Purpose This study aims to evaluate the feasibility of using piezoelectric lead zirconate titanate (PZT) sensors to monitor the effects of adding low-density polyethylene (LDPE) beads, made from waste plastic, on carbonation mineralization of modified concrete during accelerated CO2 curing. Design/methodology/approach Two concrete mixtures (P20 and P40) were prepared by replacing 20% and 40% of coarse aggregates with LDPE beads. PZT patches installed in both surface-bonded and embedded configurations were employed to capture electromechanical impedance (EMI) responses experimentally, in the user-defined 250–350 kHz frequency ranges at ten different curing ages of 0, 1, 3, 7, 10, 14, 18, 21, 24 and 28 days from plastic waste admixed-concrete mixes, and were further validated using finite element analysis. Findings The results indicate that carbonation curing significantly influences the EMI responses with increasing plastic waste content in concrete. Embedded PZT sensors exhibited higher sensitivity than surface-bonded sensors, showing maximum root mean square deviation (RMSD) and mean absolute percentage deviation (MAPD) values of up to 39.23% and 75.90%, respectively, for P40 mixes at 28 days curing duration. In contrast, surface-bonded sensors demonstrated lower deviations, with maximum RMSD and MAPD values of 7.69% and 14.28%. Originality/value This study demonstrates that the inert solid plastic additions up to 40% significantly increase the sensitivity of health indices, conclusively showing an intensified carbonation effect due to increased porosity and weak interfaces, which are ultimately correlated to strength gains with higher confidence levels.

Purpose To address the challenge of detecting ultra-small leaks with weak signals in steel–plastic transition joints of polyethylene (PE) pipelines, this study proposes an intelligent classification method for natural gas micro-leaks based on acoustic emission (AE) multi-frequency domain features and random forest (RF) algorithms, enabling effective detection of such micro-leaks. Design/methodology/approach AE signals from pinhole leaks (aperture sizes: 0.1, 0.2 and 0.35 mm) under actual operating pressures (0.1, 0.2 and 0.3 MPa) were collected via AE sensors. After filtering and pre-processing the data set, it was divided into training and test sets in a 7:3 ratio. Features were then extracted from the time-domain, frequency-domain and time–frequency domain. A parameter grid search method was used to optimise the key parameters of the RF model, identifying the optimal combination. Findings The results show that the RF model can not only respond quickly and predict the leakage degree accurately but also the performance evaluation metrics (accuracy, precision, recall, and F1 score) all exceeded 90%, which can effectively identify the weak leakage signal and accurately distinguish the micro-leakage state under different working conditions. Originality/value The research proposes an intelligent classification method for micro-leakage of PE pipeline steel–plastic joints based on AE multi-domain features and RF. The core objective is to accurately and rapidly identify micro-leakage conditions of PE pipeline steel–plastic transition joints under various operating conditions, thereby achieving early warning for micro-leakages in natural gas pipelines.

Purpose Low-velocity impacts may cause barely visible internal damage in carbon fiber-reinforced polymer (CFRP) laminates, which can significantly reduce the load-carrying capacity of Type IV hydrogen storage cylinders. This study aims to propose an acoustic-emission (AE) sensing framework to quantitatively evaluate low-velocity impact damage in CFRP laminates relevant to Type IV cylinder structures, and to establish severity grading criteria based on an interpretable scalar indicator. Design/methodology/approach Drop-weight impact tests were conducted from 10–80 J while AE signals were continuously recorded. AE features were extracted using empirical mode decomposition (EMD) and principal component analysis (PCA). A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) classifier was trained to identify damage-related AE signals, and wavelet entropy was calculated to quantify impact damage severity. Nonlinear ultrasonic testing was additionally used to validate damage progression. Findings The CNN–LSTM model achieved 93.3% classification accuracy for AE signals associated with matrix cracking, delamination and fiber fracture. Wavelet entropy showed strong correlation with impact damage severity and identified three damage stages. Two critical thresholds were found: wavelet entropy 0.275 (transition to moderate damage) and 0.55 (transition to severe damage), corresponding to impact energies of 40 and 60 J, respectively. Originality/value This study links AE signal characteristics to impact damage severity through an interpretable scalar metric (wavelet entropy) and provides quantitative thresholds (0.275 and 0.55) for three-stage damage grading. It further combines EMD–PCA feature extraction with a CNN–LSTM classifier (93.3% accuracy) to identify damage-related AE signals, and validates damage progression using nonlinear ultrasonic testing. The framework offers a practical sensing strategy for composite pressure-vessel monitoring.

