Multicomponent Sensors Research Articles

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

An intracellular phosphorus-starvation signal activates the PhoB/PhoR two-component system in Salmonella enterica.

Bacteria acquire P primarily as inorganic orthophosphate (Pi, PO43-). Once internalized, Pi is rapidly assimilated into biomass during the synthesis of ATP. Because Pi is essential, but excessive ATP is toxic, the acquisition of environmental Pi is tightly regulated. In the bacterium Salmonella enterica (Salmonella), growth in Pi-limiting environments activates the membrane sensor histidine kinase PhoR, leading to the phosphorylation of its cognate transcriptional regulator PhoB and subsequent transcription of genes involved in adaptations to low Pi. Pi limitation promotes PhoR kinase activity by altering the conformation of a membrane signaling complex comprised of PhoR, the multicomponent Pi transporter system PstSACB and the regulatory protein PhoU. However, the identity of the Pi-starvation signal and how it controls PhoR activity remain unknown. Here, we identify conditions where the PhoB and PhoR signal transduction proteins can be maintained in an inactive state when Salmonella is grown in media lacking Pi. Our results demonstrate that PhoB/PhoR is activated by an intracellular P-insufficiency signal.IMPORTANCEIn enteric bacteria, the transcriptional response to phosphorus (P) starvation is controlled by a specialized signal transduction system comprised of a membrane-bound, multicomponent signal sensor, and a cytoplasmic transcriptional factor. Whereas this system has been primarily studied in the context of phosphate (Pi) starvation, it is currently unknown how this stress initiates signal transduction. In the current study, we establish that this signaling system is regulated by a cytoplasmic signal arising from insufficient P. We demonstrate that rather than responding to extracellular conditions, cells couple the activation of their P starvation response to the availability of cytoplasmic P. This regulatory logic may enable cells to prevent toxicity resulting from excessive Pi acquisition and hinder the onset of a P starvation response when their metabolic demands are being met through the consumption of P sources other than Pi.

Device for Measuring Forces and Torques in Flexible Connection Joints for Parabolic Trough Collector

Measurement of forces and torques in flexible connection joints for parabolic trough collectors (PTC) during their life time is important to prevent damage or breakage of these elements. Plataforma Solar de Almería (PSA) has developed and patented a device for measuring these forces and torques in flexible connection joints installed in solar power plants. The device consists of three dynamometers and one torque transducer coupled to a rotation assembly that can be externally coupled to a ball joint installed in interconnections of parabolic-troughs, with no interference in the hydraulic circuit. To calibrate the device and perform initial performance checks at lab scale, a test bench was prepared for applying forces and torques on a set-up composed of a multicomponent sensor connected in series to a standard rotary ball joint and measuring simultaneously the forces and torques by mean of the designed device. Calibration tests have been performed applying a range of forces and torques to the multicomponent sensor-ball joint at different rotation positions. Multicomponent sensor and developed device measurements have been compared to obtain a calibration matrix for the last one. Results show good performance of the device once calibrated, but that will be improved measuring angular deviations from reference axes position of each of the dynamometers that compose the device during the operation, to increase the accuracy of the measurements.

Multicomponent gas sensors based on photoacoustic spectroscopy technology: A review

AbstractPhotoacoustic spectroscopy (PAS), with the advantages of high sensitivity, extensive dynamic range, high selectivity, and nondestructive, has become one of the research hotspots in multigas detection. This review aims to discuss the latest advancements in PAS for multicomponent gas detection. The key elements of the PAS technique available to support multigas detection, namely as the optical source, and the recent developments in detection methods are presented. The different optical source‐based experimental systems such as broadband light sources and various lasers are reported, and related multigas modulation methods and the performances are analyzed. Finally, in terms of the characteristics of selectivity, sensitivity, and miniaturization, we discuss the advantages and limitations of these different multicomponent trace gas PAS techniques towards practical application.

Visual Recognition of Volatile Organic Compounds by Photonic Nose Integrated with Multiple Metal-Organic Frameworks.

