Review
Chemistry, Applied
J. H. Markna, Prashant K. Rathod
Summary: This review article provides a comprehensive overview of the efficiency of quantum dot sensitized solar cells (QDSCs) based on dye-synthesized solar cells and nanotechnology, and discusses their status under the influence of photoanode and quantum dot sensitizers.
Article
Biochemistry & Molecular Biology
Dang Huu Phuc, Ha Thanh Tung, Van-Cuong Nguyen, My Hanh Nguyen Thi
Summary: In this study, TiO2/CdS/CdxCu1-xSe, TiO2/CdS/CdxMn1-xSe, and TiO2/CdS/CdxAg2-2xSe thin films were synthesized and studied for their structural, optical, and electrical properties for use as photoanodes in quantum-dot-sensitized solar cells. The results provided insights into the performance efficiency enhancement in these solar cells.
Article
Materials Science, Coatings & Films
Philipp G. Gruetzmacher, Michael Schranz, Chia-Jui Hsu, Johannes Bernardi, Andreas Steiger-Thirsfeld, Lars Hensgen, Manel Rodriguez Ripoll, Carsten Gachot
Summary: The power conversion efficiency (PCE) of PEB and PUB as sensitizers of dye-sensitized solar cells is investigated using first-principles calculations. Different adsorption models are constructed for PEB/PUB on the TiO2 surface, and their geometrical configurations and electronic properties are optimized. The obtained PCEs confirm the credibility of the current method and predict that PUB and PEB are promising candidate sensitizers for dye-sensitized solar cells.
SURFACE & COATINGS TECHNOLOGY
(2022)
Article
Chemistry, Physical
Kiran P. Shejale, Arun Jaiswal, Aditya Kumar, Sumit Saxena, Shobha Shukla
Summary: The high-quality nitrogen-doped carbon quantum dots (NCQDs) synthesized using domestic microwave-assisted pyrolysis method exhibit excellent physiochemical and optical properties, and when incorporated into the DSSC structure, they lead to improved performance with high photoconversion efficiency and photocurrent density.
Article
Chemistry, Multidisciplinary
Thibaut Baron, Waad Naim, Ilias Nikolinakos, Baptiste Andrin, Yann Pellegrin, Denis Jacquemin, Stefan Haacke, Frederic Sauvage, Fabrice Odobel
Summary: The development of transparent solar cells offers a new way to utilize windows as solar panels for electricity generation. This study introduces a new family of NIR-sensitizers based on pyrrolopyrrole cyanine dyes, which enables the development of fully transparent and colorless dye-sensitized solar cells with a record efficiency of 2.5%.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Chemistry, Applied
Haoran Zhou, Jung-Min Ji, Hwan Kyu Kim
Summary: Two porphyrin-based sensitizers, SGT-028 and SGT-029, were designed and synthesized via acceptor engineering for application in dye-sensitized solar cells (DSSCs). Despite similar absorption range and energy band gap to the reference dye SGT-021, SGT-029 achieved a higher power conversion efficiency (PCE) of 10.5% compared to SGT-028 with 9.1%, but was inferior to the benchmark porphyrin sensitizer SGT-021 (12.7%).
Article
Energy & Fuels
Feng-Lin Xing, Zhi-Hong Zhang, Chuan-Lu Yang, Mei-Shan Wang, Xiao-Guang Ma
Summary: The power conversion efficiency of PEB and PUB as sensitizers in dye-sensitized solar cells was investigated using first-principles calculations. The results showed high PCE values for PUB and PEB, indicating their potential as candidate sensitizers for dye-sensitized solar cells.
