Selective Sensor Research Articles

A Novel, Selective, and Fast Electrochemical Sensor Based on an 8-Nitroquinoline-Coated Pencil Graphite Electrode for Rutin Determination in Orange Juice

In this study, we developed a facile and low-cost method to prepare a modified electrode by coating the pencil graphite electrode (PGE) surface with 8-nitroquinoline (8-NQ). The surface modification process was carried out by electrochemical reduction of 8-NQ on the PGE surface in ethanol solution. The optimum conditions were determined for the experimental studies. The differential pulse voltammetry was used to determine rutin with the modified electrode (8-NQ/PGE). The developed electrode showed excellent performance for rutin oxidation in a pH 3.0 Britton Robinson buffer (BR). In this media, linearly increasing anodic peak currents were observed with the concentration of rutin in two wide concentration ranges (i.e., 0.016–0.96 μM and 0.96‒19.73 μM), the sensor showed a low detection limit (i.e., 4.14 nM) (3 s m−1). It displayed good stability and selectivity. Also, it was used successfully for the determination of rutin in orange juice samples.

Perspective on Quantum Sensors from Basic Research to Commercial Applications

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This paper provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom–light interactions and discusses progress toward packaging those sensors into products. This paper covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors.

Bifunctional Pt-loaded steel slag matrix composites for the detection and degradation of tetracycline antibiotics

In this paper, a method for the simultaneous detection and degradation of tetracycline antibiotics (TCs) in water was investigated. Calcium silicate hydrate (CSH), FeO and calcium aluminate hydrate (CAH) were isolated from steel slag as carriers for Pt monomers. The produced Pt-modified modified steel slag (ALANH-Pt) possessed both peroxidase activity and photocatalytic properties. As a result, a sensitive and selective colorimetric sensor was exploited on the basis of ALANH-Pt for tetracycline (TC), oxytetracycline (OTC), and doxycycline (DOX), which exhibited a detection limit (LOD) of 1.696 μM, 0.999 μM and 3.607 μM, respectively. In addition, the degradation rate for TCs could be achieved 82 % within 60 min. The possible mechanisms of detection and degradation are discussed based on ESR spectroscopy, revealing the generation of hydroxyl radicals (OH), superoxide radicals (O2−) and holes (h+). The degradation pathway of TC was inferred by HPLC-MS. The selectivity of the colorimetric sensing platform and the application of the bifunctional ALANH-Pt to real water samples were investigated. This work provides a new idea that allows for the simultaneous detection and degradation of TCs, and offers a new approach to the utilization of steel slag.

Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise

Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise

Recent Advances in Synthesis and Applications of Organic Ionic Saltsbased Sensor Arrays

Abstract: Sensor arrays contain a group of sensors, improve observations with new dimensions, provide better estimations, and additional parameters in comparison to the individual selective sensor. The array-based sensing technique provides good performance to respond to various gaseous or liquid analytes. Room temperature ionic liquids (RTILs) (melting point <25oC) and Group of uniform materials based on organic salts (GUMBOS) (melting point =25-250oC) are organic ionic salts, composed of an oppositely charged pair of bulky organic cations and bulky organic/inorganic anion and shows interesting tunable physicochemical properties. In this review article, we will discuss the sensing performance of ILs- and GUMBOS-based sensor arrays. ILs-based sensor arrays have been used in electrochemical gas sensing, solvent discrimination, colorimetric gas sensing, sensing of organic compounds, optoelectronic sensing of vapors and solutions, and vapour sensing through IL/QCM systems. GUMBOS-based sensor arrays have been employed in vapour sensing through the GUMBOS/QCM method, detection, and discrimination of proteins.

A Comprehensive Review on : Aquaponic Farming Water Quality Prediction

Aquaponic farming, which combines aquaculture and hydroponics, depends strongly on maintaining optimal water quality to guarantee the health and productivity of both fish and plants. This review paper explores the latest developments in IoT-based automated water monitoring systems, focusing on their role in predicting and managing water quality in aquaponic systems. Regardless of significant progress there are several research gaps. Recent studies highlight challenges such as inconsistent sensor selection, calibration issues, insufficient publicly available data, and inadequate data cleaning and preprocessing. Also, the issues of imbalanced datasets, limited long-term data, and underdeveloped IoT and AI integration prevent the development of accurate predictive models. The scalability and maintenance of systems, understanding microbial dynamics, and nutrient management are also critical areas needing further exploration. This review also identifies the need for deeper case studies and advanced feature extraction methods to enhance prediction accuracy. By addressing these gaps, including system scalability and nutrient management, future research can improve data availability and quality, enabling more robust predictions and contributing to more efficient and sustainable aquaponic systems.