The photonic nose inspired by the olfactory system is an integrated detection platform constructed by multiple sensing units as channels. However, in the detection of volatile organic compounds (VOCs), the sensing results that cannot be directly readable and the poor ability to distinguish analytes with similar chemical properties are the main challenges faced by this sensor. Here, 8 metal-organic frameworks (MOF)-based photonic crystals are used as the basic sensing units to construct a photonic nose detection platform. The microscopic adsorption of VOCs by MOFs enables the photonic crystals (PCs) to produce macroscopic structural color output, and further makes the photonic nose have specific color fingerprints for different VOCs, the response time of all PCs to VOCs can be within 1s. Through the color fingerprint, the visual identification of VOCs produced by 5 common solvent vapors is realized, and 9 VOCs with similar chemical properties are further distinguished. In addition, the application potential of the photonic nose in the actual environment is verified by identifying different contents of benzene in the paint. It is envisaged that the MOF-based photonic nose has great reference value for the development of intelligent and multi-component synergistic functional gas sensors.

Embedding ionic hydrogel in 3D printed human-centric devices for mechanical sensing

Flexible sensor applications have increasingly focused on ionically conductive hydrogels due to their notable deformability and easily tunable properties compared to rigid materials. These hydrogels possess electrical properties, thanks to their high water content and porous structure that facilitate effective ion transfer. Despite their attractive features, hydrogels have limitations in terms of water retention and shape fidelity, and they are more typically inspected as two dimensional films and patches. In this paper, 3D printed thermoplastic polyurethane (TPU) elastomer frames with various geometries were injected with ionic conductive polyacrylamide (PAAm) based hydrogels to create durable, robust soft mechanical sensors for detecting strain, pressure, and bending through changes in their electrical resistance. After the effectiveness of the TPU encasement in maintaining the hydrogel water content was demonstrated, hydrogel embedded frames with varying geometries were designed. Their response to mechanical loading was investigated in relation to their dimensions and geometric shape. Finally, glove-shaped frames were fabricated to fit human fingers and injected with ionic hydrogel for sensing abilities. The wearable sensors accommodated free movement of the fingers in multiple directions and were able to detect simultaneous and independent bending and stretching of the fingers. Through comprehensive observation of the electrical behavior of all soft ionic sensors in response to different kinds of mechanical stimuli, it was concluded that the resistance change following mechanical loading was dependent on the specific geometric features of each individual hybrid sensor. Thus, ionic hydrogel-embedded TPU encasement could be designed with targeted geometry to dictate the type and direction of mechanical sensing with regard to its application. This work presents a facile approach to fabricating multi-component soft geometric sensors with potential to be used for wearable electronics and human–machine interactions.

Technique for assessing the stress state of a rock mass by a multicomponent displacement sensor using the overcoring method

Relevance. Currently, the mining industry is at the beginning of a new stage of development, which corresponds to the extraction of minerals at great depths in difficult conditions. Based on this, an uncertainty is formed in the assessment of the parameters of the stress state of the rock mass in which mining operations are carried out, which contributes to the growth of emergency situations in underground conditions. The method of overcoring is able to provide quantitative and qualitative information about the stress state. The purpose of the research – substantiation of the methodology for assessing the stress state of the rock mass by the method of overcoring, which includes two main stages: 1 – field tests; 2 – processing of field test data. Research methodology. The method of overcoring is based on measuring the displacements of the points of the walls of the measuring well with a multicomponent displacement sensor as a result of creating an annular gap (formation of complete unloading). Data processing is carried out by two main approaches: 1 – numerical modelling; 2 – analytical calculation. Results. The concept of carrying out work on the assessment of the stress state of the rock mass by the method of overcoring is described. Detailed information about the field test is provided, namely the sequence of measurements and the necessary equipment. A step-by-step algorithm for processing data obtained as a result of field tests based on numerical modelling and analytical analysis has been created. Conclusion. The technique presented in this paper was tested at the deposits of AO Apatit and PAO Norilsk Nickel and showed correct quantitative and qualitative results, according to the stress state of the rock mass.

