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Investigation of Short-Circuit Current density in Non-Fullerene-Based Ternary Organic Solar Cells by Incorporating Machine Learning Algorithms with Effective descriptors

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Solar RRL 2025年第10期9卷

作者： Lee, Min-Hsuan Chen, Ying-Chun Chang, Yi-Ming Hou, Bo Ph.D. Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu Taiwan Institute of Environmental and Occupational Health Sciences School of Medicine National Yang-Ming Chiao Tung University Taipei Taiwan Institute of Environmental Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Raynergy Tek Incorporation Hsinchu Taiwan School of Physics and Astronomy Cardiff University Cardiff United Kingdom

Non-fullerene acceptor (NFA)-based ternary organic solar cells (OSCs) are emerging as promising devices for converting sunlight into electricity, contributing to environmental solutions. However, selecting the third component remains a significant challenge, as it plays a critical role in achieving high short-circuit current density (Jsc) in NFA-based ternary OSCs (comprising donors, acceptors, and the third component). Traditional trial-and-error experimental methods face substantial limitations, including high energy consumption, cost, and time demands, which may not be sufficient for investigating the quantitative relationships between material properties and Jsc in ternary OSCs. In this study, we examine the effects of the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) energy gap (ΔHOMO and ΔLUMO) between different organic materials, considering these as effective molecular descriptors, on the primary ph.tovoltaic parameter (Jsc) in NFA-based ternary OSCs. The eXtreme Gradient Boosting (XGBoost) algorithm yields reasonable predictions, with an R2 value of 0.76. Additionally, three NFA-based ternary OSCs are fabricated and characterized experimentally to validate the predictions made by the proposed model. Using three different NFA-based ternary OSCs as inputs, the model demonstrates good predictive accuracy for Jsc values. The proposed interpretable model and effective molecular descriptors offer a practical machine-learning approach for accelerating the development of NFA-based ternary OSCs with targeted Jsc values and can also be extended to other organic electronic applications. © 2025 Wiley-VCH GmbH.

关键词： Learning algorithms

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-dimensional Neuroimaging data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kuan-Cheng Chen Yi-Tien Li Tai-Yu Li Chen-Yu Liu Po-Heng Henry Lee Cheng-Yu Chen Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan National Center for High-Performance Computing NARlabs Hsinchu Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Medical Imaging Taipei Medical University Hospital Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Taiwan

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s disease Neuroimaging Initiative and Neuroimaging in Frontotemporal dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

关键词： Neuroimaging Support vector machines Quantum computing Accuracy Magnetic resonance imaging Pipelines Machine learning Medical services Signal processing Standards

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Advanced highly precise simultaneous electrochemical detection of toxic azo dyes with Nanoengineered yttrium Iron oxide decorated functionalized carbon nanofibers

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Food Chemistry 2025年 486卷 144607页

作者： Senthilkumar, dhayanithi Kuo, Chih-Yu Aldossari, Samar A. Govindasamy, Mani National Taipei University of Technology Taipei City10608 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei10608 Taiwan Taipei10608 Taiwan Department of Chemistry P. O. Box 2455 College of Science King Saud University Riyadh11451 Saudi Arabia International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Tamil Nadu Chennai602105 India Department of Chemistry Korea University Seoul Seongbuk-gu Korea Republic of

