Purpose This paper aims to address the critical transition of surface acoustic wave (SAW) microfluidics from microscale to nanoscale precision manipulation by deriving a systematic framework from a critical review of device performance data to analyze the inherent engineering constraints among resolution, throughput and biocompatibility, where resolution and throughput form the primary conflicting objectives, and biocompatibility serves as the power-density-defined feasibility boundary. Design/methodology/approach The review introduces a “Resolution-Throughput-Biocompatibility” triangular analysis framework. Guided by this framework, it methodically examines the working mechanisms and performance limitations of various SAW devices developed over the past fifteen years, considering two key dimensions: acoustic field modes and device configurations. Findings The analysis reveals that different device architectures occupy distinct positions within the triangular framework, each emphasizing specific trade-offs. The findings highlight the need for future integration of multi-physical field synergy, intelligent algorithmic control, and modular manufacturing processes to overcome bottlenecks in nanoscale manipulation, throughput and system stability. Originality/value This paper provides a novel triangular framework that systematically compares SAW device designs, offering a comprehensive perspective on the trade-offs between resolution, throughput and biocompatibility. It also outlines future directions integrating advanced control and manufacturing strategies, paving the way for practical applications in point-of-care diagnostics and online industrial monitoring.
Purpose This paper aims to explore the role of plant growth monitoring in precision agriculture, ecological environment protection and urban greening, this study analyzes the applications, challenges and future development directions of wearable flexible sensors for monitoring plant physiology and growth environments. Design/methodology/approach This review focuses on wearable flexible sensor technologies for plant physiology and environmental monitoring: optical sensors, flexible electronic sensors and chemical/biological sensors. The data were collected through Web of Science search, and then carefully manually organized, analyzed, and summarized. Findings Current challenges for wearable sensors in plant growth monitoring include insufficient long-term stability, environmental interference suppression and multi-sensor collaborative optimization. Originality/value Propose future development directions such as the development of new flexible materials, design of self-powered systems, solar energy harvesting sensors integrated with leaf veins to avoid shading critical photosynthetic tissues and AI-driven data intelligent analysis. These innovations, based on interdisciplinary integration, are expected to promote the development of precision agriculture and intelligent ecosystems.
Purpose This study aims to bridge this gap by examining electronic-nose research from a system-level perspective rather than treating sensor materials, signal degradation, data processing and learning models as isolated components. Although significant advances have been achieved in electronic-nose systems, their transition from laboratory research to reliable real-world deployment remains limited. Design/methodology/approach A systematic review of studies published between 2021 and 2025 is conducted and reorganized into a unified, deployment-oriented processing pipeline. The framework integrates sensor material design, drift formation and compensation, data representation, learning paradigms and implementation constraints, with explicit emphasis on cross-component interactions across the sensing-to-artificial intelligence (AI) continuum. Findings The analysis synthesizes recent advances in electronic nose technology and AI while identifying key unresolved challenges, including long-term sensor drift, limited data set representativeness, evaluation bias, weak model interpretability, poor cross-environment generalization and computational constraints in low-power deployment. These factors collectively constrain scalability, robustness and practical applicability. Originality/value Unlike existing surveys that primarily summarize individual subsystems, this review introduces an integrated system-level analytical framework that reveals structural bottlenecks across the full processing chain. It provides interdisciplinary research directions to support scalable, reliable and deployment-ready artificial olfaction systems.
Purpose This study aims to propose a "double closed-loop" control circuit to eliminate quadrature error based on micro electro mechanical system (MEMS) multi-ring disk gyroscope (MRDG).Design/methodology/approach First of all, this article analyzes the causes of quadrature error in the MEMS MRDG and the output signal of the MEMS MRDG. Special attention is paid to the quadrature signals generated due to stiffness coupling in the MEMS MRDG, and a "double closed-loop" control circuit mode is adopted to simulate, calibrate and test the quadrature signals. Finally, detailed analysis and simulation were conducted using the established system model, and relevant measurement and control circuits were constructed for experimental study.Findings The experimental results show that when using the "double closed-loop" control circuit for the MEMS gyroscope prototype testing, the scale factor of the "double closed-loop" control circuit is 0.5127/mV/(degrees/s), the Bias stability (BI) is 1.54 degrees/h and the operating bandwidth is approximately 18 Hz at a range of +/- 100 degrees/s.Originality/value This work presents a novel structure of MEMS MRDG and analyze quadrature error based on MEMS MRDG. A novel circuit structure of "double closed-loop" for quadrature error correction is proposed which can be widely applied in the field of the MEMS gyroscope.