Article
Chemistry, Physical
Yunfei Jiao, Shuaishuai Liu, Zhongjin Shen, Le Mao, Yongjie Ding, Dan Ren, Felix Thomas Eickemeyer, Lukas Pfeifer, Dapeng Cao, Wenjuan Xu, Juan Song, Baoxiu Mi, Zhiqiang Gao, Shaik M. Zakeeruddin, Wei Huang, Michael Gratzel
Summary: Heteroaromatic units are commonly used as pi-spacers for sensitizers in dye-sensitized solar cells. The type of pi-spacer strongly influences the solar to electric power conversion efficiency of organic dyes, with electron-rich pi-spacers leading to higher efficiency. Molecular engineering plays a crucial role in developing high efficiency organic dyes for DSSCs.
JOURNAL OF MATERIALS CHEMISTRY A
(2021)
Article
Chemistry, Physical
Radha Mishra, Kalpna Jain, Vinay Prabha Sharma, Shyam Kishor, Lavanya M. Ramaniah
Summary: A set of copper dye complexes with different ancillary ligands were investigated for their potential use as sensitizers in DSSCs. It was found that substituting the thiophene group in the ancillary ligand and enhancing conjugation in the anchoring ligand can increase the light harvesting efficiency, making the dmp-based dyes with thiophene substitution the most effective sensitizers.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2022)
Article
Electrochemistry
Savisha Mahalingam, Abreeza Manap, Ramisha Rabeya, Kam Sheng Lau, Chin Hua Chia, Huda Abdullah, Nowshad Amin, Puvaneswaran Chelvanathan
Summary: The goal of this study is to investigate the impact of the chemical treatment of titanium tetrachloride (TiCl4) on graphene quantum dots (GQDs)-based dye-sensitized solar cells (DSSCs). The mechanism of how the TiCl4 treatment provides high surface area and porosity to enhance the adsorption of GQDs and dye is proposed. The electron transport analysis showed that the treatment reduced electron recombination rate and increased electron injection efficiency, resulting in improved performance of the DSSC.
ELECTROCHIMICA ACTA
(2023)
Article
Chemistry, Physical
Ching-Chin Chen, Vinh Son Nguyen, Hsiao-Chi Chiu, Yan-Da Chen, Tzu-Chien Wei, Chen-Yu Yeh
Summary: New anthracene-bridged organic dyes CXC12 and CXC22 are designed and synthesized for high-efficiency dye-sensitized solar cells (DSSCs) under dim light. The addition of anthracene-acetylene group in CXC dyes extends the pi-conjugation of the molecules, resulting in improved absorption and molar extinction coefficient. Among the three anthracene-based dyes, CXC22 shows the most appropriate molecular structure for light harvesting and balancing dye loading and molecular aggregation, achieving a remarkable power conversion efficiency of 37.07% under dim-light conditions.
ADVANCED ENERGY MATERIALS
(2022)
Article
Green & Sustainable Science & Technology
Savisha Mahalingam, Abreeza Manap, Azimah Omar, Foo Wah Low, N. F. Afandi, Chin Hua Chia, Nasrudin Abd Rahim
Summary: Graphene quantum dots (GQDs) with outstanding properties can be chemically modified and functionalized for high-performance dye-sensitized solar cells. Optimizing electron transport and electrolyte can further enhance the efficiency of GQD-DSSC.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Energy & Fuels
Masoud Peymannia, Kamaladin Gharanjig, Amir Masoud Arabi
Summary: In this study, natural dyes were extracted from extracted from Cytisus, Alcea rosea, and Roselle flowers using the solvent method, and then combined with zinc oxide quantum dots (ZnO QDs) to enhance the efficiency of dye-sensitized solar cells (DSSC). The optimized quantum dots exhibited a particle size of about 3 nm and a maximum emission intensity of 1550 at 520 nm. The modified solar cells showed an efficiency increase of 17% compared to unmodified cells, highlighting the potential for using quantum dots to improve environmentally friendly solar cells.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Chemistry, Physical
William E. Meador, Nalaka P. Liyanage, Jonathon Watson, Katelyn Groenhout, Jared H. Delcamp
Summary: This article investigates two near-infrared sensitizers containing a proaromatic thienopyrazine (TPZ) auxiliary acceptor to enhance the efficiency of dye-sensitized solar cells. The optical, electrochemical, and device properties of the sensitizers were studied. Optimized devices, sensitized with Y123, achieved high photocurrent values of 20.3 and 14.4 mA/cm2 for WM1 and WM2, respectively.
