Search Results (3,565)

As the core sensor of inertial measurement units, the reliability of Micro-Electro-Mechanical Systems (MEMS) gyroscopes is critical for long-term navigation and motion control applications. To bridge the mechanism-data gap in MEMS multi-mechanism degradation modeling, this paper proposes a physics-informed dual-indicator reliability assessment framework based on Wiener processes. Two degradation indicators under consideration are frequency-related degradation caused by stiffness degradation and Q-factor degradation caused by damping degradation, for which corresponding physics-embedded stochastic degradation models are formulated. The two indicators are normalized and fused through a generalized weighted limit state function, where failure is defined as gyroscope-level performance failure. Closed-form reliability expressions are derived for linear limit states, while Monte Carlo simulation is used for nonlinear cases. Reduced-order multiphysics simulation cases, including a double-ended fixed beam and a cantilevered MEMS mass block, are used to demonstrate the mechanism-to-indicator-to-reliability modeling procedure. The results show that the proposed dual-indicator framework provides more balanced reliability assessment than single-indicator analysis under the simulation setting. The proposed method offers an alternative mechanism-informed approach for reliability analysis and lifetime prediction of other MEMS devices. Full article

DNA extraction is a critical prerequisite for reliable downstream analyses such as Polymerase Chain Reaction (PCR), sequencing, and genotyping. Conventional methods often require labor-intensive protocols, large sample volumes, or costly automation. Microfluidic approaches offer an alternative by reducing reagent consumption and enabling faster, more integrated workflows. Here, we present a passive lab-on-a-chip device that performs DNA extraction from complex biological media and enables subsequent on-chip single-molecule analysis. The chip integrates a magnetophoresis-based solid-phase extraction module with a fluorescence detection section capable of quantifying DNA molecules in microchannels and visualizing stretched molecules in nanochannels. The multi-level micro/nanofluidic architecture is fabricated in polymer using a single-step nanoimprinting process with a total manufacturing time of two minutes per chip, enabling scalable production. As a proof of concept, the device extracted DNA from samples spiked into buffer or plasma. On-chip transfer efficiency of DNA–bead complexes to the elution buffer reached 86%, and quantitative analysis of the recovered liquid showed an overall extraction efficiency of 40% (including DNA recovery off-chip), with intact 48 kbp DNA confirmed in both micro- and nanochannel measurements. This platform offers a promising foundation for point-of-care and point-of-interest applications, where integrated DNA extraction and analysis can reduce sample loss and support streamlined, automated workflows. Full article

To address the issues of pronounced resonance, limited control bandwidth, and insufficient trajectory tracking accuracy in piezoelectric-driven elliptical vibration-assisted cutting (EVC) systems under high-frequency vibration, this paper proposes a trajectory tracking control strategy combining damping control with frequency-domain shaping. First, a damping-control strategy is integrated into the control system to refine the plant’s inherent dynamic properties, suppressing the resonance peak and elevating the system’s stability margin. Second, to enhance the system bandwidth and dynamic response, a high-gain PID controller is designed via frequency shaping. Additionally, given that the nominal model becomes high-order after implementing the damping controller, proportional gain is used for approximate equivalence with the system transfer function, lowering the model order and streamlining controller design. Next, a disturbance observer (DOB) is introduced to estimate and compensate for the unmodeled dynamics in the feedforward path in real time, further improving the trajectory tracking accuracy. Finally, taking the designed piezoelectric-driven EVC device as the controlled plant, the system frequency response is obtained through sweep excitation experiments, based on which the nominal model is identified, and the controller parameters are determined. The experimental results demonstrate that the proposed control strategy effectively suppresses resonance effects, increases system bandwidth, and reduces the trajectory tracking error. In the complex harmonic superposition trajectory tracking experiment, the steady-state tracking error is maintained within ±0.09 μm. These results demonstrate that the proposed approach markedly improves the system’s dynamic response and trajectory tracking performance, thereby providing technical support for high-precision fabrication of micro/nano-structured surfaces. Full article

Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain–computer interfaces. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

This paper presents the design, fabrication, and characterization of a sub-milliwatt graphene-based micro thermal conductivity detector (µTCD) that utilizes a suspended multilayer graphene (MLG) bridge to sense volatile organic compounds (VOCs) in the gas phase based on their thermal transport properties. The graphene bridge is transferred onto a silicon chip with integrated microchannels using a photolithography-free process. By incorporating microchannel designs, this approach enables precise transfer of suspended MLG dimensions without conventional patterning steps. A key innovation of this work lies in the use of an ultra-low thermal mass suspended graphene architecture, which significantly increases temperature rise per unit input power, thereby enhancing sensitivity per unit power compared to conventional metal-based TCDs. The fabricated µTCD successfully produces chromatograms of multiple VOC species, closely matching those obtained using a standard flame ionization detector (FID). The device demonstrates an estimated limit of detection (LOD) of 190 ppm while consuming an average power of 151 µW under DC operation. Full article

Conventional liquid springs enable storage of energy in the form of interfacial tension at forcibly wetted lyophobic surfaces. The pressure–volume work performed to compress the liquid into a poorly wettable porous medium is recovered during spontaneous expulsion when pressure falls below the capillary pressure characteristic of a given system. Our study explores generalizations to easily wettable materials where liquid infiltration is opposed solely by steric hindrance exerted on liquid molecules in micro-sized pores. The concept is exemplified in molecular simulations of prototypical model systems with methanol intruding narrow slits between hydrocarbon or graphene surfaces. While these materials show significant wetting propensities at macroscopic interfaces with liquid methanol, substantial compression is required to wet molecular-sized pores barely accommodating a monolayer of liquid molecules. The observed O(10³) bar intrusion pressures secure stored energy densities competitive with supercapacitors and amenable to improvement. Wall–liquid attraction and small pore diameters lead to intrusion–expulsion pathways along cooperative-adsorption isotherms. The process avoids abrupt liquid/vapor transitions and associated nucleation barriers, responsible for cycle hysteresis in experiments with water in hydrophobic capillaries. Using open ensemble (Grand Canonical) Monte Carlo sampling, we identify the range of porosities supporting reversible energy storage/recovery operation in lyophilic media; the results can assist with the design of molecular spring devices with competitive storage and power capacities in pragmatic contexts. Full article

ADHD is typically assessed through reports from parents, teachers, or clinicians, but these reports may not fully capture how motor behavior is organized at the signal level. This cross-sectional study examined whether X-axis acceleration features derived from a wearable device could provide preliminary evidence of ADHD symptom-related motor-pattern differences in children and adolescents. Primary school children aged 6–13 years wore an Apple Watch Series 7, and X-axis accelerometer signals were used to extract features reflecting waveform distribution, zero-crossing rate, micro-motion, and local movement fragmentation. ADHD symptoms and broader emotional and behavioral difficulties were assessed using the SNAP-IV and SDQ. The results showed that the X-axis zero-crossing rate was the most robust feature differentiating the High ADHD and Low ADHD groups across the T-task and F-task recordings. X-axis zero-crossing rate reached statistical significance (p = 0.029), indicating more frequent short-interval switching between positive and negative acceleration directions in children with higher ADHD symptom levels. This finding suggests that directional switching or local movement fragmentation in X-axis acceleration may be a sensitive movement characteristic associated with ADHD symptoms. In addition, X-axis skewness showed a consistent directional tendency, with higher values in the High ADHD group; this effect was marginal in the T-task recording (p = 0.065), suggesting a possible tendency toward waveform asymmetry or distributional imbalance during longer movement recording. Overall, these findings provide preliminary evidence that ADHD symptom-related motor differences may be reflected in the organization of X-axis acceleration signals, particularly in directional switching indexed by zero-crossing rate and, to a lesser extent, waveform asymmetry indexed by skewness. Given the cross-sectional design, symptom-based grouping, modest sample size, and incomplete recording-context control, these results should be interpreted cautiously and require confirmation in larger, diagnostically characterized samples with standardized wearable-recording protocols. Full article

