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Search Results (441)

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Keywords = microelectromechanical (MEMS) device

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14 pages, 966 KiB  
Article
Investigation of the Thermal Conductance of MEMS Contact Switches
by Zhiqiang Chen and Zhongbin Xie
Micromachines 2025, 16(8), 872; https://doi.org/10.3390/mi16080872 - 28 Jul 2025
Viewed by 253
Abstract
Microelectromechanical system (MEMS) devices are specialized electronic devices that integrate the benefits of both mechanical and electrical structures. However, the contact behavior between the interfaces of these structures can significantly impact the performance of MEMS devices, particularly when the surface roughness approaches the [...] Read more.
Microelectromechanical system (MEMS) devices are specialized electronic devices that integrate the benefits of both mechanical and electrical structures. However, the contact behavior between the interfaces of these structures can significantly impact the performance of MEMS devices, particularly when the surface roughness approaches the characteristic size of the devices. In such cases, the contact between the interfaces is not a perfect face-to-face interaction but occurs through point-to-point contact. As a result, the contact area changes with varying contact pressures and surface roughness, influencing the thermal and electrical performance. By integrating the CMY model with finite element simulations, we systematically explored the thermal conductance regulation mechanism of MEMS contact switches. We analyzed the effects of the contact pressure, micro-hardness, surface roughness, and other parameters on thermal conductance, providing essential theoretical support for enhancing reliability and optimizing thermal management in MEMS contact switches. We examined the thermal contact, gap, and joint conductance of an MEMS switch under different contact pressures, micro-hardness values, and surface roughness levels using the CMY model. Our findings show that both the thermal contact and gap conductance increase with higher contact pressure. For a fixed contact pressure, the thermal contact conductance decreases with rising micro-hardness and root mean square (RMS) surface roughness but increases with a higher mean asperity slope. Notably, the thermal gap conductance is considerably lower than the thermal contact conductance. Full article
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15 pages, 6406 KiB  
Communication
Design and Static Analysis of MEMS-Actuated Silicon Nitride Waveguide Optical Switch
by Yan Xu, Tsen-Hwang Andrew Lin and Peiguang Yan
Micromachines 2025, 16(8), 854; https://doi.org/10.3390/mi16080854 - 25 Jul 2025
Viewed by 336
Abstract
This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si3N4) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, [...] Read more.
This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si3N4) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, a Si3N4 wire is initially positioned up suspended in the air. In the second state, this wire will be moved down to be placed between two arms of the DC waveguides, changing the coupling behavior to achieve bar and cross states of the optical switch function. In the future, the MEMS will be used to move this wire down. In this work, we present simulations of the two static states to optimize the DC structure parameters. Based on the simulated results, the device size is 8.8 μm × 55 μm. The insertion loss is calculated to be approximately 0.24 dB and 0.33 dB, the extinction ratio is approximately 24.70 dB and 25.46 dB, and the crosstalk is approximately −24.60 dB and −25.56 dB, respectively. In the C band of optical communication, the insertion loss ranges from 0.18 dB to 0.47 dB. As such, this device will exhibit excellent optical switch performance and provide advantages in many integrated optics-related optical systems applications. Furthermore, it can be used in optical communications, data centers, LiDAR, and so on, enhancing important reference value for such applications. Full article
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24 pages, 11574 KiB  
Article
Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS
by Ali Nazari, Armin Aghajani, Phiona Buhr, Byoungyoul Park, Yunli Wang and Cyrus Shafai
Micromachines 2025, 16(7), 753; https://doi.org/10.3390/mi16070753 (registering DOI) - 26 Jun 2025
Viewed by 1141
Abstract
This study presents a comprehensive multi-objective optimization framework specifically designed for micro-electromechanical systems (MEMS). The framework integrates both traditional and adaptive optimization techniques, named Surrogate-Assisted Multi-Objective Optimization (SAMOO) and Adaptive-SAMOO (A-SAMOO), respectively. By addressing key limitations of traditional approaches, such as the consideration [...] Read more.
