Enhancement Of Sensing Performance Research Articles

Simulation of electrostatic particulate matter sensor regeneration based on the particulate deposition patterns

Purpose This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field. Design/methodology/approach Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements. Findings The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles. Originality/value This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

Reviewing advances in nanophotonic biosensors.

Biosensing, a promising branch of exploiting nanophotonic devices, enables meticulous detection of subwavelength light, which helps to analyze and manipulate light-matter interaction. The improved sensitivity of recent high-quality nanophotonic biosensors has enabled enhanced bioanalytical precision in detection. Considering the potential of nanophotonics in biosensing, this article summarizes recent advances in fabricating nanophotonic and optical biosensors, focusing on their sensing function and capacity. We typically classify these types of biosensors into five categories: phase-driven, resonant dielectric nanostructures, plasmonic nanostructures, surface-enhanced spectroscopies, and evanescent-field, and review the importance of enhancing sensor performance and efficacy by addressing some major concerns in nanophotonic biosensing, such as overcoming the difficulties in controlling biological specimens and lowering their costs for ease of access. We also address the possibility of updating these technologies for immediate implementation and their impact on enhancing safety and health.

A high sensing performance piezoresistive sensor based on TPU/c-MWCNTs/ V2CTX-MXene electrospun film for human motion detection

Since wearable pressure sensors have promising applications in the fields of human physiological signal detection and bionic robots, there is an urgent need for wearable pressure sensors with comprehensive attributes such as high sensitivity and wide detection range. Here, we introduce a facile electrospinning process to fabricate a film composed of thermoplastic polyurethane/carboxyl multiwalled carbon nanotube/V2CTX-MXene (TPU/c-MWCNTs/V2C). This film has high sensitivity and great linearity, designed for flexible piezoresistive sensors. To further enhance sensor performance, we applied a spin-coating of polydimethylsiloxane (PDMS) on the sandpaper surface to create a top substrate with intermittent spinosum structures. Additionally, a layer of TPU fibers was electrospun between the conductive film and the interdigitated electrodes. The assembled components form a flexible piezoresistive sensor with a wide detection range of 0–200 kPa, a high sensitivity of 545.2 kPa−1, a low detection limit of 5 Pa, a short response time of 48 ms, and excellent long-term durability after 7000 loading/unloading cycles, making the piezoresistive sensor highly discriminating for detecting complex human movements. Thanks to the great sensing performance and stable structure, we conducted various demonstrations to capture human physiological signals, including signals of muscle activity and pulse. These findings indicate the potential of the designed flexible pressure sensor for applications in electronic skin and smart devices.

Fabrication of Nanobioengineered Interfaces Utilizing Quaternary Nanocomposite for Highly Efficient and Selective Electrochemical Biosensing of Urea.

Nanobioengineered interfaces have gained attention owing to their small size and high surface area-to-volume ratio for utilization as a platform for highly selective and sensitive biosensing applications owing to the integration of biological molecules with engineered nanomaterials/nanocomposites. In this work, a novel Ag-complex, [(PPh3)2Ag(SCOf)]-based quaternary Ag-S-Zn-O nanocomposites (NCs), was synthesized through an environmentally-friendly process. The results revealed the formation of the NCs with an average crystallite size and particle size of 36.08 and 40.22 nm, respectively. In addition, this is the first study to utilize such NCs synthesized via a single-source precursor method, offering enhanced sensor performance due to their unique structural properties. Further, these NCs were used to fabricate a urease (Ur)/Ag-S-Zn-O NCs/ITO nanobioengineered electrode for precise and sensitive electrochemical biosensing of urea. The interfacial kinetic studies revealed quasi-reversible processes with high electron transfer rates and linear current responses, indicating efficient reaction dynamics. A high diffusion coefficient and low surface concentration suggested a fast diffusion-controlled process, affirming the electrode's potential for rapid and sensitive urea detection. The biosensor demonstrated notable sensing properties such as high sensitivity (12.56 μA mM-1 cm-2) and a low detection limit (0.54 mM). The fabricated bioelectrode was highly selective and reproducible and demonstrated stability for up to 60 days. These results validate the potential of this nanobioengineered interface for next-generation biosensing applications, paving the way for advanced point-of-care diagnostics and real-time health monitoring.

