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PenAAc-based interfacial passivation to reduce voltage loss and defect densities in lead-free perovskite solar cells

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Journal of Applied Physics 2025年第21期137卷

作者： Guo, Jun Liu, Yu Chen, Xinyao Cheng, Jin Zhao, Longjiang Li, Junming Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory for Sensor School of Science Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China College of Engineering Qufu Normal University Rizhao276826 China

Tin-based perovskites are considered a promising alternative to lead-based perovskites due to their low toxicity and suitable optical bandgap. However, the defects and voltage loss issues severely limit their device performance. In this study, an interfacial passivation strategy based on pentylammonium acetate (PenAAc) was proposed in tin-based perovskite solar cells. The PenAAc-modified layer can effectively improve the perovskite film morphology, crystallinity, and hydrophobicity. Meanwhile, the introduction of the PenAAc layer can significantly inhibit carrier complexation and reduce the hysteresis effect in solar cells. By systematically analyzing the charge transport and compounding mechanisms, the PenAAc layer enhances the built-in electric field, improves the carrier separation and the carrier extraction efficiency, and reduces the defect density in the perovskite layer. The optimal tin-based perovskite solar cell achieved a power conversion efficiency of 7.0%. Moreover, the PenAAc-modified device maintained above 89% of its initial efficiency even after 1200 h storage in a nitrogen-filled glovebox. © 2025 Author(s).

关键词： Defect density

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction 22

A Robust Convolutional Neural Network for Electrical Impedan...

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22nd IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2025

作者： Hafsa, Mariem Lin, Kailun Ben Amara, Najoua Essoukri Kanoun, Olfa Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse 4023 Tunisia Technische Universität Chemnitz Professorship of Measurement and Sensor Technology Chemnitz Germany Technische Universität Chemnitz Department of Mechanical Engineering Chemnitz Germany

ISBN: (纸本)9798331542726

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging. © 2025 IEEE.

关键词： Convolutional Neural Network Electrical Impedance Tomography Experimental validation Image Reconstruction

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Simulation of High Temperature Thin Film Thermocouple on Engine Blade Surface

Simulation of High Temperature Thin Film Thermocouple on Eng...

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2022 International Conference on Mechatronics and Automation technology, ICMAT 2022

作者： Zhang, Meiju Tian, Bian Ma, Rong Huang, Manguo Liang, Xiaobo Liu, Jiangjiang Zhang, Zhongkai Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute Beijing101111 China Aviation Key Laboratory of Science and Technology on Special Condition Monitoring Sensor Technology Beijing101111 China State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9781643683669

In order to avoid the instability flutter of aero-engine blade and prevent the failure of thin film thermocouple caused by the blade in complex environment, the structural strength of the thermocouple on the actual blade surface was studied in this paper. Firstly, based on the 3D scanning of the real blade, the blade model is established after the point cloud graphics. The surface thermocouple model of the blade is established in Solidworks for simulation, and the modal and harmonic response analysis of the blade is simulated. Secondly, the vibration of the curved thin film thermocouple and the stress distribution of the blade under the acceleration shock were studied in Comsol, and the failure weakness was analyzed to avoid the failure of the thin film thermocouple caused by the bending, fracture or shedding of the thin film thermocouple on the blade due to vibration and shock, providing the most suitable thermocouple selection for the engine blade. © 2022 The authors and IOS Press.

关键词： Thermocouples

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Effect of Chloride Ion Concentrations on Luminescence Peak Blue Shift of Light-Emitting Diode Using Anti-Solvent Extraction of Quasi-Two-Dimensional Perovskite

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Tsinghua Science and technology 2021年第4期26卷 496-504页

作者： Baoyu Liu Xiaoping Zou Dan Chen Taoran Liu Yuhua Zuo Jun Zheng Zhi Liu Buwen Cheng Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Key Laboratory for SensorMOE Key Laboratory for Modern Measurement and Control TechnologyBeijing Information Science and Technology UniversityBeijing 100101China the State Key Laboratory on Integrated Optoelectronics Institute of SemiconductorChinese Academy of SciencesBeijing 100083China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

In recent years,Perovskite Light-Emitting Diodes(PeLEDs)have received considerable attention in ***,with the development of PeLEDs,commercial applications of full-color PeLED technology are largely limited by the progress of blue-emitting devices,due to the uncontrollably accurate composition,unstable properties,and low *** this article,we add Cesium chloride(CsCl)to the quasi-two-dimensional(quasi-2D)perovskite precursor solution and achieve the relatively blue shifts of PeLED emission peak by introducing chloride ions for photoluminescence(PL)and electroluminescence(EL).We also found that the introduction of chlorine ions can make quasi-2D perovskite films thinner with smoother surface of 0.408 *** is interesting that the EL peaks and intensities of PeLED are adjustable under different driving voltages in high concentration chlorine-added perovskite devices,and different processes of photo-excited,photo-quenched,and photo-excited occur sequentially with the increasing driving *** work provides a path for demonstrating full-color screens in the future.