It is essential to monitor the concentration of synthetic coloring agents in food and beverages due to their potential severe side effects on human health. In this study, yttrium iron oxide (YFO) nanoparticles were synthesized via a hydrothermal method, followed by modification with functionalized carbon nanofibers (f-CNF) through a simple sono-chemical process. The synthesized YFO/f-CNF was thoroughly characterized using various analytical techniques. Concurrent electrochemical detection of sunset yellow (SSY) and tartrazine (TRT) was achieved using a glassy carbon electrode modified with the YFO/f-CNF nanocomposite, enabling highly effective detection of SSY and TRT in food samples. Under optimized conditions, the modified electrode demonstrates a wide linear range (0.05 μM – 466.15 μM) and very low limits of detection (LOd) for SSY (1.5 nM) and TRT (3.2 nM). Additionally, the modified electrode exhibits significantly higher sensitivity (7.23 μAμM1cm−2) for SSY and (3.01 μAμM−1cm−2) for TRT compared to other reported values. This work showcases excellent sensitivity and selectivity, capable of identifying trace amounts of azo dyes in complex food matrices such as fruit juices, soft drinks, and packaged foods. The sensor's performance in real food samples, including both beverages and solid food products, was comparable to traditional high-performance liquid chromatograph. (HPLC), with a strong correlation in detection limits and accuracy (95.7–99.1 %). Furthermore, the sensor demonstrated exceptional stability and reproducibility in food matrix analysis, providing a reliable and efficient tool for routine monitoring of azo dye contamination in food and beverage industries. © 2025

关键词： Fruit juices

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Advanced ZnFe₂O₄@f-CNF electrode: A robust electrochemical sensor for Tert-butyl hydroquinone detection in food and edible oil

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Food Chemistry 2025年 486卷 144648页

作者： Yemmi, Rajeshwari Swamy, B.E. Kumara Manjunath, Kumbar Mudakappa Yusuf, Kareem Aljuwayid, Ahmed M. Govindasamy, Mani 577451 India Division of Plant Breeding and Genetics Central Coffee Research Institute Karnataka Chikkamagaluru577117 India Department of Chemistry College of Science King Saud University Riyadh11451 Saudi Arabia International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Department of Chemistry Korea University Seoul Seongbuk-gu Korea Republic of

Fabrication of a binary electrocatalyst is expounded in this study, which is based on a biocompatible ZnFe2O4 nanoparticles that is adorned onto the functionalized carbon nanofiber (f-CNF) through a straightforward ultrasonication technique. This electrocatalyst is applied for the reliable quantification of the food additive Tert-Butyl Hydroquinone (TBHQ) from various food samples and products. The electrochemical performance of the ZnFe2O4@f-CNF composite is investigated using cyclic voltammetry and electrochemical impedance spectroscopy analysis. The structural features of the composite, characterized by X-ray diffraction, scanning electron microscopy and transmission electron microscopy which reveals its crystalline and porous attributes. Then electrochemical characteristics of the ZnFe2O4@f-CNF composite for TBHQ detection is evaluated employing cyclic voltammetry and differential pulse voltammetry methodologies. Notably, this composite demonstrates a remarkable low detection limit (LOd) of 0.026 μM with a good linear detection range, accompanied by a favorable sensitivity towards TBHQ. Lastly, the developed sensor system manifests a substantial recovery range for real sample analysis, suggesting its potential as a viable alternative to existing diagnostic tools for TBHQ detection. © 2025 Elsevier Ltd

关键词： Cyclic voltammetry

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PVA-based bioinks for 3d bioprinting: A comprehensive review of their applications in tissue engineering

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Bioprinting 2025年 49卷

作者： Narges Johari Zary Adabavazeh Francesco Baino Materials Engineering Group Golpayegan College of Engineering Isfahan University of Technology Golpayegan Iran Ph.D. Program Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taiwan ROC Institute of Materials Physics and Engineering Department of Applied Science and Technology Politecnico di Torino Torino Italy