Exploring the limits of MEG spatial resolution with multipolar expansions

The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices (SQUIDs, placed 2–4 cm above scalp), scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors (allowing comparison of low- vs. high-density MEG), sensor-to-brain distance (cryogenic SQUIDs vs. scalp OPM), sensor type (magnetometers vs. gradiometers; single- vs. multi-component sensors), and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects.

Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented.

Detection of inception of preload loss and remaining life prediction for ball screw considering change in dynamics due to worktable position

The degradation of the ball screw drive reduces preload effect and eventually accounts for precision loss and backlash. Traditional methods detect the inception of preload loss considering features from the overall vibration sensor or controller signal. The sensor signal obtained from a ball screw assembly is nonlinear and non-stationary as its dynamics vary in a narrow bandwidth with feed rate, feed screw movement or worktable position, preload level, etc. The traditional methods don't consider this variation and hence detect the inception of preload loss in the later stage. This study used, for the first time, the variational mode decomposition (VMD) technique to analyze time-varying multicomponent sensor signals in a narrow bandwidth for timely detection of an anomaly in a ball screw drive. The frequency bands corresponding to the dynamics of the ball screw itself are only further considered in the next steps of the feature extraction and anomaly detection process. This process excludes the signal obtained from other sources and considers changes in dynamics due to worktable position. Hence, preload loss can be detected with a better signal-to-noise ratio. The best features from the selected frequency bands are selected using the Fisher score criterion and fused using principal component analysis (PCA). Hotelling's T-squared statistic was applied to principal components to find the similarity between the signal obtained in the healthy and degraded stage. Z hypothesis test has been applied to the T2 metric to estimate the inception of preload loss. Later, Weibull distribution is used for remaining useful life (RUL) prediction for the given value of threshold preload level. This systematic investigation has been done by analyzing internal motor controller data (i.e., torque signal) and external vibration sensor signals obtained from a naturally occurring and progressing defects experiment performed for a ball screw drive.

Frequency-Division-Multiplexed Multicomponent Gas Sensing with Photothermal Spectroscopy and a Single NIR/MIR Fiber-Optic Gas Cell.

We report a multicomponent gas sensor based on hollow-core fiber (HCF) photothermal spectroscopy with frequency-division multiplexing (FDM). A single antiresonant HCF (AR-HCF) is used as the gas cell, which supports broadband transmission from near-infrared (NIR) to mid-infrared (MIR), covering the absorption lines of water vapor (H2O) at 1.39 μm, carbon dioxide (CO2) at 2.00 μm, and carbon monoxide (CO) at 4.60 μm. The NIR and MIR pump lasers at the above wavelengths are coupled into the AR-HCF from the opposite ends and modulated at 7.5, 8.0, and 8.5 kHz, respectively, to produce photothermal phase modulations at different frequencies. A common probe Fabry-Perot interferometer at 1.55 μm is adopted to detect the phase modulations, which are demodulated simultaneously using three lock-in amplifiers at the respective second harmonic frequencies. With a 13-cm-long AR-HCF, simultaneous detections of H2O, CO2, and CO are demonstrated with the limits of detection (LODs) of 2.7 ppm, 25 ppb, and 9 ppb for 1 s lock-in time constant, respectively. The LODs go down to 222, 1.5, and 0.6 ppb, respectively, for 1000 s averaging time. The photothermal signals of CO and CO2, which are humidity-level dependent, are well calibrated by use of the measured H2O signal. The multicomponent gas sensor is compact in configuration and shows good stability with signal fluctuation less than 1.7% over 2 h.

A new DAS sensor prototype for multicomponent seismic data

We present a novel type of multicomponent sensor prototype that uses distributed acoustic sensing (DAS) technology. A prototype of the new sensor has three individual parts consisting of optical fiber wound around a polyvinyl chloride frame. We deployed the sensor prototype at a test site and recorded seismic waves generated by a wooden hammer. A geophone array was also set for comparison purposes. The data observed with the new DAS sensor prototype show good agreement with the conventional geophone. The particle motion of waveforms obtained by the DAS sensor prototype shows ellipsoidal motion, the propagating velocity of which coincides with the velocity of the Rayleigh waves estimated by the dispersion curve based on the geophone data. We introduced a simple string model to explain the dynamic behavior of the sensor. The analysis results of the string model can explain features of recorded seismic data. These results indicate that the DAS sensor prototype records seismic waves via lateral vibration of the string. The present study shows a new possibility of the multicomponent sensor with the DAS technology.