Purpose This paper aims to review the technological developments, cutting-edge applications and future trends of flexible surface acoustic wave (SAW) sensors. Design/methodology/approach Through a systematic analysis of three mainstream technical routes – thinning of rigid materials, thin-film integration on flexible substrates and intrinsically flexible materials – along with their applications in wearable technology, robotic sensing, biochemical sensing and structural health monitoring, this review is conducted. Findings The study clarifies that the three technical routes involve distinct trade-offs in performance, flexibility and fabrication processes. Flexible SAW sensors developed based on these routes demonstrate unique advantages, such as wireless and passive operation and conformal attachment, across multiple emerging fields. Originality/value This paper systematically compares the three major technical routes for flexible SAW sensors, integrates their cross-domain applications and outlines future development directions in materials, structures and system integration.
Purpose This study aims to explore the primary acoustic sensing technologies in the field of hydrogen leak detection, focusing on analyzing their working principles and technological advancements. Design/methodology/approach Firstly, the typical sources of hydrogen leakage in hydrogen fuel cell vehicles (HFCVs) are categorized. Then, the acoustic characteristics of hydrogen leakage (e.g. frequency spectrum distribution and sound pressure variation) are analyzed in depth. Based on these characteristics, the major acoustic sensing technologies for hydrogen leakage detection are investigated. These include surface acoustic wave (SAW), ultrasonic, photoacoustic and artificial intelligence (AI) integrated acoustic sensing technologies, which are investigated and compared. Finally, the current challenges and future trends of acoustic sensing-bad hydrogen leakage detection are summarized. Findings Several major acoustic sensing technologies exhibit distinct characteristics in hydrogen leak detection: Ultrasonic sensing technology features non-contact operation, strong directionality and high environmental adaptability for detecting high-pressure hydrogen leaks. SAW sensing technology offers high sensitivity, strong anti-interference capabilities and passive wireless operation. Photoacoustic sensing technology exhibits high sensitivity and selectivity, making it suitable for precise detection of low-concentration gases. Concurrently, the integration of AI significantly enhances the interference resistance and detection accuracy of acoustic sensing systems in complex automotive environments. Despite playing a vital role in hydrogen leak detection, acoustic sensing technologies still face challenges, such as environmental interference, false alarms and the complexity of leak sound characteristics. Originality/value This study presents a systematic analytical framework for HFCV hydrogen leakage detection based on acoustic sensing. This framework integrates leakage source analysis, acoustic characteristic exploration and investigation of multi-type acoustic sensing technologies. It presents AI-integrated acoustic sensing will be the main future trends in detecting hydrogen leakage detection technologies, especially in complex automotive scenarios.
Purpose In steel mill slag road transportation, accurate control of vehicle loading volume is critical to reducing road and bridge damage. However, traditional detection methods suffer from high cost, low efficiency and insufficient accuracy. This paper aims to propose an effective and high-precision method for loading volume detection of slag-dedicated transport vehicles.Design/methodology/approach A LiDAR point cloud-based detection method is proposed for slag transport vehicles. First, a loading volume calculation model is established using real 3D LiDAR scan data. A denoising algorithm combining local outlier factor with KD-trees is adopted to suppress dust and equipment interference. Second, a label-connected domain clustering algorithm and statistical filtering are used to segment the cab and cargo compartments. A spatial slicing method is applied to extract the loaded material point cloud. Finally, loading quality indicators and overloading judgment criteria are constructed, and high-precision volume detection is realized via triangular mesh visualization.Findings Experiments are conducted on 20, 24 and 45 m & sup3; dedicated transport vehicles under various working conditions. The average relative error of the proposed method is less than 2%, indicating high measurement accuracy.Originality/value This work introduces a complete LiDAR point cloud-based framework for volume detection of slag-dedicated transport vehicles in steel mills. The method is robust to complex on-site interferences and applicable to different vehicle sizes, showing good universality for industrial slag transportation scenarios.
