Search Results (287)

Miniaturized pressure sensors fabricated via micro-electro-mechanical systems (MEMSs) technology are ubiquitous in modern applications. However, the massively produced MEMS pressure sensors, prior to being practically used, need to be calibrated one by one to eliminate or minimize nonlinearity and zero drift. This paper presents a systematic design for the testing and calibration process of MEMS-based absolute pressure sensors. Firstly, a numerical analysis is carried out using finite element method (FEM) simulation, which verifies the accuracy of the temperature control of the physical calibration system. The simulation results reveal a slight non-uniformity of temperature distribution, which is then taken into consideration in the calibration algorithm. Secondly, deploying a home-made calibration system, the MEMS pressure sensors are tested automatically and rapidly. The experimental results show that each batch, which consists of nine sensors, can be calibrated in 80 min. The linearity and temperature coefficient (TC) of the pressure sensors are reduced from 46.5% full-scale (FS) and −1.35 × 10⁻⁴ V·K⁻¹ to 1.5% FS and −8.8 × 10⁻⁷ V·K⁻¹. Full article

Background: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the urgent need for early, non-invasive, and accessible diagnostic tools. This study aimed to evaluate the effectiveness of a microelectromechanical systems (MEMS)-based electronic nose (E-nose) in combination with gas chromatography–mass spectrometry (GC-MS) for CRC detection through sweat volatile organic compounds (VOCs). Methods: A total of 136 sweat samples were collected from 68 volunteer participants. Samples were processed using solid-phase microextraction (SPME) and analyzed by GC-MS, while a custom-designed E-nose system comprising 14 gas sensors captured real-time VOC profiles. Data were analyzed using multivariate statistical techniques, including PCA and PLS-DA, and classified with machine learning algorithms (LDA, LR, SVM, k-NN). Results: GC-MS analysis revealed statistically significant differences between CRC patients and healthy controls (COs). Cross-validation showed that the highest classification accuracy for GC-MS data was 81% with the k-NN classifier, whereas E-nose data achieved up to 97% accuracy using the LDA classifier. Conclusions: Sweat volatilome analysis, supported by advanced data processing and complementary use of E-nose technology and GC-MS, demonstrates strong potential as a reliable, non-invasive approach for early CRC detection. Full article

The treatment of chronic wounds and pressure sores is an important challenge in the context of public health and the effectiveness of patient treatment. Therefore, new methods are being developed to reduce or, in extreme cases, to initiate and conduct the wound healing process. This article presents an innovative smart bandage, programmable using a smartphone, which generates small amplitude impulse vibrations. The communication between the smart bandage and the smartphone is realized using BLE. The possibility of programming the smart bandage allows for personalized therapy. Owing to the built-in MEMS sensor, the smart bandage makes it possible to monitor work during rehabilitation and implement an auto-calibration procedure. The flexible, openwork mechanical structure of the dressing was made in 3D printing technology, thanks to which the solution is easy to implement and can be used together with traditional dressings to create hybrid ones. Miniature electronic circuits and actuators controlled by the PWM signal were designed as replaceable elements; thus, the openwork structure can be treated as single-use. The smart bandage containing six actuators presented in this article generates oscillations in the range from about 40 Hz to 190 Hz. The system generates low-amplitude vibrations, below 1 g. The actuators were operated at a voltage of 1.65 V to reduce energy consumption. For comparison, the actuators were also operated at the nominal voltage of 3.17 V, as specified by the manufacturer. Full article

Wearable and implantable Lab-on-Chip (LoC) biosensors are revolutionizing healthcare by enabling continuous, real-time monitoring of physiological and biochemical parameters in non-clinical settings. These miniaturized platforms integrate sample handling, signal transduction, and data processing on a single chip, facilitating early disease detection, personalized treatment, and preventive care. This review comprehensively explores recent advancements in LoC biosensing technologies, emphasizing their application in skin-mounted patches, smart textiles, and implantable devices. Key innovations in biocompatible materials, nanostructured transducers, and flexible substrates have enabled seamless integration with the human body, while fabrication techniques such as soft lithography, 3D printing, and MEMS have accelerated development. The incorporation of nanomaterials significantly enhances sensitivity and specificity, supporting multiplexed and multi-modal sensing. We examine critical application domains, including glucose monitoring, cardiovascular diagnostics, and neurophysiological assessment. Design considerations related to biocompatibility, power management, data connectivity, and long-term stability are also discussed. Despite promising outcomes, challenges such as biofouling, signal drift, regulatory hurdles, and public acceptance remain. Future directions focus on autonomous systems powered by AI, hybrid wearable–implantable platforms, and wireless energy harvesting. This review highlights the transformative potential of LoC biosensors in shaping the future of smart, patient-centered healthcare through continuous, minimally invasive monitoring. Full article