Multiplex detection of antidepressants with a single component condition-based colorimetric sensor array

Pattern-based sensing with multi-component sensor arrays, despite its merits, may be laborious and time-consuming. As an alternative approach, herein, a condition-based single component sensor array has been provided which represents an elegantly simple, low cost and minimally instrumented format for the quantification and classification of antidepressants (ADs). Tuning the pH and ionic strength enabled the single component probe to interact with the target analytes through different binding modes, providing the required cross-reactivity for multiplex detection. The analytical figures of merit verified that the condition-based sensor array is precise and accurate in both the discrimination and quantification of the ADs. Excellent sensitivity and selectivity were achieved in the discrimination of the ADs. Moreover, low limit of detections (as low as 0.009 μg.mL−1) and wide linear ranges (up to four orders of magnitude) were attained in the multivariate calibration of each AD. The results of multivariate calibration (R2cal>0.99 and R2cv>0.99) and classification (sensitivity 100% and specificity 100%) of ADs in human urine ensured the practicability of the array in complex biological fluids. Furthermore, the wide-ranging colorimetric responses that appeared due to the different environment sensitive aggregation patterns allowed visual detection.

Multiobjective Optimization of Structure and Robustness of a Split Parallel Multicomponent Strain Sensor

A novel split parallel multicomponent strain sensor structure layout is proposed based on the special requirements of the helicopter rotor airfoil wind tunnel test for measuring aerodynamic sensors. The sensor consists of two splits with the same configuration; the performance of the sensor not only depends on the split structure of the sensor but also depends on the assembly relationship between the splits. Three steps have been performed so as to enhance the technical performance of the sensor. First, the RBF neural network approximate model and the second-generation nondominated sorting genetic algorithm are used to optimize the split of sensor deterministically; secondly, the rotor airfoil wind tunnel test model and the sensor finite element system model are established, and the 6 σ robustness analysis is carried out; finally, the 6 σ robust multiobjective optimization has been carried out considering the sensor split processing errors and the assembly errors. The results show that, compared to initial designed sensors, the sensitivity of the three components of the sensor is increased by 285.46%, 284.95%, and 151.5%, respectively, and the maximum equivalent stress is reduced by 28.4%; the interference to the three components is reduced by 97.82%, 92.83%, and 99.8%, respectively, and the quality is reduced by 25.74%. Meanwhile, the quality level of the sensor was promoted, and the sensitivity of the response to assembly and manufacturing errors were reduced. These results exhibit that the structural layout, optimized path, and method in this strain sensor are suitable for the needs of helicopter rotor airfoil wind tunnel test.

Multicomponent trace gas detection with hollow-core fiber photothermal interferometry and time-division multiplexing

We report a multicomponent photothermal gas sensor with a conjoined-tube hollow-core optical fiber gas cell. With a common Fabry-Perot probe interferometer and a common gas cell, simultaneous detection of methane, acetylene and ammonia can be achieved by time-division multiplexing. Experiments with a 15-cm-long hollow-core fiber demonstrated noise-equivalent concentration of 24.2 parts-per-billion (ppb) for methane, 11.6 ppb for acetylene, and 46.1 ppb for ammonia. The dynamic range is measured to be around 5 orders of magnitude. The crosstalk issue is addressed by spectrum fitting. Assisted with an air pump and a compact gas chamber, the response time of less than 10 s is achieved.

Rotating the C-N Bond in a Coumarin-Pyridine-Based Sensor for Pattern Recognition of Versatile Metal Ions.

