Concentration Of Target Analyte Research Articles

Field-Based cDNA-Biosensor for Accurate Detection of Canine Distemper Virus in Tissue Samples.

Canine distemper, a viral disease with a global impact on various animals including dogs, foxes, wolves, lions, and leopards, requires early diagnosis for effective treatment and outbreak control. Common laboratory methods, such as enzyme-linked immunosorbent assay, polymerase chain reaction, and viral isolation, face challenges such as extended turnaround times, high costs, and the expertise required. This study has developed a field-based biosensor for detecting the canine distemper virus (CDV), utilising a screen-printed carbon electrode (SPCE) and a computer-assisted portable potentiostat. A 30-mer oligonucleotide capture probe, designed using Primer3 Plus software version 3.3.0, detected hybridisation with the CDV complementary strand through electrochemical analysis via differential pulsed voltammetry. The developed biosensor exhibited good linearity in quantifying the target analyte concentration (0.1 to 12.8µM), with a detection limit of 0.05µM, indicating high sensitivity. Specificity tests using complementary and non-complementary sequences confirmed the biosensor's accuracy. The electrode can be reused up to eight times with a residual capacity of 93.72 ± 5.45% after regeneration using a 50mM NaOH solution. The developed biosensor was also used to detect CDV in biological samples after RNA extraction and amplification. Results from the biosensor aligned with those from reverse transcriptase polymerase chain reaction (RT-PCR) findings, showing 100% agreement. These findings support the potential development of a field-deployable portable device for effectively diagnosing canine distemper in biological samples.

Enhancing Analytical Potential for Ultratrace Analysis of Inorganic Oxyanions Using Extraction Procedures with Layered Double Hydroxides.

This article provides an overview of the use of layered double hydroxides (LDHs) as effective sorbents in various extraction methods, including column-based solid-phase extraction (SPE), dispersive solid-phase extraction (DSPE), and magnetic solid-phase extraction (MSPE), for the separation and preconcentration of inorganic oxyanions of chromium, arsenic, and selenium. The primary focus is on enhancing the analytical performance of spectrometric detection techniques, particularly in terms of sensitivity and selectivity when analyzing low concentrations of target analytes in complex matrices. LDHs, which can be readily prepared and structurally modified with various substances, offer promising potential for the development of novel analytical methods. When used in analytical extraction procedures and following careful optimization of experimental conditions, the developed methods have yielded satisfactory results, as documented by studies reviewed in this paper. This review is intended to assist analytical chemists in scientific laboratories involved in developing new extraction procedures.

Periodic Arrays of Plasmonic Ag-Coated Multiscale 3D-Structures with SERS Activity: Fabrication, Modelling and Characterisation.

Surface enhanced Raman spectroscopy (SERS) is gaining importance as sensing tool. However, wide application of the SERS technique suffers mainly from limitations in terms of uniformity of the plasmonics structures and sensitivity for low concentrations of target analytes. In this work, we present SERS specimens based on periodic arrays of 3D-structures coated with silver, fabricated by silicon top-down micro and nanofabrication (10 mm × 10 mm footprint). Each 3D-structure is essentially an octahedron on top of a pyramid. The width of the top part-the octahedron-was varied from 0.7 µm to 5 µm. The smallest structures reached an analytical enhancement factor (AEF) of 3.9 × 107 with a relative standard deviation (RSD) below 20%. According to finite-difference time-domain (FDTD) simulations, the origin of this signal amplification lies in the strong localization of electromagnetic fields at the edges and surfaces of the octahedrons. Finally, the sensitivity of these SERS specimens was evaluated under close-to-reality conditions using a portable Raman spectrophotometer and monitoring of the three vibrational bands of 4-nitrobenzenethiol (4-NBT). Thus, this contribution deals with fabrication, characterization and simulation of multiscale 3D-structures with SERS activity.

Multicolorimetric Sensor Array Based on Silver Metallization of Gold Nanorods for Discriminating Dopaminergic Agents.

