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Low-Cost Bionic Microstructure-Based Flexible Piezoresistive sensors for Human Signal Detection

advanced MATERIALS TECHNOLOGIES 2025年

作者： Zhang, Yingying Gao, Leyi Luo, Kejia Yang, Yun xue, Shuping Zhang, Jing Lyuliang Univ Dept Resource & Mech Engn Lvliang 033001 Peoples R China

Various microstructure designs are frequently employed to optimize flexible sensor performance. However, the replication process of microstructures is complex, the required equipment is costly, and the production costs remain relatively high. In this study, A method involving multiple coatings of a mixed carbon solution is employed to replicate the microstructure of the leaf surface. Flexible piezoresistive sensors with microstructures resembling those of corn husks, lucky bamboo leaves, and cattail leaves are fabricated. The fabrication process is straightforward, and the production cost is low. Morphological observations and performance testing reveal that the flexible piezoresistive sensor with the corn husk-like microstructure exhibits the best sensing performance: a sensitivity of 0.0128 kPa-1 in the pressure range of 0-18 kPa. The response and recovery times are 151 +/- 12 ms and 196 +/- 28 ms, respectively, with a pressure range of 0-178 kPa, stability over 10, 000 loading-unloading cycles and a hysteresis error of 8.05%. The fabricated flexible piezoresistive sensors can be utilized to detect human body signals such as frowning, finger bending, and foot pressure. These sensors hold potential for applications in fields such as motion monitoring, healthcare, and flexible robotic systems.

关键词： bionic microstructure flexible piezoresistive sensor graphene multi-walled carbon nanotubes (MWCNTs)

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Sustainable and Tunable Synaptic Electrolyte-Gated Organic Field-Effect Transistors (EGOFETs) for Light Adaptive Visual Perceptive systems

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advanced FUNCTIONAL MATERIALS 2025年第11期35卷

作者： Serghiou, Theodoros Fernandes, Jose Diego Karthikeyan, Vaithinathan Assi, Dani S. Vieira, Douglas Henrique Alves, Neri Kettle, Jeff Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Sao Paulo State Univ UNESP Sch Technol & Appl Sci Dept Phys BR-19060900 Presidente Prudente SP Brazil Hong Kong Metropolitan Univ Sch Sci & Technol Ho Man Tin 11-F Hong Kong Peoples R China

The recent advances in optic neuromorphic devices have led to a subsequent rise in the development of energy-efficient artificial-vision systems. While the energy consumption of such devices is known to be much lower than conventional vision systems, it is known that manufacturing accounts for the largest share of the climate impact in microelectronics, dominating over the product use phase. Thus, there is a need to develop sustainable manufacturing processes and to adopt low-impact materials for hardware solutions of the future. In this study, an Electrolyte-Gated Organic Field-effect Transistor (EGOFET) is experimentally demonstrated for the implementation of a high-performing synaptic optical sensor using sustainable materials that degrade to benign products at the End of Life (EoL). The device shows remarkable light response with maximum Paired-Pulse Facilitation (PPF) Index of up to 151% at a light power density of 38 mu W cm-2, which enables artificial synaptic applications with an average power consumption as low as 2.4 pJ for each training process, representing one of the best among the reported results. To demonstrate the tunability of the vision system, an ensemble decision tree is used to enable the EGOFET to distinguish and remember different primary colors at different power densities with 95.6% accuracy.

关键词： electro-gated transistors neuromorphic imaging system optical synaptic devices organic phototransistors sustainable materials

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Advancements, challenges, and opportunities in the measurement of high heat flux for concentrated solar thermal systems

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SOLAR ENERGY 2025年 287卷

作者： Abdullah, Muhammad Corona, Brenda Hernandez Martins, Matthieu Ferber, Nicolas Lopez Armstrong, Peter Chiesa, Matteo Calvet, Nicolas Khalifa Univ Masdar Inst Solar Platform Mech & Nucl Engn Dept POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Ind & Syst Engn Dept POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Res Labs POB 127788 Abu Dhabi U Arab Emirates ENOWA NEOM Energy Div Flexibil Team NEOM Gayal Saudi Arabia

Concentrated solar thermal (CST) systems, capable of driving high-temperature processes up to 2000 K, hold significant potential for chemical and industrial applications. This paper reviews methods for measuring concentrated solar flux, focusing on their role in calculating key optical performance metrics such as concentration ratio and optical efficiency. It also explores the challenges associated with these measurements. Flux measurement methods are categorized into direct and indirect approaches. Direct methods use heat flux gauges positioned on a stationary or moving target to measure solar flux, while indirect methods rely on a CCD or CMOS camera, a Lambertian target, and a heat flux gauge. The camera captures images of solar radiation, which are calibrated to heat flux values using readings from the heat flux gauge. Heat flux gauges are a critical component of both direct and indirect measurement methods. This study also reviews and compares various types of heat flux gauges, including Gardon radiometers, Schmidt-Boelter gauges, Kendall radiometers, heat flux microsensors, and calorimeters. The comparison considers factors such as maximum flux rating, response time, durability, water-cooling requirements, cost, and measurement uncertainty. Calibration techniques for these gauges are also discussed. It is observed that indirect methods are commonly used for measuring concentrated solar flux due to their higher resolution, which enables them to identify hot spots and measure concentrated solar flux more accurately than direct methods. The key challenges in measuring concentrated solar flux include calibrating sensors under solar conditions, ensuring sensor durability, accounting for environmental factors, and managing cost considerations.