Purpose This paper aims to design and investigate a high-precision tunneling magnetoresistance (TMR) noncontact current sensor based on a dual-gap anti-interference structure, to address the susceptibility of traditional TMR current sensors to external interference in complex electromagnetic environments. Design/methodology/approach A systematic methodology integrating theoretical modeling, finite element simulation and experimental validation was used. A mathematical model of the dual-gap magnetic circuit was established based on magnetic Ohm’s law to analyze its differential output mechanism for suppressing common-mode magnetic field interference. Key parameters such as magnetic core cross-section and air gap dimensions (a = b = 7 mm) were optimized through finite element simulation, evaluating magnetic field uniformity and conductor displacement error. Thermal performance and anti-interference capability were experimentally validated under controlled conditions. Findings The optimized dual-gap structure improves magnetic field uniformity to 0.0010 and reduces measurement error caused by conductor displacement to below 0.08%. It also demonstrates superior thermal management, lowering temperature rise by 27.4% compared to a single-gap structure under high load. Under an external interference field of 10 Oe, the dual-gap sensor achieves an output voltage error of 0.014%, outperforming the single-gap design (0.017%). The sensor maintains a linearity of ± 0.5% and an accuracy of ± 1% across a temperature range of –50°C to 70°C. Originality/value This study proposes an innovative dual-gap magnetic circuit structure that effectively enhances anti-interference performance and thermal stability in TMR current sensors. The research provides a practical and reliable design pathway for developing high-precision, robust current sensors suitable for demanding electromagnetic environments.

Purpose A magnetic anomaly inversion algorithm based on second-order Euler deconvolution is proposed to reduce geomagnetic background field and time-varying interference. This study aims to investigate the effects of array baseline lengths, noise standard deviations and array geometry imperfections on the accuracy of two types of order gradient measurement and target localization. Design/methodology/approach A 13-scalar-magnetometer array with identical sensor configuration across three coordinate planes simultaneously measures first- and second-order derivatives of total-field magnetic anomalies. The structural index of magnetic sources is determined through nearest neighbor criterion and mean value statistics, while position coordinates are calculated using an iterative search algorithm featuring progressive radius reduction. Validation was conducted through numerical simulations on spherical and infinitely long horizontal cylindrical magnetic sources under the planned flight trajectory. Findings Baseline length significantly impacts gradient measurement errors, which mostly increase with increasing baseline length, while positioning errors exhibit a minimum value at an optimal baseline length, which coincides with the point of minimized second-order gradient errors. Regarding noise effects, characterized by the standard deviation, first-order gradient errors remain stable across different standard deviations; however, second-order gradient errors increase linearly with standard deviations and the amplitude of their fluctuations also increases. The effect of array geometry imperfections are investigated by theoretical analysis and numerical simulation, and results show that the dependence between the errors of two types of order gradients and the percents of magnetometer lateral offset is approximately linear or piecewise linear. Also, the curves of the relative errors of two types of order gradients are consistent with the curves of the relative errors of localization. Crucially, positioning accuracy is primarily governed by the precision of the second-order gradient measurements. Research limitations/implications The scalar magnetometer array proposed in the manuscript was not built, so the experimental verification of the inversion algorithm based on second-order Euler deconvolution was not carried out. Originality/value This novel sensor array features the symmetric tri-planar design, enabling the replacement of components within a single plane. Algorithmically, it innovates by eliminating background fields and time-varying interference through gradient-form Euler equations about total-field magnetic anomaly; integrates structural index determination directly with coordinate solving; and develops radius-reduction iterative filtering for solution refinement. Furthermore, this approach yields new insights by quantifying the dominance of second-order gradients in positioning accuracy under varying noise STD and array baseline lengths, and the quantitative analysis on the effect of array geometry imperfection is given.

Purpose While miniaturized pressure sensors are well-established, conventional silicon-based devices suffer from PN junction breakdown at high temperatures, leading to significant performance degradation. Silicon-on-Insulator sensors offer a solution for high-temperature applications but face persistent challenges in doping control, deep silicon etching and high-temperature packaging. These challenges hinder the simultaneous achievement of miniaturization, high-temperature resilience and high performance. This study aims to overcome these limitations by systematically optimizing these critcal manufacturing and packaging processes. Design/methodology/approach In this paper, a backside-sensing miniature flat high-temperature pressure sensor is designed. Through simulations and experiments, the optimal doping process and deep silicon etching process for manufacturing the corresponding presure-sensitive chip are investigated. For the packaging of the pressure-sensitive chip and the extraction of electrical signals, the deep-hole physical vapor deposition technology for the metal seed layer and the high-temperature reliable interconnection surface mount technology are employed to ensure the reliability of the pressure sensor in high-temperature environments. Findings This pressure sensor has an operating temperature range of −55°C to 235°C, a nonlinearity of 0.3% FS and a thermal sensitivity drift of ±0.018%/°C. It demonstrates excellent comprehensive performance, which fully proves that its design is reliable and reasonable. Originality/value Compared with existing studies that mostly focus on the analysis of a single packaging technology for flat pressure sensors, this paper proposes a set of optimization schemes for key steps covering the entire process from chip manufacturing to chip packaging, which effectively improves the overall performance of the sensor.