ACS APPLIED ENERGY MATERIALS
(2023)
Article
Materials Science, Multidisciplinary
Jianyong Wan, Yeshen Liu, Hongda Guo, JingJing Liang, Lvming Qiu, Yuhao Lu, Haibo Xiao
Summary: Six organic sensitizers were synthesized to improve the thermal/photo-stability and solubility while suppressing dye aggregation. The resulting devices showed higher performance due to weak or no aggregation.
MATERIALS CHEMISTRY AND PHYSICS
(2023)
Article
Computer Science, Artificial Intelligence
Jianpeng Yuan, Wensheng Huang, Yongshun Wu, Long Liu, Chao Bu, Shuqiang Wang, Weidong Zhang
Summary: This study explored the application value of deep learning combined with CT imaging omics in predicting metastatic lymph nodes in nasopharyngeal carcinoma. It found that a combination of certain CT image features and imaging omics features could efficiently predict lymphatic metastasis.
Article
Computer Science, Artificial Intelligence
Baiying Lei, Enmin Liang, Mengya Yang, Peng Yang, Feng Zhou, Ee-Leng Tan, Yi Lei, Chuan-Ming Liu, Tianfu Wang, Xiaohua Xiao, Shuqiang Wang
Summary: This paper presents a joint and deep learning framework to predict clinical scores of Alzheimer's disease by using feature selection method and multi-layer independently recurrent neural network regression to study the relationship between different brain regions and longitudinal data. The predicted clinical score values help doctors in early diagnosis and treatment.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Shengye Hu, Baiying Lei, Shuqiang Wang, Yong Wang, Zhiguang Feng, Yanyan Shen
Summary: This paper proposes a 3D end-to-end synthesis network called BMGAN for synthesizing PET images from brain MR images. The method utilizes a bidirectional mapping mechanism and employs a 3D Dense-UNet generator architecture and hybrid loss functions to generate high-quality cross-modality synthetic images while preserving the diverse brain structures of different subjects.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Automation & Control Systems
Shuqiang Wang, Xiangyu Wang, Yanyan Shen, Bing He, Xinyan Zhao, Prudence Wing-Hang Cheung, Jason Pui Yin Cheung, Keith Dip-Kei Luk, Yong Hu
Summary: Assessment of skeletal maturity is crucial for clinicians to make treatment decisions, but using machine learning for this task is challenging. In this article, an ensemble-based deep learning approach is proposed to automatically assess the maturity of the radius and ulna from left-hand X-ray images. Experimental results demonstrate the effectiveness of the proposed model.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuqiang Wang, Zhuo Chen, Senrong You, Bingchuan Wang, Yanyan Shen, Baiying Lei
Summary: State-of-the-art deep learning methods have achieved impressive performance in segmentation tasks, but they require a large amount of manually labeled masks. This study proposes a novel CPGAN method for semi-supervised stroke lesion segmentation, which reduces the need for fully labeled samples. Experimental results demonstrate the superior performance of the proposed method.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Cell Biology
Chengcheng Song, Shuqiang Wang, Zhangning Fu, Kun Chi, Xiaodong Geng, Chao Liu, Guangyan Cai, Xiangmei Chen, Di Wu, Quan Hong
Summary: Renal inflammation is a critical characteristic of diabetic kidney disease (DKD), but effective treatments are limited. This study found that insulin-like growth factor-binding protein 5 (IGFBP5) is significantly increased in the kidneys of diabetic mice. Elimination of IGFBP5 alleviated kidney inflammation in DKD mice. Mechanistically, IGFBP5 increased glycolysis in endothelial cells through the activation of the transcription factor EGR1 and enhanced expression of PFKFB3, which in turn enhanced renal inflammation.
