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Search Results (1,309)

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Keywords = chip-based devices

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9 pages, 294 KB  
Article
Germanium-Based Temperonic Crystal
by Jesus Manzanares-Martinez, Diego Soto-Puebla and Gerardo Morales-Morales
Crystals 2025, 15(10), 899; https://doi.org/10.3390/cryst15100899 (registering DOI) - 16 Oct 2025
Abstract
We propose a germanium-based temperonic crystal consisting of a two-layer unit cell designed to enable interference of thermal waves in the non-Fourier regime. Each layer features temperature-dependent properties, including thermal diffusivity D(T), thermal conductivity κ(T), [...] Read more.
We propose a germanium-based temperonic crystal consisting of a two-layer unit cell designed to enable interference of thermal waves in the non-Fourier regime. Each layer features temperature-dependent properties, including thermal diffusivity D(T), thermal conductivity κ(T), and relaxation time τ(T). Utilizing the Cattaneo-Vernotte model, we predict band gaps in the temperature oscillation frequencies. Our analysis reveals that band gaps emerge when one layer is maintained at 110 K and the other at 50 K; however, these gaps close rapidly as the temperature contrast diminishes or the overall temperature increases. Drawing from the temperonic-crystal paradigm and inspired by recent experimental observations of thermal waves in germanium, this design offers a promising pathway for on-chip control of ultrafast thermal pulses and thermal-management devices in semiconductors. Full article
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15 pages, 5868 KB  
Article
Study on the Correlation Between Surface Roughness and Tool Wear Using Automated In-Process Roughness Measurement in Milling
by Friedrich Bleicher, Benjamin Raumauf and Günther Poszvek
Metrology 2025, 5(4), 62; https://doi.org/10.3390/metrology5040062 - 15 Oct 2025
Abstract
The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the [...] Read more.
The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the Institute of Manufacturing Technology at TU Vienna together with its partners to develop a roughness measurement device that can be directly integrated into machine tools. Building on this foundation, this study tries to find applications beyond mere surface roughness assessment and demonstrates how the device could be applied in broader contexts of manufacturing process monitoring. By linking surface measurements with tool wear monitoring, the study establishes a correlation between surface roughness and wear progression of indexable inserts in milling. It demonstrates how in situ data can support predictive maintenance and the real-time adjustment of cutting parameters. This represents a first step toward integrating in situ metrology into closed-loop control in machining. The experimental setup followed ISO 8688-1 guidelines for tool life testing. Indexable inserts were operated throughout their entire service life while surface roughness was continuously recorded. In parallel, cutting edge conditions were documented at defined intervals using focus variation microscopy. The results show a consistent three-phase pattern: initially stable roughness, followed by a steady increase due to flank wear, and an abrupt decrease in roughness linked to edge chipping. These findings confirm the potential of integrated roughness measurement for condition-based monitoring and the development of adaptive machining strategies. Full article
23 pages, 2255 KB  
Article
Design and Implementation of a YOLOv2 Accelerator on a Zynq-7000 FPGA
by Huimin Kim and Tae-Kyoung Kim
Sensors 2025, 25(20), 6359; https://doi.org/10.3390/s25206359 - 14 Oct 2025
Abstract
You Only Look Once (YOLO) is a convolutional neural network-based object detection algorithm widely used in real-time vision applications. However, its high computational demand leads to significant power consumption and cost when deployed in graphics processing units. Field-programmable gate arrays offer a low-power [...] Read more.