This study presents a comprehensive multi-objective optimization framework specifically designed for micro-electromechanical systems (MEMS). The framework integrates both traditional and adaptive optimization techniques, named Surrogate-Assisted Multi-Objective Optimization (SAMOO) and Adaptive-SAMOO (A-SAMOO), respectively. By addressing key limitations of traditional approaches, such as the consideration of objective constraints and the provision of multiple design options, the proposed framework enhances both flexibility and practical applicability. Results show that adaptive optimization outperforms traditional offline methods by delivering a greater number and higher quality of optimal solutions while requiring fewer finite element method simulations. The adaptive approach showed a significant advantage by attaining high-quality solutions while requiring only 2.8% of the finite element method (FEM) evaluations compared to traditional methods that do not incorporate surrogate models. This performance boost highlights the advantages of online learning in enhancing the accuracy, speed, and diversity of solutions in MEMS optimization. These optimization schemes were tested on multiple MEMS devices with varying physics and complexities, specifically the U-shaped Lorentz force actuator, serpentine Lorentz force actuator, and thermal actuator. The results highlight the robustness and versatility of the proposed methods, particularly in addressing cases involving discrete design variables and strict objective constraints. This comprehensive, step-by-step framework serves as a valuable resource for researchers and practitioners aiming to optimize MEMS designs from the ground up, providing a reliable and effective approach to multi-objective optimization in MEMS applications. Full article
(This article belongs to the Special Issue MEMS Actuators and Their Applications)
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33 pages, 10547 KiB  
Review
Prospects and Trends in Biomedical Microelectromechanical Systems (MEMS) Devices: A Review
by Lowell Welburn, Amir Milad Moshref Javadi, Luong Nguyen and Salil Desai
Biomolecules 2025, 15(6), 898; https://doi.org/10.3390/biom15060898 - 18 Jun 2025
Cited by 1 | Viewed by 2479
Abstract
Designing and manufacturing devices at the micro- and nanoscales offers significant advantages, including high precision, quick response times, high energy density ratios, and low production costs. These benefits have driven extensive research in micro-electromechanical systems (MEMS) and nano-electromechanical systems (NEMS), resulting in various [...] Read more.
Designing and manufacturing devices at the micro- and nanoscales offers significant advantages, including high precision, quick response times, high energy density ratios, and low production costs. These benefits have driven extensive research in micro-electromechanical systems (MEMS) and nano-electromechanical systems (NEMS), resulting in various classifications of materials and manufacturing techniques, which are ultimately used to produce different classifications of MEMS devices. The current work aims to systematically organize the literature on MEMS in biomedical devices, encompassing past achievements, present developments, and future prospects. This paper reviews the current research trends, highlighting significant material advancements and emerging technologies in biomedical MEMS in order to meet the current challenges facing the field, such as ensuring biocompatibility, achieving miniaturization, and maintaining precise control in biological environments. It also explores projected applications, including use in advanced diagnostic tools, targeted drug delivery systems, and innovative therapeutic devices. By mapping out these trends and prospects, this review will help identify current research gaps in the biomedical MEMS field. By pinpointing these gaps, researchers can focus on addressing unmet needs and advancing state-of-the-art biomedical MEMS technology. Ultimately, this can lead to the development of more effective and innovative biomedical devices, improving patient care and outcomes. Full article
(This article belongs to the Special Issue Novel Materials for Biomedical Applications: 2nd Edition)
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23 pages, 1475 KiB  
Article
Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies
by Danilo Pietro Pau, Simone Tognocchi and Marco Marcon
Sensors 2025, 25(12), 3679; https://doi.org/10.3390/s25123679 - 12 Jun 2025
Viewed by 2559
Abstract
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, [...] Read more.