Screen-Printed Electrodes as Low-Cost Sensors for Breast Cancer Biomarker Detection.

This review explores the emerging role of screen-printed electrodes (SPEs) in the detection of breast cancer biomarkers. We discuss the fundamental principles and fabrication techniques of SPEs, highlighting their adaptability and cost-effectiveness. The review examines various modification strategies, including nanomaterial incorporation, polymer coatings, and biomolecule immobilization, which enhance sensor performance. We analyze the application of SPEs in detecting protein, genetic, and metabolite biomarkers associated with breast cancer, presenting recent advancements and innovative approaches. The integration of SPEs with microfluidic systems and their potential in wearable devices for continuous monitoring are explored. While emphasizing the promising aspects of SPE-based biosensors, we also address current challenges in sensitivity, specificity, and real-world applicability. The review concludes by discussing future perspectives, including the potential for early screening and therapy monitoring, and the steps required for clinical implementation. This comprehensive overview aims to stimulate further research and development in SPE-based biosensors for improved breast cancer management.

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.

Evaluating the efficacy of machine learning in calibrating low-cost sensors

Ambient air quality monitoring requires low-cost environmental sensor devices that are affordable and feasible for large-scale implementation. However, issues such as sensor drift, environmental sensitivity, and inter-sensor variability affect data accuracy and cannot be adequately addressed by traditional calibration methods. This paper summarizes the use of machine learning techniques for calibrating low-cost sensors. The literature review shows that machine learning models like Random Forest, Support Vector Regression, and Neural Networks significantly improve sensor accuracy and reliability. For instance, Random Forest models reduced the root mean squared error by 30% for PM2.5 measurements, while Neural Networks achieved an R value of 0.997 for methane sensors. Integrating machine learning with IoT and mobile technologies enhances real-time monitoring and spatial resolution. Identified gaps include the quality of training datasets, managing environmental variability, and improving model transferability across different contexts. Addressing these gaps through advanced models and real-time calibration methodologies will further enhance sensor performance, ensuring more precise and reliable environmental data.

Chemiresistive NO2 sensor: A comparative study of rGO/MCPTPP and rGO/TPTP/MCPTPP composite

This study presents a chemiresistive nitrogen dioxide (NO2) sensor based on reduced graphene oxide (rGO) combined with 5, 10, 15, 20-Tetra-p-tolyl-21 H, 23 H-porphine (TPTP) and 5-(4-Methoxycarbonylphenyl)-10,15,20-triphenyl-21 H,23 H-porphine (MCPTPP). Structural, spectroscopic, morphological, and electrical characterizations of rGO/MCPTPP and rGO/TPTP/MCPTPP were carried out using X-ray diffraction, FTIR, AFM, SEM and current-voltage (I-V) respectively. The flexible ITO-coated electrodes were used to investigate sensing properties. The rGO/TPTP/MCPTPP-based sensor exhibited exceptional selectivity, sensitivity, and reproducibility toward NO2 analytes detection. MCPTPP and TPTP enhanced sensor performance due to their unique interactions with NO2. The rGO/TPTP/MCPTPP sensor exhibited remarkable linearity with a wide dynamic range of 400–100 ppm at (room temperature) RT and remained linear for low concentrations, specifically 35–1 ppm. The sensors also displayed excellent reproducibility over multiple sensing cycles. The superior performance of these sensors makes them promising candidates for various environmental monitoring and industrial applications requiring precise and reliable NO2 detection.

The Tunable Parameters of Graphene-Based Biosensors.