Purpose Leveraging its advantages, microfluidic technology has become a crucial platform for nucleic acid analysis. The magnetic bead method, which processes nucleic acids by directly manipulating magnetic bead complexes, demonstrates unique value in microfluidic nucleic acid handling. The purpose of this paper is to understand the future trends of magnetic bead-based microfluidic technology in nucleic acid extraction applications. Design/methodology/approach First, this paper reviews the regulation mechanisms of magnetic beads in microfluidic systems. Subsequently, it explores the application of magnetic bead-based microfluidic technology in nucleic acid extraction from the perspectives of four physical fields, namely, magnetic, centrifugal inertial, acoustic and electric field. In addition, it briefly analyzes the application of this technology in point-of-care testing (POCT) for nucleic acids. Findings The four physical fields, namely, magnetic, centrifugal inertial, acoustic and electric field, can respectively optimize the key steps in the nucleic acid extraction process, thereby enhancing the extraction efficiency, purity and level of automation. Originality/value This paper innovatively sorts out and classifies the existing microfluidic systems for nucleic acid extraction from the perspective of physical fields.

Purpose This review aims to bridge the gap between electrochemistry and electromyographic (EMG) sensor design by exploring how electrochemical principles influence EMG electrode development and signal analysis for motor function assessment. Design/methodology/approach A structured literature review was conducted using databases such as PubMed, IEEE Xplore, ScienceDirect and Scopus. The analysis focuses on the electrochemical characteristics of the muscle–electrode interface, the design of advanced EMG electrodes using materials like Ag/AgCl, conductive polymers and nanostructures, and the implications for signal acquisition and processing in both clinical and wearable applications. Findings Electrochemical optimization of EMG electrodes significantly improves signal fidelity, reduces noise and enhances comfort for long-term and wearable use. Advances such as PEDOT:PSS coatings, nanomaterial enhancements and hybrid electrode designs enable high-density EMG recording with improved stability. These hardware improvements, when integrated with advanced signal processing and machine learning algorithms, allow for more precise analysis of muscle activation patterns, fatigue states and neuromuscular disorders. Originality/value This review uniquely synthesizes electrochemical theory with practical EMG sensor engineering and data analytics. It emphasizes the importance of electrochemical properties in shaping the performance of EMG systems and demonstrates how interface-level innovations contribute to enhanced diagnostic, rehabilitative and prosthetic applications. The interdisciplinary perspective fosters the development of next-generation EMG technologies for accurate and robust motor function evaluation.

Purpose This study aims to provide a critical and comprehensive assessment of the latest advancements and remaining challenges in the development of electrochemical potassium ion (K+) sensors for sweat analysis, with particular emphasis on applications in sports performance monitoring and general health assessment. Design/methodology/approach The review systematically explores the physiological relevance of K+ in sweat, its utility in different application contexts (athletic vs clinical) and the underlying principles of potentiometric solid-contact ion-selective electrodes. It evaluates the evolution of solid-contact materials from conducting polymers to carbon nanomaterials and MXenes, alongside fabrication techniques and integration strategies for multiplexed sensing platforms. Furthermore, it identifies technical obstacles – such as signal drift, on-body calibration and lack of clinical validation – and discusses current research directions to address them. Findings Advanced SC materials such as MXenes and carbon nanomaterials significantly improve potential stability and sensing performance compared to traditional conducting polymers. Multiplexed wearable systems that combine K+ sensing with other biomarkers (e.g. Na+, pH and temperature) enable more reliable and contextualized physiological data. However, in spite of progress, challenges such as long-term operational stability, sensor calibration on the body and the absence of a validated correlation between sweat and blood K+ concentrations remain major barriers to clinical translation. Originality/value This review bridges materials science, electrochemical sensing, wearable systems engineering and personalized health care. It uniquely positions sweat K+ monitoring not only as a performance optimization tool for athletes but also as a potential early-warning indicator for physiological imbalances. This study provides an interdisciplinary roadmap towards realizing autonomous, smart and clinically meaningful sweat sensors.

Purpose This paper aims to design and implement real-time indoor plant health monitoring system using optical wireless communication (OWC) integrated with various environmental sensors and an Arduino microcontroller. The system continuously monitors and transmits plant care parameters such as temperature, humidity, soil moisture, pH level and light intensity using light-emitting diode (LED) based on OWC system ensuring timely insights and responsive care for maintaining indoor plant health through a user-friendly interface. Design/methodology/approach The environmental sensors are integrated with an Arduino Uno-based prototype to monitor key plant care parameters such as temperature, humidity, soil moisture, pH level and light intensity. Three plant samples Coleus amboinicus, aloe vera and Tabernaemontana divaricate were chosen for continuous observation. The sensor data is modulated and transmitted using an LED-based OWC link. The photodetector at the receiver end detects the incoming light pulses that are demodulated and displayed through an accessible interface, enabling real-time observation of indoor plant health metrics. Findings The sensor data is transmitted over distances up to 390 cm using LED-based OWC link. The temperature and humidity ranges are maintained for healthy growth of three plant samples. Coleus amboinicus and Aloe vera are exposed to alkaline conditions, while Tabernaemontana divaricate thrived in acidic soil. High retention of moisture is observed for Coleus amboinicus while its variability is moderate for aloe vera and Tabernaemontana divaricate. Light intensity readings are monitored to assess system responsiveness to varying lighting conditions. Originality/value The work presents real-time environmental sensing integrated with OWC for indoor plant health monitoring. The sensor data is transmitted over varying distances, enabling sustainable indoor plant care.