Resource-constrained wearable systems often need to be able to execute signal processing and AI workloads. There are many trade-offs to consider for this type of application. This paper presents a lightweight convolution-aware soft processor for embedded signal-processing on resource-constrained wearable devices. This architecture represents a middle ground for signal-processing applications between dedicated accelerators and lightweight soft processors. The proposed architecture integrates a two-lane SIMD integer datapath with a split-stage IEEE-754 floating-point accumulation pipeline. The split-stage design enables overlap between multiplication, accumulation, and operand fetch, improving arithmetic utilization while maintaining low resource costs. The processor was implemented on the Artix-7-based Basys3 platform and evaluated using one-dimensional convolution workloads. The experimental results demonstrate a $6\times$ speedup over MicroBlaze-class soft processors while maintaining the same static power usage (0.073 W), and only requiring 44% higher dynamic power consumption. The architecture achieves this with significantly fewer FPGA resources than accelerator-based solutions such as DPU overlays. The proposed architecture provides a practical alternative for wearable and resource-constrained FPGA systems requiring deterministic convolution performance, demonstrating a balanced design point for embedded wearable platforms where software-defined flexibility and convolution acceleration are both required. Full article

Continuous manufacturing has emerged as the prevailing paradigm in the modern pharmaceutical industry, imposing stringent demands for efficient, real-time inspection methods. Furthermore, deploying high-performance deep learning models on industrial edge devices remains challenging due to computational constraints and the difficulty of detecting micro-defects (e.g., micro-cracks and spots). This paper proposes EMN-net, a lightweight defect detection model built upon the YOLOv8n architecture. The proposed algorithm integrates a MobileNetV3 backbone, the Efficient Local Attention (ELA) mechanism and the Normalized Wasserstein Distance (NWD) loss function to balance computational efficiency with sensitivity toward micro-defects. Evaluated on a self-built industrial tablet dataset expanded to 3086 images, EMN-net achieves an mAP50 of 97.8%, representing a 2.5% improvement over the baseline YOLOv8n. the computational complexity is reduced to 4.4 GFLOPs, while the inference throughput reaches 118 FPS, satisfying the real-time requirements of high-speed production lines. Additionally, the model exhibits improved robustness under simulated motion blur and sensor noise. EMN-net presents a balanced automated visual inspection solution for edge devices in continuous pharmaceutical manufacturing. Full article

For eyes-free operation of input interfaces, tactile feedback is increasingly recognized as an important means of transmitting intuitive information. In particular, auxiliary keypads designed for creators such as illustrators and designers can cause fatigue and input errors during prolonged use. To address these issues, we propose a tactile device that delivers input feedback directly through a single keytop. Conventional haptic actuators, such as eccentric rotating mass motors (ERMs) and linear resonant actuators (LRAs), have limitations, including vibration of the entire structure in which they are installed and operational noise. Therefore, in this study, we adopted shape memory alloy (SMA) wire actuators to achieve localized stimulation and silent operation. By integrating three SMA actuators into a keytop, the proposed tactile keytop can present various types of feedback to users. The vibration characteristics of the SMA actuator were analyzed using a high-speed camera, and the results confirmed stable micro-vibration control. User experiments confirm high recognition accuracy in the tactile presentation of both spatial directional patterns and temporal rhythm patterns. In addition, qualitative evaluations demonstrate that driving frequency adjustment enables the presentation of a diverse range of tactile sensations. These findings indicate that the proposed tactile keytop has potential as a localized tactile feedback interface for future eyes-free input systems. Full article