关键词： Concentrated solar thermal systems Heat flux measurement Indirect method Calorimeter Heat flux gauge

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A miniature photonic integrated pressure sensor based on a suspended waveguide Bragg grating 24

A miniature photonic integrated pressure sensor based on a s...

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2025 conference on MOEMS and Miniaturized systems

作者： Kannojia, Harindra Kumar Geudens, Viktor Van Steenberge, Geert Syriopoulos, Georgios Kyriazi, Evrydiki Poulopoulos, Ioannis Prousalidi, Thenia Zervos, Charalampos Avramopoulos, Hercules Missinne, Jeroen Univ Ghent Ctr Microsyst Technol CMST Technologiepk 126 B-9052 Zwijnaarde Belgium imec Technol Pk 126 B-9052 Zwijnaarde Belgium Natl Tech Univ Athens Photon Commun Res Lab 9 Iroon Polytechn St Athens 15773 Greece

ISBN: (数字)9781510685130

ISBN: (纸本)9781510685123;9781510685130

This paper presents the development of a miniature photonic integrated pressure sensor based on a suspended waveguide Bragg grating. The sensor leverages microfabrication techniques to create an ultra-compact sensor die, that interfaces seamlessly with a single optical fiber. The design features a silicon waveguide Bragg grating integrated onto a circular membrane, which is pressure-sensitive. The photonic circuit involving the Bragg grating sensor, interconnecting silicon waveguide and grating coupler was fabricated using the ISIPP50G technology and underwent post-processing to create the suspended Bragg grating sensors. The post-processing involved bonding the photonic integrated circuit (PIC) to a fused silica lid with cylindrical cavities, followed by the removal of the silicon substrate to form an approximately 10 mu m thick oxide membrane. The cylindrical cavities were created using femtosecond laser-induced chemical etching, and the PIC was bonded to the fused silica carrier using an adhesive layer. The sensor's performance was characterized using a custom-designed clamping setup, demonstrating a clear shift in the reflection spectrum of the Bragg grating as a function of applied pressure making it a promising candidate for applications in environments where traditional electrical sensors are unsuitable.

关键词： Bragg Grating Membrane Microfabrication Photonic Integrated Circuit (PIC) Pressure sensor

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Moko: Marrying Python with Big Data systems 25

Moko: Marrying Python with Big Data Systems

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20th European conference on Computer systems-EuroSys

作者： Meng, Ke He, Tao Shen, Sijie Wang, Lei Yu, Wenyuan Zhou, Jingren Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798400711961

Python stands as the preferred language for data science, thanks to its user-friendly syntax and a robust ecosystem that effortlessly accommodates a variety of data types and workloads, such as relational/tabular data, tensors, and graphs. While Python thrives in smaller data settings, it struggles to scale in distributed big data environments. MOKO is an IR-based execution framework designed to extend Python's reach into the distributed big data domain by generating code that can utilize existing systems such as Spark, Dask, Torch, and GRAPE. MOKO preserves Python's key features-interoperability, ease of use, and support for multi-model data types and workloads-while enabling efficient execution in a distributed setting. Our evaluation indicates that MOKO can accelerate Python applications by up to 11x across diverse systems, diminish data alignment overhead by 28x, and outperform hand-optimized solutions by 2.5x.

关键词： big-data Python scheduling

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The impact of sustainable management systems on the sustainable development of water resources in agriculture and the reduction of agricultural carbon footprint 2

The impact of sustainable management systems on the sustaina...

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2nd International conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Jiang, Zihao University of Illinois Urbana - Champaign Champaign United States

ISBN: (纸本)9781510689053

Environmental and economic Sustainability increasingly demands effective water resource management and reduction of the carbon footprint in agriculture. This meta-analysis synthesizes the results of 18 studies published in 2015-2024 to assess the performance of sustainable management systems in optimizing agricultural water resources and reducing carbon emissions. The review specifically focuses on integrating the Internet of Things and Artificial Intelligence into agricultural water management, employing advanced computational methods, including text clustering algorithms, support vector machines (SVM), and deep learning models for analysis. Results show that IoT-driven water management systems achieve, on average, a water economy of 27.3% (95% CI: 24.1-30.5%) against conventional approaches and reduce agricultural emissions by approximately 31.2% (95% CI: 28.4-34.0%). AI-driven optimization algorithms demonstrate remarkable effectiveness;machine learning algorithms provide an average increase in water use efficiency by 35.6% in precision irrigation applications, while deep learning models achieve 42.3% accuracy in crop water requirement predictions. The computational analysis revealed that integrated IoT-AI systems show the highest potential for agricultural Sustainability, with neural network models achieving 89.5% accuracy in resource optimization predictions. The results, therefore, show that large-scale diffusion of these technologies can make major contributions to agricultural sustainability goals while sustaining or improving crop yields. © 2025 SPIE.