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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27 pages, 8164 KiB  
Article
Machine Learning-Driven Structural Optimization of a Bistable RF MEMS Switch for Enhanced RF Performance
by J. Joslin Percy, S. Kanthamani and S. Mohamed Mansoor Roomi
Micromachines 2025, 16(6), 680; https://doi.org/10.3390/mi16060680 - 4 Jun 2025
Viewed by 717
Abstract
In the rapidly advancing digital era, the demand for miniaturized and high-performance electronic devices is increasing, particularly in applications such as wireless communication, unmanned aerial vehicles, and healthcare devices. Radio-frequency microelectromechanical systems (RF MEMS), particularly RF MEMS switches, play a crucial role in [...] Read more.
In the rapidly advancing digital era, the demand for miniaturized and high-performance electronic devices is increasing, particularly in applications such as wireless communication, unmanned aerial vehicles, and healthcare devices. Radio-frequency microelectromechanical systems (RF MEMS), particularly RF MEMS switches, play a crucial role in enhancing RF performance by providing low-loss, high-isolation switching and precise signal path control in reconfigurable RF front-end systems. Among different configurations, electrothermally actuated bistable lateral RF MEMS switches are preferred for their energy efficiency, requiring power only during transitions. This paper presents a novel approach to improve the RF performance of such a switch through structural modifications and machine learning (ML)-driven optimization. To enable efficient high-frequency operation, the H-clamp structure was re-engineered into various lateral configurations, among which the I-clamp exhibited superior RF characteristics. The proposed I-clamp switch was optimized using an eXtreme Gradient Boost (XGBoost) ML model to predict optimal design parameters while significantly reducing the computational overhead of conventional EM simulations. Activation functions were employed within the ML model to improve the accuracy of predicting optimal design parameters by capturing complex nonlinear relationships. The proposed methodology reduced design time by 87.7%, with the optimized I-clamp switch achieving −0.8 dB insertion loss and −70 dB isolation at 10 GHz. Full article
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14 pages, 1549 KiB  
Article
Equalizing the In-Ear Acoustic Response of Piezoelectric MEMS Loudspeakers Through Inverse Transducer Modeling
by Oliviero Massi, Riccardo Giampiccolo and Alberto Bernardini
Micromachines 2025, 16(6), 655; https://doi.org/10.3390/mi16060655 - 29 May 2025
Viewed by 2612
Abstract
Micro-Electro-Mechanical Systems (MEMS) loudspeakers are attracting growing interest as alternatives to conventional miniature transducers for in-ear audio applications. However, their practical deployment is often hindered by pronounced resonances in their frequency response, caused by the mechanical and acoustic characteristics of the device structure. [...] Read more.
Micro-Electro-Mechanical Systems (MEMS) loudspeakers are attracting growing interest as alternatives to conventional miniature transducers for in-ear audio applications. However, their practical deployment is often hindered by pronounced resonances in their frequency response, caused by the mechanical and acoustic characteristics of the device structure. To mitigate these limitations, we present a model-based digital signal equalization approach that leverages a circuit equivalent model of the considered MEMS loudspeaker. The method relies on constructing an inverse circuital model based on the nullor, which is implemented in the discrete-time domain using Wave Digital Filters (WDFs). This inverse system is employed to pre-process the input voltage signal, effectively compensating for the transducer frequency response. The experimental results demonstrate that the proposed method significantly flattens the Sound Pressure Level (SPL) over the 100 Hz-10 kHz frequency range, with a maximum deviation from the target flat frequency response of below 5 dB. Full article
(This article belongs to the Special Issue Exploration and Application of Piezoelectric Smart Structures)
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13 pages, 3186 KiB  
Article
The Design and Performance Evaluation of an Eye-Tracking System Based on an Electrostatic MEMS Scanning Mirror
by Minqiang Li, Lin Qin, Xiasheng Wang, Jiaojiao Wen, Tong Wu, Xiaoming Huang, Hongbo Yin, Yi Tian and Zhuqing Wang
Micromachines 2025, 16(6), 640; https://doi.org/10.3390/mi16060640 - 28 May 2025
Viewed by 2616
Abstract
In this paper, we proposed an eye-tracking system featuring a small size and high scanning frequency, utilizing an electrostatic biaxial scanning mirror fabricated through a micro-electro-mechanical system (MEMS) process. A laser beam is directed onto the mirror, and the two axes of the [...] Read more.