3d bioprinting is an innovative approach that overcomes the limitations of traditional methods for creating cell-laden biomaterials and constructs. It allows for the fabrication of complex and biologically active tissue structures. This review aims to provide a comprehensive evaluation of polyvinyl alcohol (PVA)-based bioinks within a 3d bioprinting framework. PVA-based bioinks exhibit remarkable properties, such as biocompatibility, biodegradability, and the ability to enhance cell growth and differentiation. These properties make them appropriate for many tissue engineering applications. The study evaluates the ph.sicochemical and biological properties of PVA bioinks, including how they combine with other materials such as gelatin, chitin, chitosan, alginate, agarose, cellulose, κ-carrageenan, methacrylate, nanoparticles, mineral additives, carbon nanotubes, graph.ne oxide, and extracellular matrix components. Furthermore, this review evaluates the benefits of market availability and enhanced printing resolution, in addition to the challenges posed by complexity, dependency on support baths, and the risk of contamination. The objective of this review is to draw attention to the capabilities of PVA-based bioinks and provide guidelines for future research to improve the effectiveness of these materials in tissue engineering and regenerative medicine.

关键词： Bioprinting Bioink Polyvinyl alcohol PVA-based bioinks

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Impact of Scheduled Acceleration on Weekends on Labor Productivity-Case Studies of Construction in Taiwan

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Journal of the Chinese Institute of Civil and Hydraulic engineering 2020年第1期32卷 23-33页

作者： Lin, Chien-Liang Lai, Yu-Che Construction Engineering National Kaohsiung University of Science and Technology Kaohsiung81164 Taiwan Ph. D Program in Engineering Science and Technology National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan

Most previous research has revealed that labour efficiency deteriorates with the increase of days worked, especially for schedule acceleration carried out 6 or 7 days per week. It means that labor productivity on weekdays is better than on weekends. However, the ph.nomenon may not be universally applicable to everywhere. To clarify the impact of schedule acceleration on weekends on labor productivity, this study investigates the efficiency of acceleration from three construction projects in Taiwan. data Envelopment Analysis (dEA) was utilized to examine work hours as the input and the ph.sical quantity of completed work as the output. Further interview for construction workers was performed. The results revealed that (1) acceleration on weekends will not cause the deduction of labor productivity in construction industry in Taiwan;(2) inefficiency was not found on Saturdays because labors are used to work overtime and get higher pay in construction industry in Taiwan;(3) except for getting higher pay, better efficiency was measured on Sundays because the effect of less congestion. The main contribution of this paper is to reveal that the labor productivity on weekends could be better than on weekdays, and further investigate the reasons of the result. Not only this finding reverses previous perspective from construction industry and academic, but also it implies that labor productivity will be influenced by many factors. Furthermore, it reveals the importance of continuing planning and management. In the process of revising Labor Standard Act, the results can provide insights for legislator and industry practitioners in appropriately allocating human resources. © 2020, Chinese Institute of Civil and Hydraulic engineering. All right reserved.

关键词： Construction industry

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Boost dC-dC converter with regenerative snubber

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Energy Reports 2024年 12卷 798-819页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typically include a front-end boost converter as the first stage and a second-stage isolated converter. In this paper, a new topology for a regenerative snubber in boost converters is proposed. Compared to traditional active or passive snubber topologies, the proposed snubber offers the advantages of having fewer components and occupying less space. Moreover, it does not require additional active devices and can be driven by widely-used commercial PWM control integrated circuits, achieving a higher level of cost-effectiveness and offering increased industrial applications. The final prototype of a power supply for a networking switch hub (as used in 5 G base stations) is fabricated to demonstrate the feasibility of the proposed method. © 2024 The Authors

关键词： Topology

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The Indoor People Tracking and Counting System

The Indoor People Tracking and Counting System

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2023 International Automatic Control Conference, CACS 2023

作者： Lin, Hao-Pu Han, Chin-Chuan Liao, Jun-Wei Ciou, Chong-Han Hong, Yi-Cheng Tan, Ming-Lun National United University Ph. D. Program in Material and Chemical Engineering MiaoLi Taiwan National United University Department of Computer Science and Information Engineering MiaoLi Taiwan