关键词： Carbon capture and utilization

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Development of FBG Miniature Force sensors for Tactile Sensing in Robot-Assisted Minimally Invasive Surgery 4

Development of FBG Miniature Force Sensors for Tactile Sensi...

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4th International Computational Imaging conference, CITA 2024

作者： Dong, Shiyuan xiong, xuanwei Lou, Yuyang Luo, Dong Wu, Jianwei Yang, Tianyu Liu, Huanhuan Dong, Yuming School of Physics and Electronic Engineering Chongqing Normal University Chongqing401331 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Key Laboratory of Optoelectronic Technology and Systems Chongqing University Chongqing400044 China

ISBN: (纸本)9781510688834

The present study introduces a miniature axial force sensor based on Fiber Bragg Grating (FBG) technology, specifically developed for detecting abnormalities within tissues during robot-assisted minimally invasive surgeries. This FBG force sensor is composed of a single optical fiber engraved with an FBG element, a sensor cap, an elastomer, and a sensor base. The unique structural design of the elastomer enhances sensitivity to axial forces while effectively mitigating interference from transverse forces. Unlike traditional methods that directly bond the FBG, the fiber in this design is suspended tightly at both ends of the elastomer. This approach addresses the issue of uneven adhesive thickness in the FBG region and prevents chirp-related failures. The sensor boasts a compact design with a diameter of only 1.5 mm and a length of 10 mm, making it easily integrable into surgical robots and overcoming the limitations posed by larger sensors in previous designs. Finally, a force calibration experiment was conducted, determining the sensor's sensitivity to be 276 pm/N within a force range of 0-1 N. © 2025 SPIE.

关键词： Optical fibers

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 IEEE.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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Incorporating LoRa Based Wireless sensor Network and Machine Learning Technologies to Improve Precision Agriculture 3rd

Incorporating LoRa Based Wireless Sensor Network and Machine...

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3rd International conference on Intelligent systems and applications, ICISA 2024

作者： Shaikh, Aman Ranjan, Nihar M. Rai, Satayush Rao, Pranil Ashok Shinde, Ganesh Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Pimpri-Chinchwad India

ISBN: (纸本)9789819798384

In the contemporary landscape, the agricultural sector has undergone transformational changes, embracing progressive dynamics through the integration of cutting-edge technologies and methodologies. The global adoption of IoT, AI/ML, and similar innovations reflects their capacity to furnish precise, reliable, and consistent outcomes, enabling data-driven decision-making. The amalgamation of IoT and ML models emerges as a strategic choice to enhance data analytics, aiming at a substantial improvement in both the quantity and quality of agricultural yields. This paper seeks to elucidate the synergistic interaction of various agrarian elements, orchestrating the computational prowess facilitated by ML models and data derived from IoT sensors transmitted through LoRa-based Wireless sensor Network (WSN) infrastructure. The proposed system advocates a comprehensive three-tier architectural framework, wherein ML and IoT systems collaborate seamlessly to process data for field management. The envisioned system extends its utility by translating these predictions into visually comprehensible representations. The integrated approach signifies a departure from conventional methods, offering a novel pathway to ensure the sustainability of yield, encompassing both production and grading standards. This paper thus contributes to the discourse on leveraging advanced technologies for agricultural optimization in response to evolving global demands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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High-Resolution Forward-Looking MIMO-SAR Imaging Approach for Automotive mmwave Radar 16

High-Resolution Forward-Looking MIMO-SAR Imaging Approach fo...

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2024 16th International conference on Graphics and Image Processing, ICGIP 2024

作者： Wang, Jiping Liu, Yanqi Deng, Qianyu Guo, Shuting Mao, xinhua Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Leveraging the widespread applications of Synthetic Aperture Radar (SAR) imaging technology and Multiple Input Multiple Output (MIMO) arrays in millimeter wave radar, it is now possible to obtain high-resolution forward-looking (FL) images. Forward-Looking Multiple-Input Multiple-Output Synthetic Aperture Radar (FL-MIMO-SAR) represents the state-of-the-art technology for achieving high-resolution imaging of the area in front of a vehicle. This advanced imaging capability is crucial for numerous applications, including autonomous driving, advanced driver assistance systems (ADAS), as well as surveillance and reconnaissance. However, traditional forward-looking synthetic aperture radar is characterized by a left-right ambiguity problem, while MIMO array real aperture imaging struggles with low resolution in side-view regions. To address these challenges, this paper proposes a joint imaging method for FL-MIMO-SAR. This method utilizes MIMO arrays to resolve the left-right ambiguity problem, achieving high-resolution imaging in the forward-looking region. In the squint region, higher resolution can be obtained by synthetic aperture processing because the length of the synthetic aperture is usually larger than the actual aperture. Furthermore, this paper thoroughly analyzes the amplitude and phase errors of MIMO arrays and their impact on imaging, proposing an effective calibration method to address these issues. Finally, the effectiveness of the proposed algorithm is validated through experiments with real measurement data. © 2025 SPIE.

关键词： advanced driver assistance systems

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