SnO2-Co3O4 nanocomposite sensor: Achieving ultra-selective hydrogen detection in mixed gas environments

This paper addresses the long-standing challenge of poor selectivity in MOS sensors towards gases with similar properties, exemplified by the difficulty in distinguishing between H2 and CO in mixed environments. In this paper, n-SnO2/p-Co3O4 nanocomposites have been prepared as sensing materials by a straightforward pyrolytic bimetallic organic framework method. Optimal hydrogen detection is achieved with a Sn: Co molar ratio of 1:0.15, demonstrating a high response (S=Ra/Rg=22.75 for 300 ppm H2) and exceptional anti-interference capability against CO (SH2/SCO=11.67) at 325°C. Furthermore, the incorporation of Extreme Learning Machine (ELM) model for predicting hydrogen concentration significantly reduces the interference effect common in mixed gases, achieving a high prediction accuracy a remarkably low relative error of just 0.6 %. The mechanism behind the enhanced sensitivity and selectivity of the sensor is elucidated in the paper, offering both theoretical insight and practical solution to the issue of selectivity in MOS sensors.

A hierarchical k-out-of-n optimization model for enhancing reliability of fire alarm systems

Fire alarm systems are crucial for mitigating the risks and damages associated with fires in process industries. These systems heavily depend on the performance of fire detection sensors to provide early detection and warnings, facilitating prompt evacuation and intervention measures. In order to improve the reliability of sensor systems typically requires the use of redundancy allocation techniques, such as a k-out-of-n configuration. In k-out-of-n configuration, the signals from all sensors are directed to a voter, which compares them and transmits a final signal once at least k signals are matched. However, challenges arise due to diverse failure modes with distinct impacts on the system. Integrating sensors through a single-layer k-out-of-n configuration, when there is only one voter, fails to fully highlight the potential advantages of redundancy in sensor systems. This paper addresses the limitations of the conventional configurations by introducing a hierarchical k-out-of-n system in which sensors can be integrated across multiple layers. In the k-out-of-n hierarchical system, sensors are organized into distinct groups, each employing its own k-out-of-n sub-system. Subsequently, the outputs of these sensor groups can be integrated with other groups through their respective voters in higher layers of the hierarchy. Ultimately, at the top layer, all the voters from the preceding layers must be connected to a top voter to transmit a final signal. Furthermore, an optimization model is developed to minimize the system cost while maximizing reliability, considering the best selection of sensors and the system configuration. The proposed optimization model takes into account a multi-sensor system with two competing failure modes and incorporates the probability of sensors' failure modes in the system. The hierarchical k-out-of-n system provides an opportunity to validate sensors by comparing them within their designated groups, allowing for the identification of the location of a failure and the planning of appropriate actions accordingly. The proposed optimization model is applied to a case of a fire detection system in storage warehouse to demonstrate its advantages over the conventional model.

Disposable and flexible screen-printed-carbon electrode modified with Au/poly-beta-cyclodextrin as electrochemical platform for estriol detection

In this work, a screen-printed carbon electrode in polyethylene (SPPE) modified with Au particles and poly-beta-cyclodextrin polymeric film (SPPE/Au/p-beta-CD) was develop as an electrochemical platform to detect estriol (E3) in environmental and biological samples. The stability, reproducibility and sensitivity of the electrode were confirmed via physicochemical and electrochemical characterization. Electrochemical tests demonstrated an increase in the SPPE electroactive area when 70% graphite ink was applied, and transmission electron microscopy revealed an increase in surface complexity compared with that of the unmodified electrode. The electrochemical impedance spectroscopy (EIS) to SPPE/Au/p-beta-CD data indicated a decrease and increase in the Rct (0.096 kΩ cm2) and constant phase element (CPE) (28.34 µF cm−2) values, respectively. The analytical curve was plotted using concentrations ranging from 2.0 to 48.5 µmol/L and the best results were obtained at pH 4.0; a limit of detection (LD) of 1.88 µmol/L was obtained. During a study of interferents, the platform exhibited high selectivity for E3. The SPPE/Au/p-beta-CD exhibited recovery percentages of E3 between 100.32–110.17% in river water, and female urine, saliva, and sweat. The SPPE/Au/p-beta-CD as electrochemical platform proved to be a reproducible, selective, and low-cost sensor for the detection of E3 in different complex matrices. The modification of SPPE with Au and the beta-CD polymer made the electrode more sensitive and the method used is viable for real application.