ISBN: (纸本)9798350306354

COVId-19 outbreaks and becomes serious from 2019 winter. Taking body temperature, wearing masks, recording footprint and avoiding crowds become important tasks and require a lot of manpower support for the prevention of the epidemic in every. In this study, we have integrated the infrared thermometer devices to measure people temperature and the people identities through RGB images at the entrance of buildings. Using the computer vision and deep learning methods, we would like to build a 24/7 and region-wide coverage assistance system to automatically record the footprint of a specific people in the building. We also detect if people taking off their masks in the yard at any time. The processing videos are grabbed from the existing CCTV systems. The developed system also locates the crowd and calculate the number of people in a specified area, and the manager can know the number of people and control the access in real time. It will reduce the burden of manpower. In addition, we have optimized the system performance to handle multiple camera videos and to reduce the hardware cost at the same time. © 2023 IEEE.

关键词： Access control

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Coronary heart diseases prediction based on cardiology clinical features 5

Coronary heart diseases prediction based on cardiology clini...

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5th IEEE International Conference on Big data Intelligence and Computing, dataCom 2019

作者： Yang, Cheng-Hong Chuang, Li-Yeh Chang, Po-Yin National Kaohsiung University of Science and Technology Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Dept. of Electronic Engineering Kaohsiung Taiwan I-Shou University Dept. of Chemical Engineering Institute of Biotechnology and Chemical Engineering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Dept. of Electronic Engineering Kaohsiung Taiwan

ISBN: (纸本)9781728141176

The development of modern health systems has generated a large amount of medical data. Recent research has shown that data mining technology can discover potential patterns from a vast array of medical data, providing insight into disease and predicting disease. Coronary heart disease (CHd) is one of the most important research topics in the health field. In recent years, research on CHd has been extensively studied;however, the accuracy of CHd prediction needs to be improved. Here, a hybrid prediction method BOSVM combining Bayesian optimization (BO) and support vector machine (SVM) is proposed to predict the CHd. In BOSVM, BO aims to adjust of SVM parameters for enhancing the accuracy of CHd prediction. BOSVM compares performance of CHd prediction with five forecasting algorithms, including classification and regression tree, logistic regression, k-nearest neighbors, AdaBoost, and SVM. The experimental results show that the BOSVM has superior prediction accuracy than other algorithms in CHd prediction, indicating BOSVM can effectively predict CHd. © 2019 IEEE.

关键词： Support vector regression

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design of double-deck Household Vertical-axis Wind Generator

Design of Double-deck Household Vertical-axis Wind Generator

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2nd International Conference on Advances in Materials and Manufacturing Processes (ICAMMP 2011)

作者： Wu, Yu-Chi Lu, Cheng-Lung Huang, Chun-Chen Lin, Jing-Yuan Chen, Meng-Jen Chen, Wen-Shiush Chiang, Hsien-Yin Department of Electrical Engineering National United University Taiwan Department of Electrical Engineering National Dong Hua University Taiwan Department of Electrical Engineering National Kaohsiung University of Applied Science Taiwan Ph. D. Program in Materials and Chemical Engineering National United University Taiwan

ISBN: (纸本)9783037853283

Wind power has been recognized as one of the important green energy resources in Taiwan. However, installing large-capacity wind generators needs to meet many environmental regulations, limiting the penetration of wind power to households/communities. In this paper, the design of a novel double-deck vertical-axis wind generator for households is presented, hoping "many a little makes a mickle" and making better use of green energy to reduce carbon emission. The proposed wind generator is an axial-flux permanent-magnet (PM) synchronous generator, which design is different from other existing wind generators in several aspects: (1) double decks for double wind turbines vs. single turbine, (2) silicon-steel-lamination-cored vs. air-cored for armature coils, and (3) axial-flux PM generator vs. conventional PM generator. The performance of the proposed design is simulated with the aid of a finite element software package. The experimental results are consistent with the simulation. The prototype of the proposed wind generator is also compared with a commercial mini wind generator on the market. The proposed wind generator produces satisfactory results.

关键词： Green Energy Vertical Axis Wind Generator

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