Dopaminergic agents are compounds that modulate dopamine-related activity in the brain and peripheral nerves within the pathways on both sides of the blood-brain barrier. Atypical levels of them can precipitate a multitude of neurological disorders, whose timely diagnosis signifies not only stopping the advancement of the illness but also surmounting it. A silver metallized gold nanorod (AuNRs) conditional sensor array, designed to detect dopaminergic agents for assessing nervous system disorders, yielded significant results in simultaneous detection and discrimination of Benserazide (Benz), Levodopa (L-DOPA), and Carbidopa (Carb). The array was composed of two different concentrations of silver ions as sensor elements (SEs), which generated unique signatures indicative of the presence of reductive target analytes, triggered by the incongruent formation of the Au@Ag core-shell, causing visual and fingerprint colorimetric patterns. Generating diverse responses is the key to the functionality of array-based sensing, which facilitated achieving spectral and color variation originating from the blue shift of AuNRs longitudinal localized surface plasmon resonance (LLSPR) in the extinction spectrum. Also, employing a smartphone camera enables clear visual discrimination across an extensive concentration span. Pattern recognition through linear discriminant analysis (LDA) underscored the robust discrimination accuracies of this sensor, along with quantification by means of partial least-squares regression (PLSR), affirming its potential for practical applications. Notably, the array demonstrated high sensitivity in detecting varied concentrations of target analytes, even in commercial drug samples. The sensor responses exhibited a linear correlation with the concentrations of Benz, L-DOPA, and Carb ranging from 1.59 to 100.0, 5.26 to 100.0, and 5.32 to 100.0 μmol L-1, respectively, and the minimum detectable concentrations for Benz, L-DOPA, and Carb were measured at 0.53, 1.75, and 1.77 μmol L-1, respectively. The implemented machine-learning-empowered array-based sensor represents advancements in dopaminergic agent tracing and naked eye detection.

Self-Assembled Plasmonic Structural Color Colorimetric Sensor for Smartphone-Based Point-Of-Care Ammonia Detection in Water.

Monitoring chemical levels is crucial for safeguarding both the environment and public health. Elevated levels of ammonia, for instance, can harm both humans and aquatic ecosystems, often indicating contamination from agriculture, industry, or sewage. Developing portable, high-resolution, and affordable methods for in situ monitoring of ammonia is thus imperative. Plasmonic sensors offer a promising solution, detecting ammonia by correlating changes in their optical response to the target analyte's concentration. While they are highly sensitive and can be fabricated in a variety of portable and user-friendly formats, some still require reagents or expensive optical equipment, which hinder their widespread adoption. Here, we present a self-assembled nanoplasmonic colorimetric sensor capable of directly detecting ammonia concentrations in aqueous matrices. The proposed sensor exploits the plasmonic resonance of the nanostructures to transduce changes in the chemical environment into alterations in color, offering a label-free method for real-time analysis. The sensor is fabricated using a self-assembling technique compatible with low-cost mass production based on aluminum and aluminum oxide, ensuring affordability and avoiding the use of other toxic chemicals. We developed a model to predict ammonia concentrations based on visible color change of the sensor, achieving a detection limit of 8.5 ppm. Furthermore, to address the need for on-site detection, we integrated smartphone technology for real-time color change analysis, eliminating the need for expensive, bulky optical instruments. Indeed, our approach offers a cost-effective, portable, and user-friendly solution for ammonia detection in water without the need for chemical reagents or spectrometers, making it ideal for field applications. Interestingly, this platform extends its applicability beyond ammonia detection, enabling the monitoring of various chemicals using a smartphone, without the need for any additional costly equipment.

(Invited) Continuous Monitoring of Small Molecules Using Electrochemical Aptamer-Based Biosensors with CMOS Electronics