Selective trimethylamine sensors based on Co3O4 modified WO3 spheres

Co3O4 modified WO3 microspheres with excellent trimethylamine (TMA) gas sensitive performance were prepared by a simple hydrothermal method using ZIF-67 as a template. The morphology, elemental composition, structure of the compounds, and gas-sensitive properties of the sensors based on them were researched by SEM, XRD, XPS as well as the gas-sensing analysis system. The prepared Co3O4 modified WO3 spheres exhibit significantly enhanced TMA gas sensitive properties, such as high response (198) to 100 ppm TMA, excellent selectivity, rapid response/recovery rate (11.7/2.1 s), good repeatability, long-term stability, and low detection limit (0.17 ppm) at 285 ℃. The sensing properties of the WO3/Co3O4 sensor may be due to the large surface area, significant resistance modulation effect of the p-n heterostructure, and catalytic effect of Co3O4. This work suggests that Co3O4 modified WO3 spheres may play an important role in the field of high-performance TMA sensors.

A rapid response optical fiber sensor with highly selective probe for trace detection of mercury (II) ions

A highly sensitive, selective, and well-repeatable optical fiber sensor was proposed for trace measurement of Hg2+ concentration in the water environment based on Mach-Zehnder interference. The proposed structure was prepared by splicing a piece of thin core fiber (TCF) between multimode fibers (MMF) to form MMF-TCF-MMF. A novel sensitive film 3-mercaptopropyl-trimethoxysilane (MPTMS) modified polyvinyl alcohol (PVA) has been designed and synthesized, and uniformly coated on the surface of TCF. The specific combination of Hg2+ with PVA-MPTMS sensitive film can change the effective refractive index of the coating mode, enabling the measurement of Hg2+ concentration by tracking the interference spectra. The results show that the sensitivity of the PVA-MPTMS-coated sensor can achieve 17.5 nm/μM with a detection limit of 3.42 nM in the concentration range of 0–1000 nM. The sensor represents excellent repeatability and stability and a rapid response time of roughly 90 s. Meanwhile, the proposed sensor shows a specific recognition of Hg2+ and is unaffected by many other interfering metal ions in the water, which corresponds well with the binding energy calculated by the density functional theory (DFT). The sensor for Hg2+ detection exhibits compact structure, rapid response, high selectivity and repeatability, showing promising potential applications in the field of environmental monitoring.

Selective colorimetric sensors and diagnostic microfiber yarn for carbonyl groups via ion-pairing dyes with plural chromatic behaviors

An ideal chemo-responsive dye requires several essential features, including a facile preparation method, a broad range of dynamic color changes, and colorimetric responses to multiple stimuli. Herein, we present a simple and reliable procedure for synthesis of chemo-responsive dye from ion-pair formation of oppositely charged dyes. The ion-pairing dye exhibits various colorimetric responses to pH levels and amine concentrations, indicating that the dye has plural chromatic behaviors and wide visible spectrum: ‘halochromism’ and ‘solvatochromism’. Due to this, our colorimetric sensors can selectively identify various carbonyl groups such as ketone, aldehyde, and carboxylic acid, in which the detection limits range from a minimum of 0.019 to maximum of 2.506 ppm. Furthermore, the sensing array composed of a single ion-pairing dye enables the generation of easily identifiable color-patterns as unique fingerprints for each carbonyl groups across a range of gas levels. Based on these colorimetric responses to carbonyl groups, we develop dye-loaded microfiber yarn for biomarker diagnosis, which can check for the presence of acetone gas in the exhaled breaths and confirm whether an individual is in the optimal ketosis state or not.

Electrochemical Sensors for Antibiotic Detection: A Focused Review with a Brief Overview of Commercial Technologies.