Purpose This study aims to explore laser-based spectroscopic “fingerprinting” techniques for monitoring water quality in real time. Traditional laboratory-based analysis is frequently too slow to efficiently reduce the growing threat of chemical and biological contamination to global water resources. To establish an authoritative technical reference for developing next-generation, field-deployable sensing solutions, this review aims to assess the fundamental physical principles, operational features and practical applicability of emerging laser technologies, such as laser-induced breakdown spectroscopy (LIBS), laser-induced fluorescence (LIF), Raman/surface-enhanced Raman scattering (SERS), Fourier transform infrared spectroscopy and terahertz (THz) spectroscopy. Design/methodology/approach The paper follows a comparative review approach, starting with an analysis of laser-matter interactions in aquatic settings. It provides a structured review of five main spectroscopic techniques, evaluating their performance on sensitivity, selectivity and robustness. The methodology incorporates a cross-section of current advances (2020–2025) in nano-enhanced substrates, lab-on-a-chip integration and the role of data-driven frameworks [artificial intelligence (AI)/machine learning] in automating the identification of complicated chemical fingerprints in different water matrices. Findings The review concludes that, whereas LIBS and LIF succeed in detecting elements and aromatics, Raman/SERS and THz spectroscopy provide higher molecular specificity for pathogens and trace organics. The most recent development is the move to “intelligent” sensors that use graphene-based metasurfaces and photonic crystal fibers to solve water’s infrared absorption problems. The study finds that the incorporation of AI-driven chemometrics is critical for the shift from binary detection to autonomous “fingerprinting” and quantification in diverse environmental settings. Originality/value This research offers a novel design perspective by bridging the gap between fundamental optics and autonomous environmental monitoring. It provides value to researchers and practitioners by combining the most recent “nano-to-macro” sensing trends into a unified technical framework. Unlike previous studies, it focuses on the “spectroscopic fingerprint” a contaminant’s unique identifier, as the foundation for the future generation of intelligent, autonomous water quality regulation systems.
Purpose The purpose of this study is to systematically examine advancements in microelectromechanical system (MEMS) oscillators, with a focus on their role as not only timing references but also high-sensitivity sensing platforms. This study highlights how frequency stability underpins their performance in applications such as environmental monitoring, biomedical sensing and industrial automation. Design/methodology/approach Adopting a sensor-oriented perspective, this review analyzes frequency stabilization techniques across resonator design, interface electronics and system integration. This paper covers sensor-relevant aspects including materials (e.g. AlN and ScAlN), transduction mechanisms (piezoelectric and capacitive), low-noise readout circuits, temperature compensation methods and nonlinear dynamic behavior. Emphasis is placed on implementations suitable for physical, chemical, biological and temperature sensors. Findings MEMS oscillators have evolved into dual-functional platforms for both timing and sensing. Innovations in resonator design – such as high-Q bulk acoustic modes and phononic structures – coupled with active compensation circuits (e.g. TIA-based interfaces and TDS-PLLs) enable frequency stabilities below ± 1 ppm and ultra-fine resolution in temperature, pressure and mass sensing. Nonlinear phenomena, including parametric resonance and synchronization, further enhance stability and enable novel sensing modalities. Originality/value This paper uniquely bridges MEMS oscillator stability with broader sensor applications, offering a unified review of resonator physics, circuit interfacing and system integration from a sensing perspective. This study identifies emerging trends such as multimode resonant sensing, oscillator-based sensor networks and nonlinear dynamic sensing, providing a forward-looking resource for researchers developing next-generation intelligent sensor systems.