In this paper, we proposed an eye-tracking system featuring a small size and high scanning frequency, utilizing an electrostatic biaxial scanning mirror fabricated through a micro-electro-mechanical system (MEMS) process. A laser beam is directed onto the mirror, and the two axes of the mirror generate a Lissajous scanning pattern within an artificial eyeball. The scanning pattern reflected from the eyeball is detected by a linear photodiode sensor array (LPSA). The direction and rotation angle of the artificial eyeball result in varying grayscale values across a series of pixels detected by the LPSA, in which the average grayscale values change accordingly. By performing a linear fit between different rotation angles of the same eye movement direction and the corresponding grayscale values, we can determine the correlation between the direction of eye movement and the signal magnitude received by the LPSA, thereby enabling precise eye tracking. The results demonstrated that the minimum resolution was 0.6°. This preliminary result indicates that the system has good accuracy. In the future, this eye-tracking system can be integrated into various wearable glasses devices and applied in various fields, including medicine and psychology. Full article
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13 pages, 3811 KiB  
Article
Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang and Yinlan Ruan
Appl. Sci. 2025, 15(11), 6023; https://doi.org/10.3390/app15116023 - 27 May 2025
Viewed by 2338
Abstract
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 [...] Read more.
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
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13 pages, 2332 KiB  
Article
Non-Invasive Voltage Measurement Device Based on MEMS Electric Field Sensor and Applications
by Xueqiong Zhu, Ziyang Zhang, Chengbo Hu, Zhen Wang, Ziquan Liu, Qing Yang, Jianglin Zhou, Zhenhui Qiu and Shijie Bao
Electronics 2025, 14(11), 2140; https://doi.org/10.3390/electronics14112140 - 24 May 2025
Viewed by 448
Abstract
In the context of new power systems, the safe and accurate sensing of voltage data is crucial for the secure and stable operation of power grids. Given that existing voltage measurement devices cannot meet the development requirements for wide-area deployment and distributed monitoring, [...] Read more.
In the context of new power systems, the safe and accurate sensing of voltage data is crucial for the secure and stable operation of power grids. Given that existing voltage measurement devices cannot meet the development requirements for wide-area deployment and distributed monitoring, this paper designs a non-intrusive voltage measurement device based on MEMS (micro-electromechanical system) electric field sensors, which are characterized by their small size, low power consumption, ease of installation and strong anti-interference ability. Firstly, the paper introduces the voltage measurement principle and analyzes the equivalent circuit based on this analysis; secondly, the key structural design of the measurement device is completed and the prototype of the device is developed; finally, the accuracy and anti-jamming tests of the measurement device are conducted by establishing an experimental platform, followed by field applications. Experimental results demonstrate that the voltage measurement device has high measurement accuracy, and the maximum error is only 1.215%. Additionally, the device has a good shielding capability against the coupled electric field of surrounding interference conductors, with a maximum error increase of 1.313%. In a 10 kV overhead line voltage test, the device can accurately obtain the actual voltage. The voltage measuring device developed in this paper can provide data support for the condition assessment of overhead lines and effective monitoring means for the safe and stable operation of the power system. Full article
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16 pages, 5108 KiB  
Article
Advancing Understanding of High-Temperature Micro-Electro-Mechanical System Failures with New Simulation-Assisted Approach
by Weronika Lidia Sadurska, Matthias Imboden, Jürgen Burger and Alex Jean Dommann
Sensors 2025, 25(10), 3120; https://doi.org/10.3390/s25103120 - 15 May 2025
Viewed by 516
Abstract
High-temperature micro-electro-mechanical systems (MEMSs) are critical for applications in extreme environments and applications where the operating temperature can exceed 1000 °C, but their long-term performance is limited by complex failure mechanisms, including material degradation caused by atomic migration. This study introduces a simulation-assisted [...] Read more.