Antimicrobial resistance (AMR) poses a significant threat to global health, powered by pathogens that become increasingly proficient at withstanding antibiotic treatments. This review introduces the factors contributing to antimicrobial resistance (AMR), highlighting the presence of antibiotics in different environmental and biological matrices as a significant contributor to the resistance. It emphasizes the urgent need for robust and effective detection methods to identify these substances and mitigate their impact on AMR. Traditional techniques, such as liquid chromatography-mass spectrometry (LC-MS) and immunoassays, are discussed alongside their limitations. The review underscores the emerging role of biosensors as promising alternatives for antibiotic detection, with a particular focus on electrochemical biosensors. Therefore, the manuscript extensively explores the principles and various types of electrochemical biosensors, elucidating their advantages, including high sensitivity, rapid response, and potential for point-of-care applications. Moreover, the manuscript investigates recent advances in materials used to fabricate electrochemical platforms for antibiotic detection, such as aptamers and molecularly imprinted polymers, highlighting their role in enhancing sensor performance and selectivity. This review culminates with an evaluation and summary of commercially available and spin-off sensors for antibiotic detection, emphasizing their versatility and portability. By explaining the landscape, role, and future outlook of electrochemical biosensors in antibiotic detection, this review provides insights into the ongoing efforts to combat the escalating threat of AMR effectively.

Machine learning-powered wearable interface for distinguishable and predictable sweat sensing

The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this issue, we have developed a highly selective non-enzymatic sweat sensor that seamlessly integrates with machine learning, ensuring reliable sensing and physiological monitoring of sweat biomarkers during exercise. The sensor consists of two electrodes supported by a microsystem that incorporates signal processing and wireless communication. The device generates four explainable features that can be used to accurately predict tyrosine and tryptophan concentrations, as well as sweat pH. The reliability of this device has been validated through rigorous statistical analysis, and its performance has been tested in subjects with and without supplemental amino acid intake during cycling trials. Notably, a robust linear relationship has been identified between tryptophan and tyrosine concentrations in the collected samples, irrespective of the pH dimension. This innovative sensing platform is highly portable and has significant potential to advance the biomedical applications of non-enzymatic sensors. It can markedly improve accuracy while decreasing costs.

Ultrasensitive electrochemical sensor based on dimethylglyoxime/carbon paste electrode modified with a bimetallic nanocomposite for nickel detection in environmental water samples

To date, research in the monitoring of heavy metal pollution focuses on developing an advanced and selective sensor. In this study, an electrochemical sensor-based bimetallic nanocomposite modified dimethylglyoxime carbon paste electrode (DMG/CPE) was created to detect Ni2+ selectively. The bimetallic nanocomposite was prepared by depositing a thin layer of silver nanoparticles (AgNPs) on the surface of the magnetite (Fe3O4)/DMG/CPE electrode. The incorporation of Fe3O4 and AgNPs results in a synergistic effect, increasing electron transfer and expanding the electroactive surface area (from 2.78 mm2 to 3.81 mm2), hence leading to an increase in the peak current response (ΔIp = 26.15 ± 0.33 µA at modified CPE). The prepared AgNPs/Fe3O4/DMG/CPE electrode demonstrated excellent properties, allowing for a broad linear dynamic range lying between 2–10 nM; 10–100 nM; and 100–1000 nM and a limit of detection value of 0.6 nM (S/N = 3). Additionally, the developed sensor showed great sensitivity, selectivity, and reliability in the detection of Ni2+ in river water samples, making it promising for a sensitive and selective detection of Ni-contaminated water.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Snowflake-like Ce-BTC@MoS2 heterojunction for high-performance uric acid detection