Purpose The purpose of this study is to review the development and potential clinical application of psychiatric biosensors for monitoring major psychiatric disorders. These conditions contribute substantially to the global disease burden, yet their diagnosis and follow-up largely rely on subjective clinical assessments. This review evaluates how wearable and implantable biosensors detecting physiological and biochemical biomarkers, including cortisol, dopamine and serotonin, may support objective and continuous monitoring in psychiatric care. Design/methodology/approach Relevant literature was collected through database searches and manual screening of peer-reviewed publications on psychiatric biosensors and wearable sensing technologies. The review analyses recent advancements in electrochemical, optical and hybrid biosensor platforms capable of detecting biomarkers from biofluids such as sweat, saliva, blood and urine. It also examines their integration into point-of-care systems and evaluates regulatory approval pathways in the USA and Europe. Findings Psychiatric biosensors show strong potential for real-time, noninvasive monitoring of physiological indicators related to mental health disorders. Advances in wearable integration and multimodal biomarker detection have improved feasibility for continuous monitoring. However, challenges remain in biomarker validation, clinical reliability and regulatory approval under frameworks such as the Medical Device Regulation (MDR) 2017 / 745 and pathways of the US Food and Drug Administration. Originality/value This review highlights emerging psychiatric biosensor technologies, regulatory considerations and future research directions needed to enable their safe integration into mental healthcare.
Purpose The core foundation of soft robots is comprising of flexible valves, pumps, sensors and actuators, which collectively determine the drive mechanisms, energy transfer efficiency, environmental perception capabilities and system reliability of soft robots. Recent progress in soft robotics research has yielded novel discoveries pertaining to pivotal functional components, including flexible valves, pumps, sensors and actuators. Nevertheless, systematic reviews that comprehensively summarize this field remain relatively scarce. In light of the aforementioned context, this paper synthesizes and compares the working principles, performance metrics and technological approaches of these four flexible functional components, based on relevant research outcomes. The purpose of this study is to provide a reference for future studies. Design/methodology/approach In this paper, the components of soft robots are divided into four categories: flexible control function components, flexible driving function components, flexible execution function components and flexible sensing function components. For these four categories of functional components, the authors elaborate on their further classification and operating principles, while summarizing, comparing and analyzing representative research achievements from recent years. Findings Through a summary and analysis of current relevant research, this study points that integrated design, self-perception-adaptive flexible systems and self-supply technology are poised to emerge as the next pivotal research frontiers for soft robots. Originality/value This paper systematically categorizes flexible functional components for soft robots and summarizes, analyzes and compares relevant research findings. The study aims to provide valuable reference for researchers in related fields.
Purpose Rapid and stable temperature control is essential for efficient polymerase chain reaction (PCR) amplification. Thermoelectric cooler (TEC)-based PCR thermal cycling systems are widely used in portable and point-of-care devices because of their bidirectional thermal control and compact structure. This study aims to summarize temperature sensing and closed-loop control technologies in TEC-PCR systems. Design/methodology/approach This paper examines thermal inertia, nonlinearity, indirect temperature measurement and structural coupling under rapid thermal cycling, based on thermodynamic analysis and literature review. It compares the dynamic characteristics and uncertainty sources of different layout designs and temperature sensing strategies. Considering model dependency, computational complexity and robustness, it systematically evaluates classical proportional integral derivative (PID), segmented PID, fuzzy control, feedforward–feedback control, model predictive control and data-driven methods and discusses their engineering applicability and limitations in portable TEC-PCR systems. Findings The findings show that thermal coupling pathways and sensor placement have a significant impact on closed-loop stability and temperature measurement accuracy. An ideal balance between control performance, implementation complexity and reliability is reached by the enhanced PID control. Although data-driven and model predictive approaches have the potential to handle nonlinearities, they are nonetheless limited by processing power and model generalization skills in portable systems. Heating/cooling rates, temperature consistency and system energy efficiency are all fundamentally supported by the thermal management framework. Originality/value By integrating temperature sensing, closed-loop control and thermal management into a unified framework, this review provides a systematic perspective on TEC-PCR thermal cycling technologies, offering valuable guidance for the design and optimization of portable nucleic acid amplification systems.