High-temperature micro-electro-mechanical systems (MEMSs) are critical for applications in extreme environments and applications where the operating temperature can exceed 1000 °C, but their long-term performance is limited by complex failure mechanisms, including material degradation caused by atomic migration. This study introduces a simulation-assisted approach to analyze and predict the dominant failure modes, focusing on vacancy fluxes and their driving forces, within high-temperature MEMS structures. The focus is on tungsten-based structures operating at a temperature of 1580 °C. This approach couples electric-, stress- and temperature-dependent simulations to evaluate atomic migration pathways, which are key contributors to failure. This study demonstrates that void accumulation, driven by vacancy migration, results in localized current density increase, hotspot formation, and accelerated structural degradation. The mean time to failure (MTTF) is shown to have exponential dependence on temperature and inverse-square dependence on current density, highlighting the critical role of these parameters in device reliability. These findings provide a deeper understanding of the failure mechanisms in high-temperature MEMSs and underscore the need for design strategies that mitigate electromigration and stress-induced void growth to enhance device performance and longevity. Full article
(This article belongs to the Section Physical Sensors)
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79 pages, 3684 KiB  
Review
Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology
by Vishnuram Abhinav, Prithvi Basu, Shikha Supriya Verma, Jyoti Verma, Atanu Das, Savita Kumari, Prateek Ranjan Yadav and Vibhor Kumar
Micromachines 2025, 16(5), 522; https://doi.org/10.3390/mi16050522 - 28 Apr 2025
Cited by 4 | Viewed by 6073
Abstract
Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These [...] Read more.
Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These devices enable non-invasive, real-time monitoring of biochemical, electrophysiological, and biomechanical signals, offering personalized and proactive healthcare solutions. In parallel, implantable BioMEMS have significantly enhanced long-term diagnostics, targeted drug delivery, and neurostimulation. From continuous glucose and intraocular pressure monitoring to programmable drug delivery and bioelectric implants for neuromodulation, these devices are improving precision treatment by continuous monitoring and localized therapy. This review explores the materials and technologies driving advancements in wearable and implantable BioMEMSs, focusing on their impact on chronic disease management, cardiology, respiratory care, and glaucoma treatment. We also highlight their integration with artificial intelligence (AI) and the Internet of Things (IoT), paving the way for smarter, data-driven healthcare solutions. Despite their potential, BioMEMSs face challenges such as regulatory complexities, global standardization, and societal determinants. Looking ahead, we explore emerging directions like multifunctional systems, biodegradable power sources, and next-generation point-of-care diagnostics. Collectively, these advancements position BioMEMS as pivotal enablers of future patient-centric healthcare systems. Full article
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14 pages, 4861 KiB  
Article
Pico-Scale Digital PCR on a Super-Hydrophilic Microarray Chip for Multi-Target Detection
by Qingyue Xian, Jie Zhang, Yu Ching Wong, Yibo Gao, Qi Song, Na Xu and Weijia Wen
Micromachines 2025, 16(4), 407; https://doi.org/10.3390/mi16040407 - 30 Mar 2025
Viewed by 2691
Abstract
The technology of digital polymerase chain reaction (dPCR) is rapidly evolving, yet current devices often suffer from bulkiness and cumbersome sample-loading procedures. Moreover, challenges such as droplet merging and partition size limitations impede efficiency. In this study, we present a super-hydrophilic microarray chip [...] Read more.
