chemical systems.

JTD Keywords: analytical techniques, dynamic self-assembly, high-speed afm, liquid cell tem, Analytical technique, Analytical techniques, Biological process, Chemical analysis, Chemical diversity, Complex networks, Cytoskeletal network, Dynamic self-assembly, High-speed afm, Hydrogels, In-situ, Liquid cell tem, Metabolic network, Microscopy, Nanoscale, Proteins, Self assembly, Supramolecular chemistry, Supramolecular systems, System chemistry, Systems chemistry

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