You Only Look Once (YOLO) is a convolutional neural network-based object detection algorithm widely used in real-time vision applications. However, its high computational demand leads to significant power consumption and cost when deployed in graphics processing units. Field-programmable gate arrays offer a low-power alternative. However, their efficient implementation requires architecture-level optimization tailored to limited device resources. This study presents an optimized YOLOv2 accelerator for the Zynq-7000 system-on-chip (SoC). The design employs 16-bit integer quantization, a filter reuse structure, an input feature map reuse scheme using a line buffer, and tiling parameter optimization for the convolution and max pooling layers to maximize resource efficiency. In addition, a stall-based control mechanism is introduced to prevent structural hazards in the pipeline. The proposed accelerator was implemented on the Zynq-7000 SoC board, and a system-level evaluation confirmed a negligible accuracy drop of only 0.2% compared with the 32-bit floating-point baseline. Compared with previous YOLO accelerators on the same SoC, the design achieved up to 26% and 15% reductions in flip-flop and digital signal processor usage, respectively. This result demonstrates feasible deployment on XC7Z020 with DSP 57.27% and FF 16.55% utilization. Full article
(This article belongs to the Special Issue Object Detection and Recognition Based on Deep Learning)
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23 pages, 3722 KB  
Article
Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments
by María Fernanda Cadena Vizuete, Martin Condor, Dennis Raith, Avani Sapre, Marie Follo, Gina Layedra, Roland Mertelsmann, Maximiliano Perez and Betiana Lerner
Biosensors 2025, 15(10), 693; https://doi.org/10.3390/bios15100693 (registering DOI) - 13 Oct 2025
Viewed by 126
Abstract
T cells play a pivotal role in cancer research, particularly in immunotherapy, which harnesses the immune system to target malignancies. However, conventional expansion methods face limitations such as high reagent consumption, contamination risks, and difficulties in maintaining suspension cells in dynamic culture environments. [...] Read more.
T cells play a pivotal role in cancer research, particularly in immunotherapy, which harnesses the immune system to target malignancies. However, conventional expansion methods face limitations such as high reagent consumption, contamination risks, and difficulties in maintaining suspension cells in dynamic culture environments. This study presents a microfluidic system for long-term culture of non-adherent cells, featuring automated perfusion and image acquisition. The system integrates deep learning-based image analysis, which quantifies cell coverage and estimates cell numbers, and efficiently processes large volumes of data. The performance of this deep learning approach was benchmarked against the widely used Trainable Weka Segmentation (TWS) plugin for Fiji. Additionally, two distinct lab-on-a-chip (LOC) devices were evaluated independently: the commercial ibidi® LOC and a custom-made PDMS LOC. The setup supported the proliferation of Jurkat cells and primary human T cells without significant loss during medium exchange. Each platform proved suitable for long-term expansion while offering distinct advantages in terms of design, cell seeding and recovery, and reusability. This integrated approach enables extended experiments with minimal manual intervention, stable perfusion, and supports multi-reagent administration, offering a powerful platform for advancing suspension cell research in immunotherapy. Full article
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17 pages, 880 KB  
Review
Salivary and Microbiome Biomarkers in Periodontitis: Advances in Diagnosis and Therapy—A Narrative Review
by Casandra-Maria Radu, Carmen Corina Radu and Dana Carmen Zaha
Medicina 2025, 61(10), 1818; https://doi.org/10.3390/medicina61101818 - 11 Oct 2025
Viewed by 119
Abstract
Background and Objectives: Periodontitis is a common chronic inflammatory disease and a leading cause of tooth loss worldwide. Traditional diagnostic methods, such as probing and radiographic assessment, are retrospective and fail to detect ongoing disease activity. In recent years, salivary biomarkers and oral [...] Read more.
Background and Objectives: Periodontitis is a common chronic inflammatory disease and a leading cause of tooth loss worldwide. Traditional diagnostic methods, such as probing and radiographic assessment, are retrospective and fail to detect ongoing disease activity. In recent years, salivary biomarkers and oral microbiome profiling have emerged as promising tools for earlier detection and precision-based management. The aim of this review is to synthesize current evidence on salivary and microbiome-derived biomarkers in periodontitis and to evaluate their translational potential in diagnostics and therapy. Materials and Methods: A narrative review was performed using PubMed, Scopus, and Web of Science to identify studies published between 2020 and 2025. Search terms included periodontitis, salivary biomarkers, oral microbiome, dysbiosis, and precision therapy. Priority was given to systematic reviews, meta-analyses, and translational studies that addressed diagnostic or therapeutic applications. Eligible publications included English-language original studies and reviews reporting on the diagnostic or therapeutic relevance of salivary or microbiome biomarkers in periodontitis. Results: Salivary biomarkers such as cytokines, matrix metalloproteinases (MMPs), oxidative stress markers, microRNAs, and extracellular vesicles (EVs) show consistent associations with disease activity and treatment outcomes. Oral microbiome studies reveal that both classical pathogens and community-level dysbiosis contribute to disease risk. Translational advances include chairside immunoassays, biosensors, lab-on-a-chip devices, and artificial intelligence (AI)-driven analyses. Biomarker-guided therapies—such as microbiome modulation, natural bioactive compounds, host-response modulation, and smart biomaterials—are being evaluated with increasing frequency in translational studies. Conclusions: By integrating salivary and microbiome biomarkers with novel diagnostic technologies and emerging therapies, this review complements existing systematic evidence and offers a translational roadmap toward precision periodontology. Full article
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20 pages, 4835 KB  
Article
An Asymmetric SiC Power Module Directly Integrated with Vapor Chamber for Thermal Balancing in MMC
by Binyu Wang, Xiwei Zhou, Yawen Zhu, Mengfei Qi, Hai Lin, Bobin Yao, Shaohua Huang, Xuetao Wang, Qisheng Wu and Weiyu Liu
Appl. Sci. 2025, 15(20), 10869; https://doi.org/10.3390/app152010869 - 10 Oct 2025
Viewed by 171
Abstract
Power modules in silicon carbide (SiC)-based modular multilevel converters (MMCs) suffer from notably severe thermal imbalance and localized overheating. This paper puts forward an asymmetric SiC power module with direct integration of a vapor chamber (VC), designed to balance the thermal distribution inside [...] Read more.