The technology of digital polymerase chain reaction (dPCR) is rapidly evolving, yet current devices often suffer from bulkiness and cumbersome sample-loading procedures. Moreover, challenges such as droplet merging and partition size limitations impede efficiency. In this study, we present a super-hydrophilic microarray chip specifically designed for dPCR, featuring streamlined loading methods compatible with micro-electro-mechanical systems (MEMS) technology. Utilizing hydrodynamic principles, our platform enables the formation of a uniform array of 120-pL independent reaction units within a closed channel. The setup allows for rapid reactions facilitated by an efficient thermal cycler and real-time imaging. We achieved absolute quantitative detection of hepatitis B virus (HBV) plasmids at varying concentrations, alongside multiple targets, including cancer mutation gene fragments and reference genes. This work highlights the chip’s versatility and potential applications in point-of-care testing (POCT) for cancer diagnostics. Full article
(This article belongs to the Special Issue Application of Microfluidic Technology in Bioengineering)
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10 pages, 1998 KiB  
Article
MEMS-Integrated Tunable Fabry–Pérot Microcavity for High-Quality Single-Photon Sources
by Ziyang Zheng, Jiawei Yang, Xuebin Peng and Ying Yu
Photonics 2025, 12(4), 315; https://doi.org/10.3390/photonics12040315 - 28 Mar 2025
Viewed by 2720
Abstract
We propose a micro-electromechanical system (MEMS)-integrated Fabry–Pérot (F–P) microcavity designed for a tunable single-photon source based on a single semiconductor quantum dot (QD). Through theoretical simulations, our design achieved a Purcell factor of 23, a photon extraction efficiency exceeding 88%, and an optical [...] Read more.
We propose a micro-electromechanical system (MEMS)-integrated Fabry–Pérot (F–P) microcavity designed for a tunable single-photon source based on a single semiconductor quantum dot (QD). Through theoretical simulations, our design achieved a Purcell factor of 23, a photon extraction efficiency exceeding 88%, and an optical cavity mode tuning range of more than 30 nm. Experimentally, we fabricated initial device prototypes using a micro-transfer printing process and demonstrated a tuning range exceeding 15 nm. The device exhibits high mechanical stability, full reversibility, and minimal hysteresis, ensuring reliable operation over multiple tuning cycles. Our findings highlight the potential of MEMS-integrated F–P microcavities for scalable, tunable single-photon sources. Furthermore, reaching a strong coupling regime could enable efficient single-photon routing, opening new possibilities for integrated quantum photonic circuits. Full article
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16 pages, 2488 KiB  
Perspective
Methods for Capturing and Quantifying Contact Events in Collision Sports
by Craig Bolger, Jocelyn Mara, Byron Field, David B. Pyne and Andrew J. McKune
Sports 2025, 13(4), 102; https://doi.org/10.3390/sports13040102 - 27 Mar 2025
Viewed by 679
Abstract
Technological advancements have led to widespread use of wearable devices that capture external performance metrics in team sports. Tracking systems including global positioning system (GPS) technology with inbuilt microelectromechanical systems (MEMS), instrumented mouthguards (iMGs), and video analysis provide valuable insights into the contact [...] Read more.
Technological advancements have led to widespread use of wearable devices that capture external performance metrics in team sports. Tracking systems including global positioning system (GPS) technology with inbuilt microelectromechanical systems (MEMS), instrumented mouthguards (iMGs), and video analysis provide valuable insights into the contact demands of collision sports. In collision sports, successfully “winning the contact” is positively associated with better individual and team performance, but it also comes with a high risk of injury, posing a concern for player welfare. Understanding the frequency and intensity of these contact events is important in order for coaches and practitioners to adequately prepare players for competition and can simultaneously reduce the burden on athletes. Different methods have been developed for detecting contact events, although limitations of the current methods include validity and reliability issues, varying thresholds, algorithm inconsistencies, and a lack of code- and sex-specific algorithms. In this review, we evaluate common methods for capturing contact events in team collision sports and detail a new method for assessing contact intensity through notational analysis, offering a potential alternative for capturing contact events that are currently challenging to detect through microtechnology alone. Full article
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