The ability to continuously monitor specific “molecules” over a long period of time can provide fundamentally new insights into disease dynamics and the transition from a healthy state and lead to the design of next-generation screening and detection tools. However, currently only a few biomolecules, such as glucose, oxygen, and dopamine, can be continuously and real-time monitored with significant relevance to chronic disease management. The prevalent glucose sensor uses glucose oxidase (GOx), an enzyme designed to oxidize glucose persistently in its proximity, producing an electrochemical current proportional to the glucose concentration. The enzyme's specificity means it cannot be readily adapted to target different biomolecules, thus constraining its versatility. This limitation also extends to many enzyme-based sweat sensors. In contrast, oxygen and dopamine measurements exploit their inherent molecular characteristics. Oxygen saturation (SpO2) in blood is determined via pulse oximetry, which uses the distinct light absorption properties of deoxygenated and oxygenated blood under red and infrared lights. Dopamine, a neurotransmitter integral to our emotional responses, is highly electrochemically active and can undergo reversible oxidation, allowing it to be detected by analytical methods like fast-scan cyclic voltammetry (FSCV). Nevertheless, such distinct properties are not common among biomolecules, making these methods non-transferable to a broader range.To this end, our group and others have previously utilized aptamers to achieve continuous detection of small molecules in vivo. Aptamers are “synthetic antibodies” composed of nucleic acids that can specifically bind to the target analytes in complex samples, such as the whole blood (Fig. 1(a)). Importantly, they can be engineered into “aptamer-switches” that undergo structure-switching upon target binding in a reversible manner. By conjugating electroactive reporters to the aptamers, the changes in their structure (thus, the analyte concentration) can be detected electrochemically. As sample preparation is not needed, aptamer switches are capable of continuous monitoring of biomolecules in vivo.Coupling with structure-switching aptamers, our team has presented the first wireless, electrochemical aptamer-sensing chip in the 65-nm CMOS technology and achieved continuous recording of small-molecule drugs, e.g., antibiotics or chemotherapeutics, from a freely moving rodent (Fig. 1(b) and 1(c)). The chip features a chronoamperometric (CA) sensing circuit that probes the electron transfer (ET) kinetics of the structure-switching aptamers that are correlated to the concentration of target analytes. Notably, we have innovated a sample-and-hold (S/H) circuit technique into our readout front end to overcome the noise/power trade-off commonly experienced in conventional circuit design (Fig. 1(d)). This technique employs two switched-capacitor circuits that maintain the electrode potentials on an open-loop basis, disabling the power-intensive and noise-contributing on-chip amplifiers during the recording phase. The technique achieves simultaneous reductions of 286× in circuit noise (4.36 nArms to 15.2pArms) and 23× in power consumption (5.25 mW to 0.22 mW), along with a 10× increase in throughput compared to the traditional square-wave voltammetry (SWV) readout methods that have been commonly used in interrogating E-AB sensors (5 vs. 0.5 Samples/sec). Fig. 1(d) shows the measured current transients that reflects the changes in ET kinetics at different target concentrations.While previous circuit focuses on lowering the electronics noise, we also develop a second electrochemical readout circuit that aims at boosting the detection signal through redox current integration at the circuit domain. This integration process captures the charges, instead of the current, leading us to term this technique square-wave voltcoulometry (SWVC, Fig. 1(e)). Our analysis demonstrates that the proposed SWVC technique can yield a remarkable signal improvement of 250×. Importantly, this improvement is achieved while maintaining nearly identical levels of input-referred noise (5 pArms) at a power consumption of only few mW. Fig. 1(f) compares the measurements obtained using conventional SWV readout and the proposed SWVC technique. The results demonstrate a remarkable 25-fold improvement in the signal-to-noise ratio (SNR), and we anticipate another 4× boosting once we optimize the test setup.We've also addressed issues related to drift and biofouling. We utilize kinetic-differential measurements (KDM) that monitor the rate of current change rather than absolute current values, mitigating the effects of biosensor detachment from the electrode surface. We also develop a dual-aptamer strategy where two aptamer sensors responding differentially to the same analyte targets are utilized to increase the signal while rejecting the slow-moving drifts (Fig. 1(g)). For biofouling, we employ a combinatorial-selected polyacrylamide hydrogel coating to specifically resist platelet adhesion (Fig. 1(h)). By integrating these advancements, we have validated our technology in vivo within whole blood via a probe catheterized into a rat's vein and interstitial fluids (ISF) through whole-system implantation (Fig. 1(i)). This technology also holds promise for the multiplexed monitoring. Figure 1

TWO-DIMENSIONAL ION CHROMATOGRAPHY TANDEM-MASS SPECTROMETRIC (2D-IC-MS/MS) METHOD FOR THE ANALYSIS OF PHOSPHORUS COMPOUNDS IN SOIL

Investigations into soil organic phosphorus (Po) dynamics are instrumental in understanding the transformations and processes responsible for ecosystem productivity. However, quantitative analysis of Po in a soil environment is extremely challenging due to low target analyte concentrations and matrix interferences with chromatography and analysis. Consequently, a two-dimensional ion chromatography-tandem mass spectrometric (2D-IC-MS/MS) method was developed to estimate soil Po concentrations. The first dimension diverted early eluting anions to waste while preconcentrating P compounds in a trap column, followed by chromatographic separation and detection in the second dimension. Detection was done using a mass spectrometer, and quantification was performed using the multiple reaction monitoring scan (MRM) method. The linear range of the studied P compounds, mostly nucleotides, was 0.05 – 50 ng/mL. Most P compounds were detected and quantified in calcareous subsoil samples in the concentration ranges 0.70 – 51.78 ng/g. The developed method achieved chromatographic separation that allowed unambiguous identification of isobars/isomers and isotopologues contributing to interferences in MS detection. However, improvements to the extraction method and post-clean-up procedures are required due to the complexity of soil extract composition, extreme matrix effect and/or loss of analyte during preconcentration. The method is ideal for simultaneously analyzing P compounds from environmental samples to elucidate key components of the soil P dynamics.