Power modules in silicon carbide (SiC)-based modular multilevel converters (MMCs) suffer from notably severe thermal imbalance and localized overheating. This paper puts forward an asymmetric SiC power module with direct integration of a vapor chamber (VC), designed to balance the thermal distribution inside MMC SMs. Specifically, the chips on the lower side of the HBSM are soldered onto a VC, which is additionally mounted on the direct bonding copper (DBC). Endowed with merits such as favorable temperature uniformity, exceptional thermal conductivity, compact size, flexible design, high integration level, and reasonable cost, the VC serves as an outstanding heat diffuser significantly expanding the effective thermal conduction area and reducing thermal resistance. Moreover, in this structure, the VC also functions as a conductor for device current. Finite element method (FEM) simulation results reveal that the proposed structure can notably reduce the hotspot temperature (from 109 °C to 71.8 °C), the maximum temperature difference among chips (from 45 °C to 13.89 °C), and the low-frequency temperature swing (TSL) (from 68 °C to 38 °C). Consequently, the issues of localized overheating and thermal imbalance in SiC-MMC SMs are effectively addressed. Lifetime analysis further indicates that the proposed structure can reduce the annual damage rate of the chip solder layer by 92.6%. Full article
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15 pages, 2936 KB  
Article
Experimental Characterization of a Silicon Nitride Asymmetric Loop-Terminated Mach-Zehnder Interferometer with a Refractive Index-Engineered Sensing Arm
by Muhammad A. Butt, Mateusz Słowikowski, Dagmara Drecka, Michał Jarosik and Ryszard Piramidowicz
Nanomaterials 2025, 15(19), 1532; https://doi.org/10.3390/nano15191532 - 8 Oct 2025
Viewed by 314
Abstract
We report the design, fabrication, and experimental characterization of an asymmetric loop-terminated Mach–Zehnder interferometer (a-LT-MZI) realized on a silicon nitride (SiN) platform for refractive index (RI) sensing. The LT-MZI architecture incorporates a Sagnac loop that enables bidirectional light propagation, effectively doubling the interaction [...] Read more.