Graphene-based surface plasmon resonance (SPR) biosensors have emerged as a promising technology for the highly sensitive and accurate detection of biomolecules. This study presents a comprehensive theoretical analysis of graphene-based SPR biosensors, focusing on configurations with single and bimetallic metallic layers. In this study, we investigated the impact of various metallic substrates, including gold and silver, and the number of graphene layers on key performance metrics: sensitivity of detection, detection accuracy, and quality factor. Our findings reveal that configurations with graphene first supported on gold exhibit superior performance, with sensitivity of detection enhancements up to 30% for ten graphene layers. In contrast, silver-supported configurations, while demonstrating high sensitivity, face challenges in maintaining detection accuracy. Additionally, reducing the thickness of metallic layers by 30% optimizes light coupling and enhances sensor performance. These insights highlight the significant potential of graphene-based SPR biosensors in achieving high sensitivity of detection and reliability, paving the way for their application in diverse biosensing technologies. Our findings pretend to motivate future research focusing on optimizing metallic layer thickness, improving the stability of silver-supported configurations, and experimentally validating the theoretical findings to further advance the development of high-performance SPR biosensors.

A Review on Small Molecule Based Fluorescence Chemosensors for Bioimaging Applications.

This review provides a thorough examination of small molecule-based fluorescence chemosensors tailored for bioimaging applications, showcasing their unique ability to visualize biological processes with exceptional sensitivity and selectivity. It explores recent advancements, methodologies, and applications in this domain, focusing on various designs rooted in anthracene, benzothiazole, naphthalene, quinoline, and Schiff base. Structural modifications and molecular engineering strategies are emphasized for enhancing sensor performance, including heightened sensitivity, selectivity, and biocompatibility. Additionally, the review offers valuable insights into the ongoing development and utilization of these chemosensors, addressing current challenges and charting future directions in this rapidly evolving field.

Nanostructured transition metal dichalcogenides-based colorimetric sensors: Synthesis, characterization, and emerging applications.

Nanostructured transition metal dichalcogenides (TMDCs) have garnered significant attention as prospective materials for the development of highly sensitive and versatile colorimetric sensors. This work explores the synthesis, characterization, and emerging applications of TMDC-based sensors, focusing on their unique structural aspects and inherent properties. The synthesis methods involve tailored fabrication techniques, such as chemical vapor deposition and hydrothermal processes, aimed at producing well-defined nanostructures that enhance sensor performance. Characterization techniques, including microscopy, spectroscopy, and surface analysis, are employed to elucidate the structural and chemical features of the nanostructured TMDCs. These analyses provide insights into the correlation between the material's morphology and its sensing capabilities. The colorimetric sensing mechanism relies on the modulation of optical properties in response to specific analytes, enabling rapid and visual detection. The emerging applications of TMDC-based colorimetric sensors span diverse fields, including environmental monitoring, healthcare, and industrial processes. The sensors exhibit high sensitivity, selectivity, and real-time response, making them ideal candidates for detecting various target analytes. Furthermore, their integration with complementary technologies such as microfluidics, can facilitate the development of on-site and point-of-care applications. This work highlights the interdisciplinary significance of nanostructured TMDC-based colorimetric sensors and underscores their potential contributions to addressing contemporary challenges in sensing technology.

Nanoplasmonic sensor optimization via digital imaging analysis and antibody evolution

Localized surface plasmon resonance (LSPR) biosensors have emerged as powerful analytical tools for detecting biomolecules like proteins, DNA, and RNA across biomedical research and diagnostics, owing to their exquisite sensitivity. While optimizing nanostructures, surface functionalization, and optics have enhanced sensor performance, LSPR imaging facilitates multi-parametric quantitative analysis by acquiring supplementary spatial, chromatic, and luminance data, improving detection precision and sensitivity. Here, we introduce an innovative integrated multi-molecular dynamics (MD) simulation approach to optimize surface functionalization, circumventing limitations of singular MD methods that may ignore system complexities. Exemplifying this strategy through affinity maturation of a SARS-CoV-2 spike protein-targeting human neutralizing antibody (hNAb), we employed a comprehensive MD simulation suite to engineer an hNAb variant (MT2) exhibiting significantly augmented binding affinity (KD = 9.2 nM) compared to wild-type (20.6 nM). Consequently, MT2 enabled more sensitive SARS-CoV-2 pseudovirion detection, limits of detection (LOD) is 2524 vp/mL (wild-type LOD = 4039 vp/mL). Furthermore, our multi-parametric hue-based analysis, leveraging chromatic variations in microscopic images, optimized the LSPR optical system, demonstrating heightened sensitivity in detecting pseudovirions (765 vp/mL) and spike protein (64.25 ng/mL).