Fabrication of rGO-decorated hBNNS hybrid nanocomposite via organic-inorganic interfacial chemistry for enhanced electrocatalytic detection of carcinoembryonic antigen.

Organic-inorganic hybrid nanocomposites (OIHN), with tailored surface chemistry, offer ultra-sensitive architecture capable of detecting ultra-low concentrations of target analytes with precision. In the present work, a novel nano-biosensor was fabricated, acquainting dynamic synergy of reduced graphene oxide (rGO) decorated hexagonal boron nitride nanosheets (hBNNS) for detection of carcinoembryonic antigen (CEA). Extensive spectroscopic and microscopic analyses confirmed the successful hydrothermal synthesis of cross-linked rGO-hBNNS nanocomposite. Uniform micro-electrodes of rGO-hBNNS onto pre-hydrolyzed ITO were obtained via electrophoretic deposition (EPD) technique at low DC potential (15V). Optimization of antibody incubation time, pH of supporting electrolyte, and immunoelectrode preparation was thoroughly investigated to enhance nano-biosensing efficacy. rGO-modified hBNNS demonstrated 29% boost in electrochemical performance over bare hBNNS, signifying remarkable electro-catalytic activity of nano-biosensor. The presence of multifunctional groups on the interface facilitated stable crosslinking chemistry, increased immobilization density, and enabled site-specific anchoring of Anti-CEA, resulting in improved binding affinity. The nano-biosensor demonstrated a remarkably low limit of detection of 5.47pg/mL (R2 = 0.99963), indicating exceptional sensitivity and accuracy in detecting CEA concentrations from 0 to 50ng/mL. The clinical evaluation confirmed its exceptional shelf life, minimal cross-reactivity, and robust recovery rates in human serum samples, thereby unraveling the potential for early, highly sensitive, and reliable CEA detection.

An electrocatalytic sensing of 2, 6-dichlorophenol based on Dy2O3-Co3O4@CeO2 nanocomposite modified GCE: An enhanced environmental safety

Anthropogenic activities are continuously polluted our environment by releasing various kind of toxic chemicals as waste product. Their removal has gained the impressive importance among the scientists in order to maintain the integrity of our ecosystem. In this present work, Dy2O3-Co3O4@CeO2 nanocomposite (NC) was prepared by simple solution method with the aspiration to detect as well as electro-hydrolyzed the toxic chemicals in an aqueous system. Structural, morphological and optical properties of our newly synthesised nanocomposite were investigated by using advanced analytical tools such as ultraviolet–visible difused reflectance spectroscopy (UV-DRS), fourier transform infrared (FT-IR) spectroscopy, powder X-ray diffraction (XRD), field emission scanning electron microscope equipped with energy dispersive spectroscopy (FESEM-EDS), and transmission electron microscope (TEM) in addition to Brunauer-Emmet-Teller (BET) analysis. After detailed characterization of newly synthesized Dy2O3-Co3O4@Ce2O3 nanocomposite, it was used with 5 % PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrenesulfonate), a conducting polymer binder, to modify the glassy carbon electrode (GCE) as selective electrochemical sensor (Dy2O3-Co3O4@CeO2-NC/PEDOT:PSS/GCE). After that, this newly designed Dy2O3-Co3O4@CeO2-NC/PEDOT:PSS/GCE was evaluated so as to check its detection as well as electrocatalytic behaviour against the toxic chemicals in an aqueous system using novel electrochemical current–potential (I–V) approach, for the first time. A comparative investigation revealed that this newly designed Dy2O3-Co3O4@CeO2-NC/PEDOT:PSS/GCE was very selective against the 2, 6-dichlorophenol (2, 6-DCP) and had good affinity with it even in the company of other interference chemicals. In order to optimise newly developed Dy2O3-Co3O4@CeO2-NC/PEDOT:PSS/GCE as an effective as well as selective 2, 6-DCP electrochemical sensor, analytical parameters such as, limit of detection (LOD), limit of quantification (LOQ), sensitivity etc were calculated from slop of calibration curve. The LOD at signal-to-noise ratio (S/N) of 3, LOQ, and sensitivity were calculated as 0.014 ± 0.001 nM, 0.046 ± 0.001 nM, and 65.82 µAµM-1cm−2, respectively, over a linear dynamic range (LDR) of our target analyte concentration (0.1 nM – 0.1 M) with r2 square value as 0.9988. Consequently, this study enlightens us that this newly fabricated electrochemical sensor, selective only for 2, 6-DCP, is very cost-effective, efficient, and stable over time. Moreover, it also initiates a new way to effectively interrogate the various toxic chemicals in real samples by using a novel electrochemical (I–V) approach based on reported / non-reported different kind of semiconductor nanocomposite modified GCEs as selective electrochemical sensors.