We report the design, fabrication, and experimental characterization of an asymmetric loop-terminated Mach–Zehnder interferometer (a-LT-MZI) realized on a silicon nitride (SiN) platform for refractive index (RI) sensing. The LT-MZI architecture incorporates a Sagnac loop that enables bidirectional light propagation, effectively doubling the interaction length without enlarging the device footprint, enhancing sensitivity and improving stability against environmental noise. Subwavelength grating (SWG) waveguides were integrated into the sensing arm to further strengthen light-matter interaction. The fabricated devices exhibited stable and well-defined interference fringes, with uniform wavelength shifts that scaled linearly with changes in the surrounding refractive index. Standard a-LT-MZI structures (ΔL = 300 μm) achieved experimental sensitivities of 288.75–301.25 nm/RIU, while SWG-enhanced devices reached 496–518 nm/RIU, confirming the effectiveness of refractive index engineering. Comparative analysis against previously reported MZI-based sensors highlights the competitive performance of the proposed design. By combining the scalability and CMOS compatibility of silicon nitride with the sensitivity and robustness of the a-LT-MZI architecture, this device provides a compact and versatile platform for next-generation lab-on-chip photonic sensors. It holds strong potential for applications in biochemical diagnostics, medical testing, and environmental monitoring. Full article
(This article belongs to the Section Nanoelectronics, Nanosensors and Devices)
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22 pages, 1759 KB  
Review
Tumour-on-Chip Models for the Study of Ovarian Cancer: Current Challenges and Future Prospects
by Sung Yeon Lim, Lamia Sabry Aboelnasr and Mona El-Bahrawy
Cancers 2025, 17(19), 3239; https://doi.org/10.3390/cancers17193239 - 6 Oct 2025
Viewed by 406
Abstract
Ovarian cancer is a highly lethal malignancy, characterised by late-stage diagnosis, marked inter- and intra-tumoural heterogeneity, and frequent development of chemoresistance. Existing preclinical models, including conventional two-dimensional cultures, three-dimensional spheroids, and organoids, only partially recapitulate the structural and functional complexity of the ovarian [...] Read more.
Ovarian cancer is a highly lethal malignancy, characterised by late-stage diagnosis, marked inter- and intra-tumoural heterogeneity, and frequent development of chemoresistance. Existing preclinical models, including conventional two-dimensional cultures, three-dimensional spheroids, and organoids, only partially recapitulate the structural and functional complexity of the ovarian tumour microenvironment (TME). Tumour-on-chip (CoC) technology has emerged as a promising alternative, enabling the co-culture of tumour and stromal cells within a microengineered platform that incorporates relevant extracellular matrix components, biochemical gradients, and biomechanical cues under precisely controlled microfluidic conditions. This review provides a comprehensive overview of CoC technology relevant to ovarian cancer research, outlining fabrication strategies, device architectures, and TME-integration approaches. We systematically analyse published ovarian cancer-specific CoC models, revealing a surprisingly limited number of studies and a lack of standardisation across design parameters, materials, and outcome measures. Based on these findings, we identify critical technical and biological considerations to inform the rational design of next-generation CoC platforms, with the aim of improving their reproducibility, translational value, and potential for personalised medicine applications. Full article
(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)
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15 pages, 2880 KB  
Article
Double-Layered Microphysiological System Made of Polyethylene Terephthalate with Trans-Epithelial Electrical Resistance Measurement Function for Uniform Detection Sensitivity
by Naokata Kutsuzawa, Hiroko Nakamura, Laner Chen, Ryota Fujioka, Shuntaro Mori, Noriyuki Nakatani, Takahiro Yoshioka and Hiroshi Kimura
Biosensors 2025, 15(10), 663; https://doi.org/10.3390/bios15100663 - 2 Oct 2025
Viewed by 291
Abstract
Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, [...] Read more.
Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article
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27 pages, 7591 KB  
Article
Switching Frequency Figure of Merit for GaN FETs in Converter-on-Chip Power Conversion
by Liron Cohen, Joseph B. Bernstein, Roni Zakay, Aaron Shmaryahu and Ilan Aharon
Electronics 2025, 14(19), 3909; https://doi.org/10.3390/electronics14193909 - 30 Sep 2025
Viewed by 330
Abstract
Power converters are increasingly pushing toward higher switching frequencies, with current designs typically operating between tens of kilohertz and a few megahertz. The commercialization of gallium nitride (GaN) power transistors has opened new possibilities, offering performance far beyond the limitations of conventional silicon [...] Read more.