关键词： Perovskite Light-Emitting Diodes(PeLEDs) quasi-two-dimensional(quasi-2D) chloride ions blue shifts

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Advances in wearable and implantable devices for wireless electrical stimulation therapy

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Discover Electronics 2025年第1期2卷 1-18页

作者： Zhang, Yue Wang, Yijing Zhao, Kun Yang, Minye Ye, Zhilu Zhang, Xiaohui State Key Laboratory for Manufacturing Systems Engineering Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education Engineering Research Center of Spin Quantum Sensor Chips Universities of Shaanxi Province School of Electronic Science and Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory for Manufacturing Systems Engineering The Key Laboratory of Biomedical Information Engineering of Ministry of Education Center for Mitochondrial Biology and Medicine School of Life Science and Technology International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi’an Key Laboratory for Biomedical Testing and High-End Equipment Xi’an Jiaotong University Xi’an China

Wireless electrical stimulation devices have gained considerable attention due to their potential to offer therapeutic benefits in various medical applications, including pain management, neurological rehabilitation, and tissue regeneration. Traditionally, electrical stimulation is delivered through large, hospital-based equipment, limiting patient accessibility and convenience. Recent advancements in wearable and implantable devices, however, have paved the way for in-home electrical stimulation therapies. These devices, designed to be wireless and battery-free, offer greater flexibility, but their development hinges on effective methods of wireless power transfer. This review explores key approaches for wirelessly powering these devices, such as inductive coupling, mechanical energy harvesting, magnetic field induction, and optical power transfer. Each method presents unique advantages and limitations in terms of efficiency, range, and practicality for wearable or implantable use. Additionally, we discuss emerging materials that enhance device performance, including bioabsorbable materials that degrade naturally after use, stretchable materials that improve stretchability, and self-adhesive materials that enhance secure attachment. This review provides a comprehensive overview of the key strategies and materials that are driving the development of more effective and accessible electrical stimulation technologies. These advancements hold great promise for improving patient outcomes and expanding the use of electrical stimulation in both clinical and at-home therapies.

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Pluggable multitask diffractive neural networks based on cascaded metasurfaces

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Opto-Electronic Advances 2024年第2期7卷 23-31页

作者： Cong He Dan Zhao Fei Fan Hongqiang Zhou Xin Li Yao Li Junjie Li Fei Dong Yin-Xiao Miao Yongtian Wang Lingling Huang Beijing Engineering Research Center of Mixed Reality and Advanced Display Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Institute of Modern Optics Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network TechnologyNankai UniversityTianjin 300350China Department of Physics and Optoelectronics Faculty of ScienceBeijing University of TechnologyBeijing 100124China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100191China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing 100076China

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering *** has been considered as one of the powerful tools in promoting the fields of imaging processing and object ***,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems *** push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal *** the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared ***,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

关键词： optical neural networks diffractive deep neural networks cascaded metasurfaces

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Electrical Impedance Tomography Image Reconstruction with apriori Knowledge for Gesture Recognition 19

Electrical Impedance Tomography Image Reconstruction with ap...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Alasasfeh, Hayat Hafsa, Mariem Bader, Oumaima Ibbini, Mohammed Hasan, Sameer Kanoun, Olfa Jordan University of Science and Technology Biomedical Engineering Department Irbid Jordan Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical impedance tomography (EIT) reconstructs the conductivity images of an object based on the voltages and the currents at the surface of the object. Among the clinical applications of EIT thorax imaging, brain function monitoring and breast cancer screening. However, EIT suffers from a poor signal-to-noise ratio and spatial resolution. To improve the quality of reconstructed images and provide clear information about the structure, the paper proposes to include prior knowledge about the structure from MRI. The proposed techniques could be very promising with more work. To illustrate this technique in this paper, eight electrodes with the adjacent current pattern were employed in this research. For each gesture, forty data points are recorded. The Gauss-Newton and Back-Projection algorithms are used for image reconstruction with prior knowledge of the human forearm from MRI images. The resultant contours of the Forearm, Ulna, and Radius have been used to build the mesh. In the Gauss-Newton algorithm, the results reveal that structural information improves image quality and conductivity distribution, particularly in the region of two bones. In addition, the reconstructed images using the Gauss-Newton algorithm performed better than images reconstructed using the back-projection algorithm. © 2022 IEEE.