Background/Objectives: Evaluation of real-world outcomes of standalone implantation of the third-generation trabecular micro-bypass device (iStent infinite) in eyes with mild to severe primary open-angle glaucoma (POAG). Materials: This retrospective, uncontrolled consecutive case series included eyes undergoing standalone iStent infinite implantation at a single U.S. practice. Outcomes were assessed through 12 months, including changes in intraocular pressure (IOP) and medication burden (primary), proportions achieving IOP ≤ 12/15/18 mmHg and medication categories (secondary), and safety. Subgroup analyses were completed based on preoperative IOP, glaucoma severity, and medication burden. Results: Fifty-one eyes (mean age 66.5 ± 10.8 years) were included. Mean baseline IOP was 20.1 ± 5.4 mmHg on 2.1 ± 1.2 medications. At month 12, the mean IOP decreased to 16.0 ± 3.6 mmHg (−4.1 mmHg, −20.4%; p < 0.001), and mean medications decreased to 1.5 ± 1.2 (−28.6%; p < 0.001). The proportion of eyes achieving IOP ≤ 18/15/12 mmHg increased from 41.2%/15.7%/3.9% to 79.6%/42.9%/16.3%, respectively (all p < 0.001). Medication-free eyes increased from 15.7% to 30.6%, while eyes requiring ≥ 3 medications decreased from 47.1% to 20.4%. Eyes with baseline IOP > 18 mmHg achieved greater IOP reduction (−27.8%), whereas eyes with baseline IOP ≤ 18 mmHg maintained stable IOP with reduced medications. Kaplan–Meier analysis demonstrated 12-month freedom from incisional reintervention of 92.2%. No intraoperative complications occurred. Transient self-resolving hyphema was observed in 3.9% of eyes. A secondary incisional surgery was performed in four eyes (7.8%); no vision-threatening complications were reported. Conclusions: Standalone iStent infinite implantation resulted in significant IOP and medication reductions with a favorable safety profile over 12 months, with outcomes aligned with preoperative treatment goals. These results suggest potential benefit as a less invasive real-world glaucoma intervention, warranting confirmation in larger prospective studies. Full article

Microfluidic devices are widely used for cell manipulation, but the effects of physical contact between cells and microchannel walls are not well understood. This study examines how such contact influences the behaviour of red blood cells (RBCs) during controlled manipulation. RBCs were driven through a narrow microchannel constriction (3.6 × 3.0 µm in cross-section and 2500 µm in length), enabling precise application of mechanical load. A pump system allowed accurate control of flow conditions, ranging from complete immobilisation to defined shear stress by adjusting flow rates. Under immobilised conditions, the recovery time constant of RBCs increased with longer loading durations, consistent with previous studies. However, when shear stress was introduced, recovery dynamics changed significantly. Notably, a 30-fold difference in recovery time constant was observed between a 5 s immobilisation and a 5 s load applied at a flow speed of 0.5 mm/s. Furthermore, the rapid elastic recovery typically occurring within approximately 0.1 s after unloading was suppressed under flow conditions. These results demonstrate that viscous interactions between channel walls and surrounding fluid play a critical role in determining cellular responses during microfluidic manipulation. Full article

The self-calibration of micro-component position and orientation is a key step in micro-assembly. To address the limitations of conventional self-calibration methods—where the calibration substrate is fixed and lacks adaptability—this study proposes a droplet-array-based method for self-calibrating micro-component position and orientation. By using a droplet array to form a reconfigurable calibration substrate, the method supports iterative updates of micro-devices and enables synchronous restructuring of the substrate. First, a mechanical model of the self-calibration process is established to analyze the coupling forces exerted by the liquid-bridge array between the calibration substrate and the micro-component, thereby clarifying the mechanism of droplet-array-driven self-calibration. Next, the effects of micro-component material and surface properties on calibration error are examined. Extensive experiments are then conducted to validate the proposed analytical approach. The results show that a droplet array matching the shape and size of the micro-component can be constructed in real time as a calibration substrate. Through the coupling forces generated by the liquid bridges, self-calibration of micro-components with arbitrary shapes and dimensions can be achieved. Calibration accuracy is dependent upon the material and surface roughness of the micro-component. Variations in the micro-component material lead to different forces being applied by the liquid bridge, with the self-calibration error arising from the interplay of these factors. For micro-components of identical material, a smoother surface corresponds to higher calibration accuracy. Full article