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

Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.Copyright © 2022. Published by Elsevier B.V.

JTD Keywords: calibration, chemical sensors, drone, dynamic olfactometry, electronic nose, odourquantification, olfaction, volatile organic-compounds, wwtp, Calibration, Chemical sensors, Drone, Dynamic olfactometry, Electronic nose, Environmental monitoring, Odour quantification, Olfaction, Variable selection methods, Wwtp

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