Aging infrastructure is a growing concern worldwide, with many bridges exceeding 50 years of service, prompting questions about their structural integrity. Over the past decade, the deterioration of bridges has driven extensive research into Structural Health Monitoring (SHM), a tool for early detection of structural deterioration, with particular emphasis on remote-sensing technologies. This review combines a scientometric analysis and a state-of-the-art review to assess recent advancements in the field. From a dataset of 702 publications (2014–2024), 171 relevant papers were analyzed, covering key SHM aspects including sensing devices, data acquisition, processing, damage detection, and reporting. Results show a 433% increase in publications, with the United States leading in output (28.65%), and Glisic, B., with collaborators forming the largest research cluster (11.7%). Accelerometers are the most commonly used sensors (50.88%), and data processing dominates the research focus (50.29%). Key challenges identified include cost (noted in 17.5% of studies), data corruption, and WSN limitations, particularly energy supply. Trends show a notable growth in AI applications (400%), and increasing interest in low-cost, crowdsource-based SHM using smartphones, MEMS, and cameras. These findings highlight both progress and future opportunities in SHM of footbridges. Full article

A single-crystal magnesium oxide (MgO) dual-Fabry–Perot (FP)-cavity sensor based on MEMS technology and laser micromachining is proposed for simultaneous measurement of temperature and pressure. The pressure sensitive cavity is processed by wet chemical etching and direct bonding, which can improve machining efficiency, ensure the quality of the reflection surface and achieve thermal stress matching. Femtosecond laser and micromachining technologies are used to fabricate a rough surface and a through hole to reduce the reflect surface and fix the optical fiber. The bottom surface of the pressure cavity and the upper surface of the MgO wafer form a temperature cavity. A cross-correlation signal demodulation algorithm combined with a temperature decoupling method is proposed to achieve dual-cavity demodulation and eliminate the cross-sensitivity between temperature and pressure, improving the accuracy of pressure measurement. Experimental results show that the proposed sensor can stably operate at an ambient environment of 22–800 °C and 0–0.5 MPa with a pressure sensitivity of approximately 0.20 µm/MPa (room temperature), a repeatability error of 2.06% and a hysteresis error of 1.90%. After temperature compensation, thermal crosstalk is effectively eliminated and the pressure measurement accuracy is 2.01%F.S. Full article

This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still suffer from anchor-related energy losses and limited quality factors (Qs), posing significant challenges for high-performance applications. This study investigates interface modification to boost the quality factor (Q) and reduce the motional resistance, thus improving the electromechanical coupling coefficient and reducing insertion loss. To balance the trade-off between device miniaturization and performance, this work uniquely applies DC current-induced localized annealing to TPoS MEMS resonators, facilitating metal diffusion at the interface. This process results in the formation of platinum silicide, modifying the resonator’s stiffness and density, consequently enhancing the acoustic velocity and mitigating the side-supporting anchor-related energy dissipations. Experimental results demonstrate a Q-factor enhancement of over 300% (from 916 to 3632) and a reduction in insertion loss by more than 14 dB, underscoring the efficacy of this method for reducing anchor-related dissipations due to the highest annealing temperature at the anchors. The findings not only confirm the feasibility of Joule heating for interface modifications in MEMS resonators but also set a foundation for advancements of this post-fabrication thermal treatment technology. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Acoustic tweezers, as advanced micro/nano manipulation tools, play a pivotal role in biomedical engineering, microfluidics, and precision manufacturing. However, piezoelectric-based acoustic tweezers face performance limitations due to multi-physical coupling effects during microfabrication. This study proposes a novel approach using injection molding with embedded electronics (IMEs) technology to fabricate piezoelectric micro-ultrasonic transducers with micron-scale precision, addressing the critical issue of acoustic node displacement caused by thermal–mechanical coupling in injection molding—a problem that impairs wave transmission efficiency and operational stability. To optimize the IME process parameters, a hybrid multi-objective optimization framework integrating NSGA-II and MOPSO is developed, aiming to simultaneously minimize acoustic node displacement, volumetric shrinkage, and residual stress distribution. Key process variables—packing pressure (80–120 MPa), melt temperature (230–280 °C), and packing time (15–30 s)—are analyzed via finite element modeling (FEM) and validated through in situ tie bar elongation measurements. The results show a 27.3% reduction in node displacement amplitude and a 19.6% improvement in wave transmission uniformity compared to conventional methods. This methodology enhances acoustic tweezers’ operational stability and provides a generalizable framework for multi-physics optimization in MEMS manufacturing, laying a foundation for next-generation applications in single-cell manipulation, lab-on-a-chip systems, and nanomaterial assembly. Full article