On-Chip Electrochemical Sensing with an Enhanced Detecting Signal Due to Concentration Polarization-Based Analyte Preconcentration.

Here, we integrated two key technologies within a microfluidic system, an electrokinetic preconcentration of analytes by ion Concentration Polarization (CP) and local electrochemical sensors to detect the analytes, which can synergistically act to significantly enhance the detection signal. This synergistic combination, offering both decoupled and coupled operation modes for continuous monitoring, was validated by the intensified fluorescent intensities of CP-preconcentrated analytes and the associated enhanced electrochemical response using differential pulse voltammetry and chronoamperometry. The system performance was evaluated by varying the location of the active electrochemical sensor, target analyte concentrations, and electrolyte concentration using fluorescein molecules as the model analyte and Homovanillic acid (HVA) as the target bioanalyte within both phosphate-buffered saline (PBS) and artificial sweat solution. The combination of on-chip electrochemical sensing with CP-based preconcentration renders this generic approach adaptable to various analytes. This advanced system shows remarkable promise for enhancing biosensing detection in practical applications while bridging the gap between fundamental research and practical implementation.

Evidence-based evaluation of the analytical schemes in ASTM E2329-17 Standard Practice for Identification of Seized Drugs for methamphetamine samples

This study involved 71 forensic seized drug laboratories analyzing 65 total samples; 17 were ground-truth positive (i.e., they contained methamphetamine or cocaine); 48 were ground-truth negative (i.e., they did not contain methamphetamine or cocaine). The positive samples were prepared at several target-analyte concentrations and combined with common cutting agents. The negative samples were designed to be challenging and prepared to contain positional isomers of methamphetamine. Participants were sent two different sample sets. In the first, they were directed to only use a single, pre-selected analytical technique. In the second, they were directed to use a pre-selected analytical scheme consisting of multiple techniques in compliance with ASTM E2329-17. The results of the study showed good accuracy; sensitivity was 1.000 for all analytical schemes with 1-specificity (the false-positive rate) ranging from 0.000 to 0.250 when ASTM E2329-17 compliant analytical schemes were used. When only a single technique was used, accuracy was generally not as good; sensitivity ranged from 1.000 to 0.091, and 1-specificity ranged from 0.000 to 0.245.

Nanozyme-assisted amplification-free CRISPR/Cas system realizes visual detection.

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR associated) system has proven to be a powerful tool for nucleic acid detection due to its inherent advantages of effective nucleic acid identification and editing capabilities, and is therefore known as the next-generation of molecular diagnostic technology. However, the detection technologies based on CRISPR/Cas systems require preamplification of target analytes; that is, target gene amplification steps through isothermal amplification or PCR before detection to increase target analyte concentrations. This creates a number of testing limitations, such as extended testing time and the need for more sophisticated testing instruments. To overcome the above limitations, various amplification-free assay strategies based on CRISPR/Cas systems have been explored as alternatives, which omit the preamplification step to increase the concentrations of the target analytes. Nanozymes play a pivotal role in enhancing the sensitivity of CRISPR-based detection, enabling visual and rapid CRISPR assays. The utilization of nanozyme exceptional enzyme-like catalytic activity holds great promise for signal amplification in both electrochemical and optical domains, encompassing strategies for electrochemical signal sensors and colorimetric signal sensors. Rather than relying on converting a single detection target analyte into multiple analytes, these methods focus on signal amplification, the main mechanism of which involves the ability to form a large number of reporter molecules or to improve the performance of the sensor. This exploitation of nanozymes for signal amplification results in the heightened sensitivity and accuracy of detection outcomes. In addition to the strategies that improve sensor performance through the application of nanozymes, additional methods are needed to achieve visual signal amplification strategies without preamplification processes. Herein, we review the strategies for improving CRISPR/Cas systems that do not require preamplification, providing a simple, intuitive and preamplification-free CRISPR/Cas system detection platform by improving in-system one-step amplification programs, or enhancing nanozyme-mediated signal amplification strategies.