Purpose Methamphetamine (METH), a methylated derivative of amphetamine, is a potent central nervous system stimulant with high abuse potential and major implications for forensic science and public health. This study aims to critically evaluate recent advances in nanomaterial-based sensing platforms for METH detection and to assess their translational relevance to forensic toxicology practice. Design/methodology/approach The review systematically surveys peer-reviewed literature on nanomaterial-enabled METH sensors, with particular emphasis on work published from 2022 onward. Key classes of nanomaterials (gold and silver nanoparticles, carbon nanotubes, graphene derivatives, quantum dots and metal–organic frameworks) and the main transduction modes (electrochemical and optical detection techniques, including surface-enhanced Raman spectroscopy and fluorescence-based methods) are discussed. For each platform, the authors summarize sensing mechanisms, analytical performance, sample matrices and where possible, compare reported limits of detection with forensically relevant concentration ranges and regulatory cut-offs. Findings Nanomaterials consistently enhance analytical performance by lowering limits of detection, increasing sensitivity and enabling miniaturized, portable and even wearable formats for presumptive METH screening. Nevertheless, no nanomaterial-based sensor has yet been incorporated into international organization for standardization/international electrotechnical commission 17025-accredited forensic workflows, which remain dominated by immunoassay screening with gas chromatography-mass spectrometry/liquid chromatography-tandem mass spectrometry (LC-MS/MS) confirmation. Major barriers include nanomaterial stability, batch-to-batch reproducibility, high production costs, matrix interferences, toxicological concerns for certain materials (e.g. cadmium-based quantum dots) and incomplete validation against forensic concentration windows. Regulatory, ethical and legal issues, particularly around evidentiary acceptance and nanotoxicity, further constrain deployment. Originality/value Unlike prior reviews that focused primarily on nanobiosensor design, this paper integrates a forensic toxicology perspective by explicitly relating nanosensor performance to biological and legal thresholds, highlighting gaps with current accreditation and regulatory frameworks and discussing artificial intelligence-assisted analysis, wearable/remote sensing, nanozyme-based and field-deployable devices. The review provides a consolidated roadmap of technical, regulatory and ethical challenges that must be addressed to translate nanomaterial-based METH sensors from laboratory prototypes to reliable forensic tools.
PurposeBlood pressure monitoring is fundamental for cardiovascular health assessment; however, traditional cuff-based sphygmomanometers provide only intermittent measurements, cause user discomfort and cannot support continuous real-time monitoring. This review aims to systematically summarize recent advances in flexible blood pressure sensing technologies and to analyze the current challenges and future development directions of continuous noninvasive blood pressure monitoring systems. Design/methodology/approachThis review synthesizes recent research progress in five major categories of flexible blood pressure sensing technologies, including piezoresistive sensors, capacitive sensors, optical sensors, piezoelectric sensors and ultrasonic sensors. The sensing mechanisms, device structures, materials, performance characteristics and system integration approaches of these technologies are comparatively analyzed. In addition, key issues related to calibration methods, intelligent algorithms, hybrid sensing architectures and data interoperability are reviewed to evaluate the overall development of flexible blood pressure monitoring systems. FindingsFlexible blood pressure sensing technologies have achieved significant progress in sensitivity, response time, device miniaturization and wearable integration. Advanced materials such as MXene/black phosphorus composites, graphene layers, PVDF films and PZT-5H ceramics enable continuous, skin-conformal and user-friendly blood pressure monitoring. However, several challenges remain, including accuracy limitations, calibration burden, limited sample diversity in validation studies, susceptibility to environmental interference, and lack of unified data interoperability standards. Emerging solutions such as hybrid sensing architectures, adaptive calibration algorithms based on transfer learning, intelligent physiological modeling frameworks, and multidimensional standardization strategies show strong potential for improving monitoring accuracy and clinical applicability. Research limitations/implicationsCurrent technologies lack unified interoperability standards and validation in special populations (BMI > 30, age > 65). These limitations hinder clinical translation and require multidimensional standardization for broader application. Practical implicationsThe reviewed flexible sensing technologies enable low-cost, wearable continuous blood pressure monitoring, with huge commercial potential in consumer health electronics and remote patient monitoring devices. Social implicationsFacilitates early screening and real-time management of cardiovascular diseases, reduces the burden of hypertension-related complications, and promotes equitable access to personalized cardiovascular healthcare. Originality/valueThis review provides a comprehensive and systematic analysis of flexible blood pressure monitoring technologies from sensing mechanisms to system integration and clinical challenges. Unlike previous reviews that primarily focus on sensor materials or individual sensing technologies, this paper emphasizes hybrid sensing strategies, intelligent calibration algorithms, system interoperability and clinical validation challenges, offering a broader perspective on the future development of continuous, personalized blood pressure monitoring technologies.