The analysis of uric acid (UA) in urine is adopted as a standard method for diagnosing related diseases, which provides an effective way for non-invasive detection of UA. However, no portable instruments presently can take an effective detection of UA in urine due to their detection limit, the indicator mainly impacted by sensitive materials. Hence, it is crucial to design electrochemical biosensor electrode materials with high sensitivity and the wide linear region for the detection of UA in urine. This research critically reveals a snowflake-like complex by simply combining the cerium-based metal-organic framework (Ce-BTC) and MoS2. The excellent electrochemical performance of Ce-BTC@MoS2 origins from the strong interfacial electronic interactions between Ce-BTC and MoS2 heterojunction and abundant active sites for UA oxidation. The Ce-BTC@MoS2 exhibits sensitivity (300.29 μA mM−1cm−2) for UA much higher than that of pristine Ce-BTC (129.14 μA mM−1cm−2) as well as MoS2 (209.71 μA mM−1cm−2) in the linear region from 5 to 2500 μM. The outstanding selectivity, excellent stability and attractive repeatability of the UA sensor fabricated by Ce-BTC@MoS2 have been also demonstrated, on account of the unique heterojunction of the electrode material. Additionally, the extensive linear region (0.5–4.4 mM) for the Ce-BTC@MoS2 sensor will be employed to analyze the concentration of UA in urine which can be directly measured without being processed. The prepared urine UA detector combined with the commercial blood UA detector provides an important basis for the comprehensive UA detection and clinical diagnosis.

Enhance electrochemical sensing of ascorbic acid and dopamine using V-CuO/GO nanocomposite

The electrochemical detection of Ascorbic acid and dopamine is crucial due to their vital roles in biological processes. Both biomolecules coexist in the human body and have the same oxidation potential, leading to electrode fouling and overlapping of oxidation potential. Accurate detection enables monitoring their levels in biological samples, aiding in disease diagnosis and treatment. This study presents the fast and green synthesis of a highly sensitive and selective electrochemical biosensor for the simultaneous detection of dopamine and ascorbic acid. The change in spherical cluster morphology into nanorods enhances the electrode's electroactive surface area, enhancing sensor sensitivity and selectivity. Electrochemical characterization techniques, including cyclic voltammetry and electrochemical impedance spectroscopy, demonstrated excellent electron transportation and low charge transfer resistance. The sensor exhibited simultaneous detection with a low limit of detection of 8 µM for dopamine and 6.9 µM for ascorbic acid, with a wide linear working range. Additionally, the sensor showed excellent selectivity and repeatability with a vast potential gap at simultaneous detection, making it suitable for clinical and food industry applications.

Highly selective localized surface plasmon resonance sensor for selenium diagnosis in selenium-rich soybeans

It is a challenge to determine selenium in acid aqueous for environmental monitoring and selenium-rich agricultural diagnosis. Herein, we developed a novel localized surface plasmon resonance (LSPR) sensor to detect Se(IV) ions based on the extraordinary laterals etching of gold nanorods (AuNRs). The etching started from the laterals in the low amount of Se(IV) ions, and accompanied by an apparent red shift of the longitudinal plasmon band (LPB), and then transformed to the tips etching with the upward of Se(IV) ions, the LPB band immediately shifted to the shorter wavelength. The red shift change (Δλ) of LPB band was utilized to quantitative analysis instead of blue shift or absorbance intensity, which gave a high selectivity for the proposed sensor. More importantly, this sensor could be performed in 0.1 mol/L of HCl solution, which achieved the seamlessly jointing with the pretreatment of complex samples, without time-consuming pH adjustment.Successful selenium detection was demonstrated in complex soybean samples that collected from the maturity after spraying organic chelated selenium at full flower period. The sensor provided a promising way to monitor and diagnose selenium in complex environmental samples and selenium-rich crops.

Development of a paper-based fluorescent carbon quantum dots MIPs sensor for selective detection of lumpy skin disease virus.

Lumpy skin disease (LSD) is a contagious viral disease caused by the Lumpy Skin Disease virus (LSDV), a member of the Capripoxviridae family. Traditional LSDV diagnostic procedures proved to have challenges in terms of cross reactivity as well as limited sensitivity and specificity. Herein, we combined molecularly imprinted polymers (MIPs) and quantum dots (QDs) technology to develop a paper-based turn on fluorescence sensor for rapid, sensitive and selective detection of LSDV. Under optimal conditions, the sensor showed linear enhancement in fluorescence intensity with the increase of LSDV concentration and exhibited a detection limit of 101 log10 TCID50 per ml. It also presented high specificity towards LSDV compared to other viruses viz sheep pox virus (SPV). Furthermore, the proposed sensor was successfully tested with spiked and real LSDV samples, proving its potential to serve as a sensitive selective sensor for LSDV diagnosis. Based on our knowledge, this is the first record of a paper-based diagnostic sensor for LSDV utilizing a CQDs-MIPs turn-on mechanism.