Power converters are increasingly pushing toward higher switching frequencies, with current designs typically operating between tens of kilohertz and a few megahertz. The commercialization of gallium nitride (GaN) power transistors has opened new possibilities, offering performance far beyond the limitations of conventional silicon devices. Despite this promise, the potential of GaN technology remains underutilized. This paper explores the feasibility of achieving sub-gigahertz switching frequencies using GaN-based switch-mode power converters, a regime currently inaccessible to silicon-based counterparts. To reach such operating speeds, it is essential to understand and quantify the intrinsic frequency limitations imposed by GaN device physics and associated parasitics. Existing power conversion topologies and control techniques are unsuitable at these frequencies due to excessive switching losses and inadequate drive capability. This work presents a detailed, systematic study of GaN transistor behavior at high frequencies, aiming to identify both fundamental and practical switching limits. A compact analytical model is developed to estimate the maximum soft-switching frequency, considering only intrinsic device parameters. Under idealized converter conditions, this upper bound is derived as a function of internal losses and the system’s target efficiency. From this, a soft-switching figure of merit is proposed to guide the design and layout of GaN field-effect transistors for highly integrated power systems. The key contribution of this study lies in its analytical insight into the performance boundaries of GaN transistors, highlighting the roles of parasitic elements and loss mechanisms. These findings provide a foundation for developing next-generation, high-frequency, chip-scale power converters. Full article
(This article belongs to the Topic Wide Bandgap Semiconductor Electronics and Devices)
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12 pages, 4545 KB  
Article
Wearable Flexible Wireless Pressure Sensor Based on Poly(vinyl alcohol)/Carbon Nanotube/MXene Composite for Health Monitoring
by Lei Zhang, Junqi Pang, Xiaoling Lu, Xiaohai Zhang and Xinru Zhang
Micromachines 2025, 16(10), 1132; https://doi.org/10.3390/mi16101132 - 30 Sep 2025
Viewed by 394
Abstract
Accurate pressure monitoring is crucial for both human body applications and intelligent robotic arms, particularly for whole-body motion monitoring in human–machine interfaces. Conventional wearable electronic devices, however, often suffer from rigid connections, non-conformity, and inaccuracies. In this study, we propose a high-precision wireless [...] Read more.
Accurate pressure monitoring is crucial for both human body applications and intelligent robotic arms, particularly for whole-body motion monitoring in human–machine interfaces. Conventional wearable electronic devices, however, often suffer from rigid connections, non-conformity, and inaccuracies. In this study, we propose a high-precision wireless flexible sensor using a poly(vinyl alcohol)/single-walled carbon nanotube/MXene composite as the sensitive material, combined with a randomly distributed wrinkle structure to accurately monitor pressure parameters. To validate the sensor’s performance, it was used to monitor movements of the vocal cords, bent fingers, and human pulse. The sensor exhibits a pressure measurement range of approximately 0–130 kPa and a minimum resolution of 20 Pa. At pressures below 1 kPa, the sensor exhibits high sensitivity, enabling the detection of transient pressure changes. Within the pressure range of 1–10 kPa, the sensitivity decreases to approximately 54.71 kPa−1. Additionally, the sensor demonstrates response times of 12.5 ms at 10 kPa. For wireless signal acquisition, the pressure sensor was integrated with a Bluetooth chip, enabling real-time high-precision pressure monitoring. A deep learning-based training model was developed, achieving over 98% accuracy in motion recognition without additional computing equipment. This advancement is significant for streamlined human motion monitoring systems and intelligent components. Full article
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20 pages, 2506 KB  
Article
Design of an RRAM-Based Joint Model for Embedded Cellular Smartphone Self-Charging Device
by Abhinav Vishwakarma, Anubhav Vishwakarma, Matej Komelj, Santosh Kumar Vishvakarma and Michael Hübner
Micromachines 2025, 16(10), 1101; https://doi.org/10.3390/mi16101101 - 28 Sep 2025
Viewed by 777
Abstract
With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage [...] Read more.