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

In recent years, using sound as a source of information in environmental monitoring systems has become increasingly important. Thanks to the development of Internet of Things (IoT) and artificial intelligence (AI) technologies, it has become possible to create distributed, intelligent acoustic systems used in medicine, industry, cities, and the natural environment. The article presents an overview of modern methods of acquiring and analysing sound data, from MEMS sensors and microphones, signal processing, and feature extraction to machine learning algorithms. The analysis of many works shows how diverse the approach to acoustic analysis can be, depending on the purpose, context, and environmental constraints. Technical challenges, privacy issues, and possible directions for further development, such as integration with multimodal monitoring systems or edge processing, are also discussed. The article is cross-sectional and can be a starting point for further research on intelligent acoustic monitoring in systems based on AI and IoT. Full article

To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices. Full article

Microsystem technology (MST) and micro-electro-mechanical systems (MEMS) are key technologies that continually introduce new application opportunities. Increasing complexity and individualization require systematic process development to avoid errors and delays. While existing methods for process development address various aspects of the manufacturing process, the systematic consideration of external factors influencing the process environment (PEnv) remains broadly inadequate. Despite extensive standards, PEnv-related influences lead to quality fluctuations in practice. A list of influencing factors and an example process illustrate these challenges. This study aims to analyze which methods exist for process development in MST and to what extent they systematically consider process environmental factors. A mixed methods design was used for the analysis. In a systematic literature review (SLR) using traditional databases and Artificial intelligence-supported search tools, a total of 75 relevant studies from the years 2005 to 2024 were identified. The methods that cover various aspects of process development are presented in an overview. An adapted GRADE (Grading of Recommendations Assessment, Development, and Evaluation) analysis was used to check the extent to which the PEnv can be included in process development using the methods currently available. The results show that existing approaches often take PEnv into account insufficiently. Efficient consideration with the use of current methods requires extensive expert knowledge, knowledge management, and project-specific supplementary methods. This study emphasizes the need for research into methods that systematically integrate environmental requirements into process development to improve the efficiency and quality of MST manufacturing in this area. Full article

Background: In response to the growing demand for the real-time, in-field characterization of odorous anthropogenic emissions, this study develops and uses a MEMS-based micropacked thermal desorption Gas Chromatography system coupled with a PhotoIonization Detector (GC/PID) for Hot Mix Asphalt (HMA) plant emissions. Methods: The innovative portable device, Pyxis GC, enables the high-sensitivity profiling of Volatile Organic Compounds (VOCs), particularly aldehydes and ketones, with sub-ppb detection limits using ambient air as the carrier gas. A comprehensive experimental design optimized the preconcentration parameters, resulting in an efficient, green analytical method evaluated via the Green Analytical Procedure Index (GAPI). Sorbent comparison showed quinoxaline-bridged cavitands outperform the conventional materials. Results and conclusions: The method was successfully deployed on site for source-specific sampling at an HMA plant, generating robust emission fingerprints. To assess environmental impact, a Generalized Additive Model (GAM) was developed, incorporating the process temperature and Sum of Odour Activity Values (SOAV) to predict odour concentrations. The model revealed a significant non-linear influence of temperature on emissions and validated its predictive capability despite the limited sample size. This integrated analytical–statistical approach demonstrates the utility of MEMS technology for real-time air quality assessment and odour dispersion modelling, offering a powerful tool for environmental monitoring and regulatory compliance. Full article

This study explores the combined use of convective and infrared drying (CIRD) for bread slices, utilizing advanced MEMS sensors to monitor temperature, moisture, and drying rates in real time, optimizing efficiency, and energy use. The dehydration kinetics of 1 cm thick bread slices under a controlled CIRD method was used. This analyzes drying rate (water loss speed, WLS) and energy efficiency (EE) using sensor technology. IR drying used 150 W lamps at 7 cm and 15 cm, while convective drying involved 60 °C hot air at 3 m/s. Sensor data aligned with gravimetric measurements. The most energy-efficient model used a 150 W IR lamp at 7 cm (0.645 kWh, 21.572 kWh/kg water removed) but had the longest drying time (220 min at 15 cm). The least efficient model used a 250 W IR lamp at 15 cm (EE = 32.734 kWh/kg). These results of CIRD in bread drying are statistically significant and can be applied to industrial bakery drying processes, helping manufacturers to reduce energy costs, and adopt sensor-driven process control for enhanced sustainability. The CIRD model, which uses a 150 W IR lamp placed 15 cm above the bread slices being dried, represents the most effective optimization strategy. Full article