Purpose This review aims to highlight the importance of acquiring continuous, in situ physiological information during plant growth through plant monitoring technologies. Compared with noncontact techniques such as optical imaging and remote sensing, wearable sensors offer superior temporal and spatial resolution. However, conventional rigid sensors often damage plant tissues. Flexible sensors, with their excellent flexibility, biocompatibility and interface adaptability, present a promising alternative. This study analyzes the applications, challenges and future directions of wearable flexible sensors in plant monitoring. Design/methodology/approach This review focuses on the key technologies involved in fabricating flexible wearable sensors, including sensor materials and structural design. It then discusses the monitoring roles of these devices in plant growth, covering the surveillance of microclimate, gases and growth processes. Particular attention is given to the working principles, performance, advantages and disadvantages of different sensor types. Data were collected via Web of Science and then manually organized, analyzed and summarized. Findings Current challenges for wearable sensors in plant growth monitoring include the damage caused by rigid sensors to plant tissues, as well as the need for improved long-term stability, better suppression of environmental interference, and enhanced multisensor collaborative optimization in flexible sensing systems. Originality/value This review proposes future development directions such as the development of novel materials and structures for plant sensors, improving the output performance of power supply components, and the integration of multiple parameter indicators. These innovations aim to enhance production efficiency and resource management in smart agriculture, thereby advancing precision agriculture and intelligent ecosystems.
PurposeThis study aims to develop a fully automatic method for more accurate and efficient deformation measurement of bridges from terrestrial laser scanning point clouds, overcoming the limitations of traditional labor-intensive inspection and manual point selection. Design/methodology/approachThe proposed deformation measurement method comprises intensity-augmented Gaussian filtering to suppress environmental noise, a new fast boundary extraction algorithm for improving robustness and speed, an exhaustive-search-based corner point detection method to ensure precise cross-sectional localization and (a normal-vector-guided automatic alignment extraction algorithm to reconstruct elevation profiles. FindingsThe proposed method was validated on a multispan continuous rigid-frame bridge. The cross-sectional dimensions of four piers were reconstructed with an average root mean square error of 3 cm. The deformation of the midspan was detected to be 6 mm, satisfying the serviceability limit-state deflection limit. Originality/valueThe proposed exhaustive-search-based feature corner point detection algorithm outperformed the conventional Hough transform algorithm. The proposed method demonstrated both high efficiency in alignment extraction and deformation assessment, contributing to intelligent inspection of in-service bridges.
Purpose This paper systematically reviews the past two decades of angular displacement sensor research, focusing on capacitive types. This paper aims to help readers grasp the classification, principles, performance and applications of angular displacement sensors, identify the technical challenges and the research gaps of capacitive sensors and provide references for sensor selection, optimization and innovation in scenarios like wearables and aerospace. Design/methodology/approach This paper first classifies angular displacement sensors into contact (e.g. potentiometer) and non-contact (e.g. capacitive, inductive and optical) types, outlining their core features. This paper then delves into capacitive sensors, explaining their flat capacitance principle, key issues (edge effect, parasitic capacitance and environmental sensitivity) and solutions (structural optimization and signal conditioning). This study reviews four typical capacitive sensor studies and compares performance parameters (resolution, precision, range and size) of various sensors via tables. Finally, this study summarizes applications in wearables, aerospace and engineering and discusses research gaps. Findings Capacitive angular displacement sensors stand out for their high precision/resolution, low power, simple structure and low cost, but face issues like edge effect and environmental sensitivity – mitigable via structural design, signal processing and environmental adaptation. They have achieved breakthroughs in miniaturization (less than 8 mm outer diameter), high precision (26-bit resolution) and multi-functionality (dual angular/linear measurement), with broad prospects in wearable monitoring and aerospace control. They balance performance and cost more effectively than inductive/optical sensors for industrial and wearable applications. Originality/value This paper uniquely focuses on in-depth analysis of capacitive sensors from principles to latest research. This paper constructs a performance comparison framework for angular displacement sensors, systematically summarizes wearable application details (e.g. hand/limb joint sensors) to fill existing gaps and identifies research gaps (precision, dynamic performance, fusion and miniaturization) and future directions (nanomaterials, three-dimensional printing and IoT integration) to guide innovative development.