CELL DEATH & DISEASE
(2022)
Article
Computer Science, Artificial Intelligence
Wen Yu, Baiying Lei, Michael K. Ng, Albert C. Cheung, Yanyan Shen, Shuqiang Wang
Summary: The proposed model in this study combines deep learning and high-order pooling techniques for early diagnosis of AD, and demonstrates superior performance in MRI image classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Haijun Lei, Zhihui Tian, Hai Xie, Benjian Zhao, Xianlu Zeng, Jiuwen Cao, Weixin Liu, Jiantao Wang, Guoming Zhang, Shuqiang Wang, Baiying Lei
Summary: In this paper, a lesion attention conditional generative adversarial network (LACGAN) is proposed to synthesize retinal images with realistic lesion details, aiming to improve the training of disease detection models. Experimental results demonstrate that this method can generate retinal images with reasonable details, which helps enhance the performance of the disease detection model.
Article
Computer Science, Artificial Intelligence
Baiying Lei, Yun Zhu, Shuangzhi Yu, Huoyou Hu, Yanwu Xu, Guanghui Yue, Tianfu Wang, Cheng Zhao, Shaobin Chen, Peng Yang, Xuegang Song, Xiaohua Xiao, Shuqiang Wang
Summary: In this study, a new framework called multi-scale enhanced graph convolutional network (MSE-GCN) is proposed for mild cognitive impairment (MCI) detection by analyzing brain connectivity networks. The experimental results demonstrate the promising performance of our method in MCI detection and its superiority over other competing algorithms.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Wen Yu, Baiying Lei, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen, Michael K. Ng
Summary: In this study, a multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features of patients with early stages of Alzheimer's disease (AD), indicating the severity of the disease. Experimental results demonstrate that MP-GAN outperforms existing methods and the visualized lesions are consistent with what clinicians observe.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Senrong You, Baiying Lei, Shuqiang Wang, Charles K. Chui, Albert C. Cheung, Yong Liu, Min Gan, Guocheng Wu, Yanyan Shen
Summary: Magnetic resonance (MR) imaging is crucial in clinical and brain exploration, yet it is challenging to acquire high-resolution MR images due to hardware limitations, scanning time, and cost. In this article, the authors propose FP-GANs, a model that uses divide-and-conquer approach to generate super-resolution MR images. The model separates and processes the low-frequency and high-frequency components of MR images and achieves better structure recovery and classification performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Editorial Material
Neurosciences
Shu-Qiang Wang, Zhiguo Zhang, Fei He, Yong Hu
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Baiying Lei, Yun Zhu, Enmin Liang, Peng Yang, Shaobin Chen, Huoyou Hu, Haoran Xie, Ziyi Wei, Fei Hao, Xuegang Song, Tianfu Wang, Xiaohua Xiao, Shuqiang Wang, Hongbin Han
Summary: In multi-site studies of Alzheimer's disease, data differences in multi-site datasets affect the model performance in target sites. To overcome the privacy issue in traditional domain adaptation methods, this paper proposes a multi-site federated domain adaptation framework based on Transformer network. The proposed method not only protects data privacy but also eliminates data heterogeneity. Experimental results show high accuracy rates for various classification tasks.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Engineering, Biomedical
Qiankun Zuo, Yanyan Shen, Ning Zhong, C. L. Philip Chen, Baiying Lei, Shuqiang Wang
Summary: The paper proposes a novel cross-modal transformer generative adversarial network (CT-GAN) to effectively fuse the functional and structural information in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity to analyze the deterioration of Alzheimer's disease. The proposed model shows great potential in improving prediction performance and detecting AD-related brain regions.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Changwei Gong, Changhong Jing, Junren Pan, Yishan Wang, Shuqiang Wang
Summary: This study investigates the functional changes in neural circuits between nicotine addiction and healthy control groups using fMRI. A novel feature-selected graph spatial attention network (FGSAN) is proposed to extract biomarkers of addiction and identify brain networks associated with addiction.
BRAIN INFORMATICS (BI 2022)
(2022)