A cross-reactive sensor array is powerful for high-throughput discrimination of various kinds of metal ions. However, the construction of a multicomponent sensor array is always time-consuming and cost-ineffective. Herein, a practical four-component X1-based sensor array (X1SA) was obtained by simply dissolving a single dye molecule X1 in respective solvents such as methanol, ethanol, dimethyl sulfoxide, and acetonitrile. In this design, X1 exhibits strong solvatochromic fluorescence properties via an excited-state intramolecular proton transfer and intramolecular charge transfer combined mechanism. Moreover, rotation of the C-N bond between the pyridine and coumarin units in X1 enabled it to coordinate with metal ions through different binding modes, which acted as an additional dimension of the sensor array. Inspired by this C-N bond rotation strategy, X1SA was determined to be powerful in discriminating 20 kinds of metal ions in both phosphate-buffered saline and 5% serum media in a range of 0.1-100 μM. In addition, the sensor array was also successfully applied in differentiating similar and mixed metal ions such as Fe3+/Fe2+, Cd2+/Hg2+, and Sn2+/Pb2+ in serum samples, which is meaningful for investigating the biological roles of iron and early diagnosis of related metal poisoning accidents.

Near-infrared laser photoacoustic gas sensor for simultaneous detection of CO and H2S.

A ppb-level H2S and CO photoacoustic spectroscopy (PAS) gas sensor was developed by using a two-stage commercial optical fiber amplifier with a full output power of 10 W. Two near-infrared diode lasers with the central wavenumbers of 6320.6 cm-1 and 6377.4 cm-1 were employed as the excitation laser source. A time-division multiplexing method was used to simultaneously detect CO and H2S with an optical switch. A dual-resonator structural photoacoustic cell (PAC) was theoretically simulated and designed with a finite element analysis. A µV level background noise was achieved with the differential and symmetrical PAC. The performance of the multi-component sensor was evaluated after the optimization of frequency, pressure and modulation depth. The minimum detection limits of 31.7 ppb and 342.7 ppb were obtained for H2S and CO at atmospheric pressure.

Evolution of Supramolecular Systems Towards Next-Generation Biosensors.

Supramolecular materials, which rely on dynamic non-covalent interactions, present a promising approach to advance the capabilities of currently available biosensors. The weak interactions between supramolecular monomers allow for adaptivity and responsiveness of supramolecular or self-assembling systems to external stimuli. In many cases, these characteristics improve the performance of recognition units, reporters, or signal transducers of biosensors. The facile methods for preparing supramolecular materials also allow for straightforward ways to combine them with other functional materials and create multicomponent sensors. To date, biosensors with supramolecular components are capable of not only detecting target analytes based on known ligand affinity or specific host-guest interactions, but can also be used for more complex structural detection such as chiral sensing. In this Review, we discuss the advancements in the area of biosensors, with a particular highlight on the designs of supramolecular materials employed in analytical applications over the years. We will first describe how different types of supramolecular components are currently used as recognition or reporter units for biosensors. The working mechanisms of detection and signal transduction by supramolecular systems will be presented, as well as the important hierarchical characteristics from the monomers to assemblies that contribute to selectivity and sensitivity. We will then examine how supramolecular materials are currently integrated in different types of biosensing platforms. Emerging trends and perspectives will be outlined, specifically for exploring new design and platforms that may bring supramolecular sensors a step closer towards practical use for multiplexed or differential sensing, higher throughput operations, real-time monitoring, reporting of biological function, as well as for environmental studies.

Multi-Component Sensor Coatings for Identification and Quantification of Four Chemical Isomers: Ethylbenzene and M-, P-, O-Xylene in Water