With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage current. RRAM devices are recently used extensively in applications such as self-charging wireless sensor networks and storage elements, owing to their intrinsic non-volatility and multi-bit capabilities, making them a potential candidate for mitigating the von Neumann bottleneck. We propose a new RRAM-based hybrid memristor model incorporated with a permanent magnet. The proposed design (1T2R) was simulated in Cadence Virtuoso with a 1.5 V power supply, and the finite-element approach was adopted to simulate magnetization. This model can retain the data after the power is off and provides fast power on/off transitions. It is possible to charge a smartphone battery without an external power source by utilizing a portable charger that uses magnetic induction to convert mechanical energy into electrical energy. In an embedded smartphone self-charging device this addresses eco-friendly concerns and lowers environmental effects. It would lead to the development of magnetic field-assisted embedded portable electronic devices and open the door to new types of energy harvesting for RRAM devices. Our proposed design and simulation results reveal that, under usual conditions, the magnet-based device provide a high voltage to charge a smartphone battery. Full article
(This article belongs to the Special Issue Self-Tuning and Self-Powered Energy Harvesting Devices)
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13 pages, 5338 KB  
Article
High-Performance Silicon Nanowire Array Biosensor for Combined Detection of Colorectal Cancer Biomarkers
by Jiaye Zeng, Mingbin Liu, Xin Chen, Jintao Yi, Wenhe Liu, Xinjian Qu, Chaoran Liu, Serestina Viriri, Guangguang Yang, Weichao Yang and Xun Yang
Micromachines 2025, 16(10), 1089; https://doi.org/10.3390/mi16101089 - 26 Sep 2025
Viewed by 430
Abstract
This study presents a high-performance silicon nanowire (SiNW) array biosensor for the combined detection of two key colorectal cancer (CRC) biomarkers: circulating tumor DNA (ctDNA) and carcinoembryonic antigen (CEA). The device was fabricated using conventional micromachining techniques, enabling the integration of dual SiNW [...] Read more.
This study presents a high-performance silicon nanowire (SiNW) array biosensor for the combined detection of two key colorectal cancer (CRC) biomarkers: circulating tumor DNA (ctDNA) and carcinoembryonic antigen (CEA). The device was fabricated using conventional micromachining techniques, enabling the integration of dual SiNW arrays on a single chip with precise control over structure and surface functionalization. Specific probe DNA and anti-CEA antibodies were immobilized on distinct array regions to facilitate targeted binding. The biosensor demonstrated exceptional performance, achieving an ultralow detection limit of 10 aM for ctDNA with a linear range from 0.1 fM to 10 pM, and a sensitivity of 1 fg/mL for CEA. It exhibited high selectivity against interfering substances, including single-base mismatched DNA and non-specific proteins, and maintained robust performance in human serum samples. The platform offers a scalable, label-free, and real-time detection solution with significant potential for application in early CRC screening and personalized medicine. Full article
(This article belongs to the Special Issue Advanced Micro- and Nano-Manufacturing Technologies, 2nd Edition)
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13 pages, 2717 KB  
Article
Learning Dynamics of Solitonic Optical Multichannel Neurons
by Alessandro Bile, Arif Nabizada, Abraham Murad Hamza and Eugenio Fazio
Biomimetics 2025, 10(10), 645; https://doi.org/10.3390/biomimetics10100645 - 24 Sep 2025
Viewed by 313
Abstract
This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, [...] Read more.
This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making. Full article
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29 pages, 5223 KB  
Review
Multifunctional Colloidal Quantum Dots-Based Light-Emitting Devices for On-Chip Integration
by Ruoyang Li, Jie Zhao, Yifei Qiao, Xiaoyan Liu and Shiliang Mei
Nanomaterials 2025, 15(18), 1422; https://doi.org/10.3390/nano15181422 - 16 Sep 2025
Viewed by 828
Abstract
Colloidal quantum dots (CQDs) have attracted significant attention in optoelectronics due to their size-tunable bandgap, high photoluminescence quantum yield, and solution processability, which enable integration into compact and energy-efficient systems. This review consolidates recent progress in multifunctional CQD-based light-emitting devices and on-chip integration [...] Read more.
Colloidal quantum dots (CQDs) have attracted significant attention in optoelectronics due to their size-tunable bandgap, high photoluminescence quantum yield, and solution processability, which enable integration into compact and energy-efficient systems. This review consolidates recent progress in multifunctional CQD-based light-emitting devices and on-chip integration strategies. This review systematically examines fundamental CQD properties (quantum confinement, carrier dynamics, and core–shell heterostructures), key synthesis methods including hot injection, ligand-assisted reprecipitation, and microfluidic flow synthesis, and device innovations such as light-emitting field-effect transistors, light-emitting solar cells, and light-emitting memristors, alongside on-chip components including ongoing electrically pumped lasers and photodetectors. This review concludes that synergies in material engineering, device design, and system innovation are pivotal for next-generation optoelectronics, though challenges such as environmental instability, Auger recombination, and CMOS compatibility require future breakthroughs in atomic-layer deposition, 3D heterostructures, and data-driven optimization. Full article
(This article belongs to the Section Nanophotonics Materials and Devices)
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