Nanofibrous Conductive Sensor for Limonene: One-Step Synthesis via Electrospinning and Molecular Imprinting.

Detecting volatile organic compounds (VOCs) emitted from different plant species and their organs can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multiwalled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule, using an electrospinning technique. The functional cavities were fixed using the UV curing method, followed by a target molecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly responded to S(-)-limonene, reaching a plateau within 200 s. Enhancing fiber density improved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarkably, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared with other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalool) with similar structures. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environmental monitoring.

Design and Optimization of Mechano transduction Sensors for effective analysis of proteins with Robotic interference

Méchano transduction sensors convert mechanical inputs into electrical signals, allowing robots to detect and interact with their environments in various bio-imaging applications. Current bio-sensor systems have some drawbacks that prevent them from being widely used in robotic applications. These include low sensitivity, short durability, and expensive manufacturing costs regarding picture prediction and categorization. To overcome these obstacles and improve robotic mechanotransduction sensors for efficient protein analysis, this work suggests Unique Sensor Fabrication Techniques (USFT). These methods enhance sensor performance in protein analysis while keeping costs low and scalability high via integrating complex micro- and nano-scale materials and architectures. In comparison to traditional sensor designs, comprehensive evaluations based on predefined parametric criteria show significant improvements in areas such as sensitivity, response time, and predictability in protein biomolecules. In addition, assessments of scalability and manufacturability point to the possibility of widespread use in robotic systems for protein categorization and prediction. In bioimaging applications, this study helps advance sensor technology for reliable and efficient robot-environment interaction.

Improved ammonia gas sensing performance at room temperature of polypyrrole by polyoxometalate decoration

Here, we introduced polyoxometalate (POMs) into polypyrrole (PPy) gas sensitive material for the first time to restrain the electron-hole recombination process and enhance the gas sensing performances. At room temperature, the sensing response value of PPy/POMs composite nanoparticles to 100 ppm NH3 gas can be improved to 80.85 %, 2.99 times than that of pure PPy nanoparticles. PPy/3 % SiW12 sensor response-recovery time was 39 s/166 s. It had good linearity (R2 = 0.9958), the LOD of NH3 was 85 ppb. In 21 days, the sensor still maintained over 90 % of the initial ammonia response value. Selectivity, repeatability and other sensing parameters were systematically investigated. In addition, the mechanism of sensing performance enhancement was analyzed and discussed. The study proved that POMs can separate electrons and holes, finally improved gas sensing properties. This work provides a new idea for the design and construction of high sensitivity PPy-based gas sensor by introducting polyoxometalates.

MOF-derived metal oxide (Cu, Ni, Zn) gas sensors with excellent selectivity towards H2S, CO and H2 gases

Metal-organic framework (MOF)-derived metal oxides blend the sensing properties of metal oxides with MOF porosity, enhancing gas sensing capabilities. In this study, M-MOFs (M = Cu, Ni and Zn) were synthesized and then calcined at different temperatures to obtain their corresponding metal oxides (CuO, NiO and ZnO). The synthesis method incorporated novel approaches to enhance sensor performance, such as optimizing calcination temperatures for improved selectivity. Structural and morphological analyses confirmed the high surface area and porosity of the metal oxide materials, facilitating efficient gas adsorption and promoting enhanced sensor response. Gas sensing studies revealed significantly enhanced performance of MOF-derived metal oxides over M-MOFs, strongly influenced by calcination temperature. Moreover, CuO, NiO and ZnO MOF-derived metal oxides showed improved selectivity towards H2S, CO and H2 gases, respectively. This study demonstrates that tuning MOF and calcination parameters can tailor sensor selectivity effectively.