Introduction The eight-carbon aromatic isomers ethylbenzene and m-, p-, and o-xylene are difficult to separate from one another due to similarities in molecular structure and physicochemical properties. Separating these isomers is often necessary for industrial chemical synthesis [1]. Additionally, each isomer displays slightly different toxicity [2]. However, in drinking water guidelines, only data for total xylenes is provided because separate quantification of each isomer without the use of sophisticated spectroscopic or chromatographic methods is difficult. Furthermore, for most sensors based on sorption into polymer coatings, polymer/water partition coefficients for these analytes are much lower than polymer/air partition coefficients, resulting in low sensitivity and poor selectivity in their liquid-phase sensing.One method for addressing the above issues is to design multi-component sensor coatings that highlight the small variations in isomer structures. Such coatings can be designed by selecting coating components based on their chemical and physical properties, as well as the properties of the target analytes [3]. Properly designed coatings will show favorable sensing parameters (sensitivity, partial selectivity) in liquid-phase measurements compared to commercially available single-component polymers.Initial results indicate that selectivity of multi-component coatings can be significantly higher, thus enabling identification of chemical isomers, which were previously not (or barely) distinguishable in the liquid phase, with limits of detection in the low parts-per-billion (ppb) by weight. Furthermore, by utilizing advanced sensor signal processing [4] to analyze sensor data, minute response differences between the isomers ethylbenzene and the three xylenes can be uniquely identified, even in solutions of multiple analytes. Sensor Coating Design The design of chemical sensor coatings should select properties of the coating components, in light of the target analytes, that predict and control sensing behavior. The sensing mechanism utilized here relies on bulk absorption of target analytes into polymer coatings. Polarity/dipole moment and polarizability of coating and target isomers affect the absorption process and, as a result, the sensitivity of the coated sensor to the analyte. Multi-component coatings using polymer-plasticizer blends to generate Hansen solubility parameters (HSP) that favor strong coating-analyte interactions, i.e. favorable coating/water analyte partition coefficients, were designed for this work. These coatings exhibit significantly increased sensitivity and partial selectivity to target isomers compared to single-component polymers, such as poly(epichlorohydrin) or poly(isobutylene). As a result, minute variations in analyte properties (polarity/dipole moment, polarizability, etc.) can be detected at lower analyte concentrations. The designed coatings consist of blends of polystyrene (PS) and one of two plasticizers, diisooctyl azelate (DIOA) or ditridecyl phthalate (DTP). Identification of ethylbenzene and xylene is demonstrated for each polymer-plasticizer pair, with sensitivities for ethylbenzene and each xylene isomer in good agreement with trends in dipole moments and polarizabilities. Method Solutions of multi-component coatings were prepared with varying plasticizer-to-polymer weight ratios (given below) and with varying concentrations of polymer-plasticizer blend in the solvent. Coatings were deposited onto SH-SAW sensor devices via spin coating for use in direct liquid-phase measurements. Coating thicknesses were determined by profilometry. Coated sensors were exposed to diluted aqueous solutions of target analytes in flow-through measurements. The concentrations of analyte solutions were independently confirmed using a gas-chromatograph photoionization detector (GC-PID) system. Frequency responses of coated sensors are used to extract sensing parameters such as sensitivity, response time constant, and viscoelastic properties of the coatings. Extracted sensing parameters for each isomer serve as inputs for an advanced estimation-theory-based sensor signal-processing technique that quantifies mixtures. Results and Conclusions Table 1 shows relative energy difference (RED) values for pairs of coatings and analytes calculated using Equation 1, as well as dipole moment, polarizability, and measured sensitivity and response time constant for each isomer. The polymer-plasticizer blend coatings selected are 17.5% DIOA-PS and 30% DTP-PS. RED values for each pair of materials are well below 1.0, indicating excellent miscibility. While differences between isomers are relatively small, the measured variation in responses is highlighted with an appropriate multivariate signal-processing method. The extracted average sensitivity and average response time constant for each coating/analyte pair, shown in Table 1, correlate well with the trends in analytes’ dipole moments and polarizabilities. High sensitivities to ethylbenzene and xylene isomers are observed. Responses of the DIOA-PS coating to ethylbenzene and total xylenes are easily distinguishable from one another. Figure 1 shows single-analyte response curves for the DTP-PS coating for ethylbenzene and m-, p-, and o-xylene. Sensing parameters were extracted from the responses, thus allowing minute differences to be clearly observed. It is demonstrated that DTP-PS coatings show partial selectivity between ethylbenzene, m-, p-, and o-xylene. Figure 2 shows the response curve of a multi-analyte solution of the three xylene isomers, with estimated and measured concentrations for each isomer in the mixture in Table 2. Experimental responses and extracted results strongly support that selectivity to each xylene isomer can be achieved with appropriate sensor coating design.