(General Student Poster Award Winner, 1st Place) Accelerated Estimation of Chemical and Sensory Liquid Attributes Using an AI-Assisted Electrochemical Electronic Tongue

Electronic tongues based on arrays of potentiometric sensors bears promises for enabling portable and fast analysis of complex liquids by leveraging low-selectivity and high-sensitivity of the inherent sensing materials. Thus, they have been studied and developed to target multiple applications, including analysis of chemical compositions or for untargeted quality control in the food industry. A key element to enable practical use of electronic tongues, however, has often been the analysis and interpretation of their combinatorial response due to the cross-sensitivity of the employed sensors. Indeed, disentangling the effect of multiple liquid constituents on the potentiometric response is the main associated challenge. The present contribution aims at studying the feasibility and accuracy of employing this class of sensors to both the quantification of multiple analytes in water as well as prediction of sensory attributes in coffee samples. A proof-of-concept system comprises an integrated array of miniaturized polymeric sensors and an electronic board records 15 differential voltages, produced by the interaction with both ionic and organic constituents of liquids, and can transmit them via Bluetooth to an external device. An automated pipeline performs pre-processing, hand-crafted feature extraction and training of machine learning models that can be then deployed on the cloud or edge device (e.g., smartphone). The same array can be then reconfigured to address a new specific task by leveraging new potentiometric data collection and applying the automated data analysis. In particular, the device presented herein employed 16 polymeric sensors that were electrodeposited on conventional electroless nickel immersion gold (ENIG) electrodes. The conductive polymers (PEDOT, PPy, PANI and PAPBA) were synthesized by chronoamperometry or cyclic voltammetry and enriched with doping agents for enhanced sensitivity. Differential voltages between these polymeric sensors were measured during the transition of the sensor array from a reference solution to a test solution, thereby obviating the need for a conventional reference electrode. Indeed, the use of low-selective sensors for potentiometric measurements does not require integration of reference electrodes, which are known to be unpractical for remote sensing applications. The analysis of the potentiometric response revealed deviations from linear sensitivities, showing non-Nernstian voltage trends when varying concentration of target analytes. Training data were obtained by alternatively immersing the sensor array in reference (120 s) and test (60 s) solutions continuously using an automated test rig. In addition to the conventional approach of measuring the equilibrium potential after a certain settling time, the complete evolution of the potentiometric signal in time could provide richer information that could be useful for building calibration models. Hence, hand-crafted features were extracted from time series recorded during sensor training in order to reduce the dimension of data and describe the complete voltage perturbation of the sensor during transition between reference and test solution. If exposed to enough “known liquid examples”, the sensor array can learn patterns to perform accelerated qualitative and quantitative analysis. The device has demonstrated enhanced discrimination of various mixtures and was proved able to predict beverage sensory properties after intensive training with samples with known sensory attributes. Firstly, the sensitivity to multiple ionic species was estimated in model mixtures obtained following an Orthogonal Experimental Design (OED) for a set of four metal ions, including Al3+, Cu2+, Na+ and Fe3+, and another set comprising Ca2+, Mg2+ and Na+. The former experiment demonstrated that non-linear regression models, such as a Random Forest of Extra Trees, can cope with non-linearity of the sensor response and yielded better performances than widely used multivariate regression model i.e., Multiple Linear Regression (MLR). The mean relative quantification error varied between 1% for Fe3+ and 44% for Na+ in the concentration range 1-10 mg/L. The second mixture set was used instead to unveil the cross-sensitive response of the sensor array, build a quantification model to predict major cations in mineral water and proving the importance of feature selection for data-driven sensors. Overall, the sensor showed comparable accuracy to ICP-MS (MRE 12-24%) but with a drastic reduction of cost and processing time. Finally, the same sensor array configuration was also used to test 12 coffee samples, which were also evaluated by a sensory panel through 5 taste attributes. Regression models were built to map instrument response to sensory data and Extra-Tree algorithm yielded the highest performances with a MRE varying from 8% to 21% depending on coffee sample and validation scheme. Thus, it was demonstrated that data-driven sensor arrays could be also valuable alternative tools for accelerated evaluation of beverage sensory characteristics and support food innovation processes. Figure 1

Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning

False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

A Novel Biosensing Approach: Improving SnS2 FET Sensitivity with a Tailored Supporter Molecule and Custom Substrate.

The exclusive features of two-dimensional (2D) semiconductors, such as high surface-to-volume ratios, tunable electronic properties, and biocompatibility, provide promising opportunities for developing highly sensitive biosensors. However, developing practical biosensors that can promptly detect low concentrations of target analytes remains a challenging task. Here, a field-effect-transistor comprising n-type transition metal dichalcogenide tin disulfide (SnS2 ) is developed over the hexagonal boron nitride (h-BN) for the detection of streptavidin protein (Strep.) as a target analyte. A self-designed receptor based on the pyrene-lysine conjugated with biotin (PLCB) is utilized to maintain the sensitivity of the SnS2 /h-BN FET because of the π-π stacking. The detection capabilities of SnS2 /h-BN FET are investigated using both Raman spectroscopy and electrical characterizations. The real-time electrical measurements exhibit that the SnS2 /h-BN FET is capable of detecting streptavidin at a remarkably low concentration of 0.5 pm, within 13.2s. Additionally, the selectivity of the device is investigated by measuring its response against a Cow-like serum egg white protein (BSA), having a comparative molecular weight to that of the streptavidin. These results indicate a high sensitivity and rapid response of SnS2 /h-BN biosensor against the selective proteins, which can have significant implications in several fields including point-of-care diagnostics, drug discovery, and environmental monitoring.