PurposeNucleic acid amplification forms the core of molecular diagnostics, with its efficiency dependent upon rapid and precise temperature control. Sensor-based closed-loop control serves as a key approach, whilst microfluidic chips provide an ideal platform for efficient thermal regulation. This paper aims to review recent advances in microfluidic chip temperature control technologies and explores their application prospects and challenges in point-of-care testing. Design/methodology/approachThrough systematic analysis of thermal heating, optical heating, semiconductor thermoelectric cooling (TEC) and fluid-driven fixed-temperature-zone strategies, this study compares their performance in heating rates, temperature uniformity, and system integration. Application discussions are supplemented with recent research examples. FindingsDifferent temperature control methods possess distinct advantages: electrical heating offers simplicity but higher power consumption; optical heating provides rapid temperature rise yet is constrained by light source coupling; TEC enables bidirectional temperature regulation but requires complex heat dissipation design; while fixed-temperature-zone fluid-driven systems demonstrate outstanding performance in continuous-flow PCR. Originality/valueThis review paper assesses the efficacy, advantages, disadvantages and clinical feasibility of various temperature control solutions incorporating temperature sensors, based on the requirements of microfluidic nucleic acid amplification. It aims to provide design references for future high-efficiency molecular diagnostic systems.
PurposeThis paper aims to critically examine the validity of wrist-worn photoplethysmography (PPG) sensors in classifying exercise intensity zones in real time, with particular emphasis on the interplay between physiological, biomechanical and technological factors. This review addresses limitations in validity across different intensity domains and explores solutions integrating advanced analytics and signal processing. Design/methodology/approachOver 100 peer-reviewed sources were systematically reviewed to assess the validity and limitations of PPG sensors across treadmill, cycling and free-living conditions. The review dissects validity by intensity zones, explores inter-subject variability linked to skin pigmentation, BMI and age and evaluates algorithmic approaches from simple averaging filters to convolutional and recurrent neural networks. FindingsValidity of PPG sensors significantly declines with increasing exercise intensity, primarily due to motion artefacts, vasoconstriction and low signal-to-noise ratios. Variability across devices and user demographics remains a major challenge. Studies show that hybrid sensor configurations (e.g. IMU + PPG) and machine learning-based artefact rejection (CNN–LSTM) enhance signal fidelity. However, most commercial wearables still rely on proprietary heuristics, limiting transparency and reproducibility. Cross-validation against ECG gold standards reveals that mean absolute percentage error increases up to 18% in high-intensity domains, raising concerns for zone-based training prescriptions. Originality/valueThis review bridges engineering, physiology and data science by offering a comprehensive synthesis of the mechanisms, limitations and solutions associated with PPG-based HR sensing in sports. It critiques the sufficiency of existing validation frameworks and advocates for standardised benchmarking, federated data sets and interpretable AI to guide future innovation.
Purpose The purpose of this paper is to review recent advancements in reduced graphene oxide–zinc oxide (rGO–ZnO) hybrid nanostructure-based nitrogen dioxide (NO2) gas sensors, focusing on their potential for sensitive, real-time environmental monitoring and mitigation of health risks. Design/methodology/approach The method includes a litreature-based comparative analysis of recent studies on rGO–ZnO NO2 gas sensors, covering synthesis techniques, structural configurations, operating conditions and performance metrics such as response time, detection limit and recovery efficiency. Findings Hybrid rGO–ZnO sensors fabricated mainly through hydrothermal and chemical synthesis methods demonstrate high sensitivity, with room-temperature operation, rapid response times (seconds to minutes) and detection limits down to parts-per-billion. Key challenges include low recovery efficiency, humidity interference and substrate limitations. Originality/value This paper provides a cohesive evaluation of synthesis methods, performance characteristics and design strategies for rGO–ZnO NO2 gas sensors, highlighting both technological advancements and unresolved challenges, and suggesting future directions such as green synthesis, flexible substrates and Internet of Things-enabled applications.