关键词： Image reconstruction

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Feature engineering for Accurate Low-Power Electrical Impedance Tomography in Hand Sign Recognition

Feature Engineering for Accurate Low-Power Electrical Impeda...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Bilel Ghoul Rim Barioul Salwa Sahnoun Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Ons City Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Achieving high classification accuracy from raw measurements in Electrical Impedance Tomography (EIT) for hand sign recognition is a significant challenge due to the inherent ill-posed nature of the corresponding inverse problem to solve. Energy consumption is an issue, especially for wearable devices, so there is a growing preference to measure EIT with fewer electrodes or fewer current injections. In a previous study, we demonstrated that an 8-electrode hand gesture recognition system could achieve high accuracy with only 5 current injections instead of 8, resulting in significant power savings. In this study, we investigate feature extraction and selection techniques to improve the classification accuracy in the low-power EIT system based on less measurements. We evaluate the performance of 12 statistical features by analyzing the Feature Importance (FI) and identify 8 features with non-zero values: WL, MAV, Kurtosis, VORDER, DASDV, RMS, P2P and VAR. Using K-Nearest Neighbour (KNN), a comparative classification is performed using subsets of the best ranked features ranging from 2 to 8. Compared to the 96% accuracy achieved with raw data, our solution achieves 99.8% accuracy with the 8 extracted features. The results show that feature engineering significantly improves classification accuracy compared to the use of raw data.

关键词： Hands Performance evaluation Electrical impedance tomography Reactive power Accuracy Current measurement Nearest neighbor methods Feature extraction Kurtosis Wearable devices

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Biomimetic Robotic Hand with Highly Sensitive Integrated Nanocomposite Force sensors for Adaptive Grasping

Biomimetic Robotic Hand with Highly Sensitive Integrated Nan...

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International Workshop on Robot Sensing (ROSE)

作者： Ahmed Attaoui Achraf Djemal Bilel Ben Atitallah Asma El Jaoui Olfa Kanoun Professorship Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Mechanics of Sousse National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks National School of Electronics and Telecommunications of Sfax Sfax Tunisia

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

Robotic hands have immense potential for a wide range of applications, but the key challenge is still to achieve human-like dexterity. This requires overcoming several challenges, such as high system complexity, slow response time, and difficulties in integrating effective sensors. In this study, we present a novel robotic hand with 17 degrees-of-freedom equipped with nanocomposite based pressure and strain sensors. Inspired by the human hand's capabilities, the hand's design strives to mimic its dexterity and shape. The implemented nanocomposite sensors have exceptional electrical, mechanical, and electrochemical capabilities, making them ideal for integration into our robotic hand system design. The pressure sensor has a rapid response time, high pressure sensitivity of 4.4 kΩ /kPa, and remains stable over more than 5,000 cycles, demonstrating a high reliability. The strain sensor also maintains good linearity of 0.98 when subjected to strains from 0% to 24.49%. To address the challenge of balancing secure grasping and delicate touch, a finite state machine (FSM) control method is proposed enabling the hand to dynamically adjust its grasping pressure based on the real-time sensor feedback, allowing for precise manipulation of fragile objects. Experiments demonstrate the effectiveness of the proposed approach, paving the way for the development of intelligent robotic systems with enhanced dexterity and adaptability for real-world applications.

关键词： Automata Tactile sensors Grasping Robot sensing systems Capacitive sensors sensor systems sensors

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A Portable Dynamic Path-Length Optical sensor for Phenol Detection

SSRN

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SSRN 2025年

作者： Zhang, Junru Chen, Ying Liu, Junfei Li, Wanwen Zhu, Dandan Niu, Liyong Zhu, Qiguang Hebei Province Key Laboratory of Test/Measurement Technology and Instrument School of Electrical Engineering Yanshan University Hebei Qinhuangdao066004 China Hebei Province Key Laboratory of Special Fiber and Fiber Sensor School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China

A current challenge faced by portable phenol sensors is their susceptibility to environmental factors, which affects their robustness under low-cost constraints. This study develops a low-cost optical sensor for phenol detection. The system utilizes a UV-sensitive photodetector paired with specific wavelength filters for wavelength selection. It further optimizes detection accuracy and robustness through a specially designed dynamic optical path quartz cuvette. Additionally, the use of a dual detection unit and standardized methodology minimizes the interference of external environmental factors on the sensor. Experimental results demonstrate that the detection system exhibits linearity within the range of 0.1 to 5 mg/L, with an average absolute error of 0.1604 in real-world environmental testing. This provides an efficient and economical solution for on-site rapid detection of phenolic wastewater. The sensor has a detection limit of 0.038 mg/L and a total cost of $251.01. © 2025, The Authors. All rights reserved.

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