Occurrence, spatial variation, seasonal difference, and risk assessment of neonicotinoid insecticides, selected agriculture fungicides, and their transformation products in the Yangtze River, China: From the upper to lower reaches

Neonicotinoid insecticides (NNIs) and agricultural fungicides (including strobilurin, azole, and morpholine fungicides) are widely used, while data on their contamination in the Yangtze River of China and the risks posed by them are limited. The occurrence and distribution of ten NNIs, twenty-one transformation products (TPs) of them, seventeen agricultural fungicides, and six TPs of them were investigated in the main stream of the Yangtze River. Surface water samples (n = 144) were obtained from 72 sampling points in dry season and wet season. Among the NNIs, the detection frequencies (DFs) of acetamiprid (ACE), clothianidin, dinotefuran, flonicamid, imidacloprid (IMI), thiacloprid (THCP), and thiamethoxam (THM) were higher than 85%, with the median concentrations ranged from 0.06 ng/L (THCP) to 3.63 ng/L (IMI). The DFs of the TPs descyano-acetamiprid, desmethyl-acetamiprid (DM-ACE), N-[(6-Chloropyridin-3-yl) methyl] methylamine, desnitro-clothianidin, desnitro-imidacloprid, desnitro-thiamethoxam, imidacloprid-urea, and thiamethoxam-urea (THM-urea) were higher than 80%, with the median concentrations ranged from 0.25 ng/L for DM-ACE to 2.41 ng/L for THM-urea. Some agricultural parent fungicides, including azoxystrobin (AZS), carbendazim (CBDZ), difenoconazole, dimethomorph, propiconazole, pyraclostrobin, and tebuconazole (TBCZ), were detected in all the samples; others were also detected in more than 80% of the samples except for fluoxastrobin (12.5%). The median concentrations of the frequently detected fungicides ranged from 0.02 ng/L (trifloxystrobin) to 26.8 ng/L (CBDZ). The DFs of the fungicide TPs azoxystrobin acid (AZS acid), difenoconazole-alcohol, tebuconazole-tert-butylhydroxy (TBCZ-OH), and 5-hydroxymethyl-tricyclazole were higher than 75%, with the median concentrations ranged from 0.09 ng/L (TBCZ-OH) to 1.80 ng/L (AZS acid). The summed concentrations of the NNIs and their TPs at the sampling points varied between 0.23 and 418 ng/L, and the summed concentrations of the selected fungicides and their TPs varied from 0.29 to 1160 ng/L. The spatial distribution of most target analytes revealed an increasing trend in their concentrations from the upstream to downstream Yangtze River (250 times increase in their cumulative concentration). Most target pesticides in this study had significantly higher concentrations during wet season than those during dry season. Furthermore, ecological risk assessment suggested that ACE, IMI, THM, CBDZ, TBCZ, and thifluzamide in some samples (n = 1, 11, 1, 1, 1, and 6, respectively) posed high risks to the ecosystem (risk quotient > 1). Priority attention should be paid to the ecological risk posed by these pesticides. Thirty-seven samples had concentrations of individual target analytes over 100 ng/L and four samples had cumulative concentrations of the target analytes over 500 ng/L, exceeding the European Commission guideline values. Taken together, our findings demonstrate a widespread occurrence of the NNIs, agricultural fungicides, and their TPs in the mainstream of the Yangtze River and potential ecological risks posed by some of them.

Portable Sensor Array for On-Site Detection and Discrimination of Pesticides and Herbicides Using Multivariate Analysis.