Wearable Devices for Respiratory Monitoring

AbstractRespiratory diseases are currently monitored through traditional pulmonary function tests, such as spirometry. However, the restrictions of these procedures, particularly in the context of the COVID‐19 pandemic, have underscored the need for alternative approaches to respiratory health assessment. Wearable devices have emerged as a promising solution, providing continuous data collection, and overcoming the limitations posed by conventional methods. This review explores the multifaceted field of wearable devices for respiratory monitoring, presenting the most common sensing technologies applied to pulmonary ventilation, their constituent materials, fabrication techniques, and diverse morphologies to enhance sensor performance. The role of machine learning algorithms and ethical data sharing is highlighted, contributing to the forthcoming patient‐centered healthcare landscape. Ultimately, the importance of validation and calibration protocols for wearable devices is underlined. In anticipation of evolving healthcare needs, this in‐depth study addresses the current challenges in wearable respiratory monitoring while laying a robust foundation for a personalized, connected, and ethically sound future for respiratory care.

Non-Invasive Multi-Gas Detection Enabled by Cu-CuO/PEDOT Microneedle Sensor.

Metal-oxide-based gas sensors are extensively utilized across various domains due to their cost-effectiveness, facile fabrication, and compatibility with microelectronic technologies. The copper (Cu)-based multifunctional polymer-enhanced sensor (CuMPES) represents a notably tailored design for non-invasive environmental monitoring, particularly for detecting diverse gases with a low concentration. In this investigation, the Cu-CuO/PEDOT nanocomposite was synthesized via a straightforward chemical oxidation and vapor-phase polymerization. Comprehensive characterizations employing X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and micro Raman elucidated the composition, morphology, and crystal structure of this nanocomposite. Gas-sensing assessments of this CuMPES based on Cu-CuO/PEDOT revealed that the response current of the microneedle-type CuMPES surpassed that of the pure Cu microsensor by nearly threefold. The electrical conductivity and surface reactivity are enhanced by poly (3,4-ethylenedioxythiophene) (PEDOT) polymerized on the CuO-coated surface, resulting in an enhanced sensor performance with an ultra-fast response/recovery of 0.3/0.5 s.

Controlled Insertion of Silver Nanoparticles in LbL Nanostructures: Fine-Tuning the Sensing Units of an Impedimetric E-Tongue

Silver nanoparticles (AgNPs) possess unique characteristics ideal for enhancing device sensitivity, primarily due to their high surface-to-volume ratio facilitating heightened interaction with analytes. Integrating AgNPs into polymers or carbon-based materials results in nanocomposites with synergistic properties, enabling the detection of minute changes in the environment across various applications. In this study, we investigate the adsorption kinetics of AgNPs within multilayered layer-by-layer (LbL) structures, specifically examining the impact of AgNPs concentration in the LbL film formation that is further explored as sensing units in an impedimetric microfluidic e-tongue. Although absorption kinetic studies are infrequent, they are crucial to optimize the AgNPs adsorption and distribution within LbL structures, significantly influencing upcoming applications. Through systematic variation of AgNPs concentration within identical LbL architectures, we applied the films as sensing units in a microfluidic e-tongue capable of distinguishing food enhancers sharing the umami taste profile. Across all tested scenarios, our approach consistently achieves robust sample separation, evidenced by silhouette coefficient, principal component analyses, and long-term stability. This work contributes to exploring controlled nanomaterial-based developments, emphasizing the importance of precise parameter control for enhanced sensor performance across diverse analytical applications.

Grain boundary-induced drastic sensing performance enhancement of Fe2O3 gas sensors for acetone

Grain boundary-induced drastic sensing performance enhancement of Fe2O3 gas sensors for acetone