Modern agricultural practice relies heavily on pesticides and herbicides to increase crop productivity, and consequently, their residues have a negative impact on the environment and public health. Thus, keeping these issues in account, herein we developed an azodye-based chromogenic sensor array for the detection and discrimination of pesticides and herbicides in food and soil samples, utilizing machine learning approaches such as hierarchical clustering analysis, principal component analysis, linear discriminant analysis (LDA), and partial least square regression (PLSR). The azodye-based sensor array was developed in combination with various metal ions owing to their different photophysical properties, which led to distinct patterns toward various pesticides and herbicides. The obtained distinct patterns were recognized and processed through automated multivariate analysis, which enables the selective and sensitive identification and discrimination of various target analytes. Further, the qualitative and quantitative determination of target analytes were performed using LDA and PLSR; the results obtained show a linear correlation with varied concentrations of target analytes with R2 values from 0.89 to 0.96, the limit of detection from 5.3 to 11.8 ppm with a linear working range from 1 to 30 μM toward analytes under investigation. Further, the developed sensor array was successfully utilized for the discrimination of a binary mixture of pesticide (chlorpyrifos) and herbicide (glyphosate).

A Comprehensive Study of Electrocatalytic Degradation of M-Tolylhydrazine with Binary Metal Oxide (Er2O3@NiO) Nanocomposite Modified Glassy Carbon Electrode

Generally, our ecosystem is continuously contaminated as a result of anthropogenic activities that form the basis of our comfort in our routine life. Thus, most scientists are engaged in the development of new technologies that can be used in environmental remediation. Herein, highly calcined binary metal oxide (Er2O3@NiO) semiconductor nanocomposite (NC) was synthesized using a classical wet chemical process with the intention to both detect and degrade the toxic chemicals in an aqueous medium using a novel electrochemical current–potential (I–V) approach for the first time. Optical, morphological, and structural properties of the newly synthesized semiconductor NC were also studied in detail using FT-IR, UV/Vis., FESEM-EDS, XPS, BET, EIS, and XRD techniques. Then, a modified glassy carbon electrode (GCE) based on the newly synthesized semiconductor nanocomposite (Er2O3@NiO-NC/Nafion/GCE) as a selective electrochemical sensor was fabricated with the help of 5% ethanolic-Nafion as the conducting polymer binder in order to both detect and electro-hydrolyze toxic chemicals in an aqueous medium. Comparative study showed that this newly developed Er2O3@NiO-NC/Nafion/GCE was found to be very selective against m-tolyl hydrazine (m-Tolyl HDZN) and to have good affinity in the presence of other interfering toxic chemicals. Analytical parameters were also studied in this approach to optimize the newly designed Er2O3@NiO-NC/Nafion/GCE as an efficient and selective m-Tolyl HDZN sensor. Its limit of detection (LOD) at an SNR of 3 was calculated as 0.066 pM over the linear dynamic range (LDR) of our target analyte concentration (0.1 pM–0.1 mM). The limit of quantification (LOQ) and sensitivity were also calculated as 0.22 pM and 14.50 µAµM−1cm−2, respectively. m-Tolyl HDZN is among the toxic chemicals in our ecosystem that have lethal effects in living beings. Therefore, this newly designed electrochemical sensor based on semiconductor nanostructure material offers, for the first time, a cost-effective technique, in addition to long-term stability, that can be used as an alternative for efficiently probing other toxic chemicals in real samples.

In-vial dried urine spot collection and processing for quantitative analyses.

Analysis of dried urine spots (DUSs) is becoming an emerging technique in clinical, toxicological, and forensic chemistry due to the fully non-invasive collection, facile transportation, and simple storage of DUS samples. Correct DUS collection and elution is of the utmost importance because inadequate DUS sampling/processing may have direct consequences on quantitative DUS analyses and these aspects were, for the first time, comprehensively investigated in this contribution. Various groups of endogenous and exogenous species were selected as model analytes and their concentrations were monitored in DUSs collected on standard cellulose-based sampling cards. Strong chromatographic effects were observed for most analytes having a crucial impact on their distribution within the DUSs during sampling. Concentrations of target analytes were up to 3.75-fold higher in the central DUS sub-punch in comparison to the liquid urine. Consequently, substantially reduced concentrations of these analytes were determined in peripheral DUS sub-punches demonstrating that sub-punching, often applied to dried material spots, is not acceptable for quantitative DUS analyses. Hence, a simple, rapid, and user-friendly procedure was suggested, which employed an in-vial collection of a known urine volume on a pre-punched sampling disc (using a low-cost micropipette designed for patient-centric clinical sampling) and in-vial processing of the whole DUS. Excellent accuracy (0.20%) and precision (0.89%) of liquid transfers were achieved by the micropipette, which was also applied to remote DUS collection by laic and expert users. The resulting DUS eluates were analysed by capillary electrophoresis (CE) for the determination of endogenous urine species. The CE results demonstrated no significant differences between the two user groups, elution efficiencies of 88-100% (in comparison to the liquid urine), and precision better than 5.5%.