Opto-Electronic Science最新文献_第3页

Towards integrated mode-division demultiplexing spectrometer by deep learning 基于深度学习的集成模分解复用光谱仪研究

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.220012

Ze-huan Zheng, Sheng-ke Zhu, Ying Chen, H. Chen, Jin‐hui Chen

引用次数: 6

Multi-foci metalens for spectra and polarization ellipticity recognition and reconstruction 用于光谱和偏振椭圆度识别与重建的多焦点超构透镜

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2023.220026

Hui Gao, Xuhao Fan, Yuxi Wang, Yuncheng Liu, Xing Wang, Ke Xu, Leimin Deng, C. Zeng, Tingan Li, J. Xia, Wei Xiong

引用次数: 5

Configurable topological beam splitting via antichiral gyromagnetic photonic crystal 反手旋磁光子晶体的可配置拓扑束分裂

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.220001

Jianfeng Chen, Zhi-Yuan Li

引用次数: 7

Specialty optical fibers for advanced sensing applications 用于高级传感应用的特种光纤

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2023.220025

Huanhuan Liu, D. Hu, Qizhen Sun, Lei Wei, Kaiwei Li, C. Liao, Bozhe Li, Cong Zhao, Xinyong Dong, Yuhan Tang, Yihong Xiao, G. Keiser, P. Shum

引用次数: 4

Perovskite-transition metal dichalcogenides heterostructures: recent advances and future perspectives 钙钛矿-过渡金属二硫族化合物异质结构:最新进展和未来展望

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.220006

A. Elbanna, Ksenia Chaykun, Y. Lekina, Yuanda Liu, B. Febriansyah, Shuzhou Li, Jisheng Pan, Z. Shen, J. Teng

引用次数: 10

Measurement of optical coherence structures of random optical fields using generalized Arago spot experiment 用广义Arago光斑实验测量随机光场的光学相干结构

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2023.220024

Xin Liu, Qian Chen, Jun Zeng, Y. Cai, C. Liang

引用次数: 4

Terahertz generation from laser-induced plasma 激光诱导等离子体产生太赫兹

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.220003

Wen-feng Sun, Xin-Ke Wang, Yan Zhang

引用次数: 14

Femtosecond laser-induced periodic structures: mechanisms, techniques, and applications 飞秒激光诱导的周期结构:机制、技术和应用

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.220005

Yuchan Zhang, Q. Jiang, Mingquan Long, Ruozhong Han, Kaiqiang Cao, Shian Zhang, D. Feng, T. Jia, Zhenrong Sun, J. Qiu, Hongxing Xu

引用次数: 20

Solar cell-based hybrid energy harvesters towards sustainability 以太阳能电池为基础的可持续性混合能源收集器

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2023.230011

Tianxiao Xiao, Suo Tu, Suzhe Liang, Renjun Guo, Ting Tian, Peter Müller‐Buschbaum

{"title":"Solar cell-based hybrid energy harvesters towards sustainability","authors":"Tianxiao Xiao, Suo Tu, Suzhe Liang, Renjun Guo, Ting Tian, Peter Müller‐Buschbaum","doi":"10.29026/oes.2023.230011","DOIUrl":"https://doi.org/10.29026/oes.2023.230011","url":null,"abstract":"Energy harvesting plays a crucial role in modern society. In the past years, solar energy, owing to its renewable, green, and infinite attributes, has attracted increasing attention across a broad range of applications from small-scale wearable electronics to large-scale energy powering. However, the utility of solar cells in providing a stable power supply for various electrical appliances in practical applications is restricted by weather conditions. To address this issue, researchers have made many efforts to integrate solar cells with other types of energy harvesters, thus developing hybrid energy har-vesters (HEHs), which can harvest energy from the ambient environment via different working mechanisms. In this review, four categories of energy harvesters including solar cells, triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs), and thermoelectric generators (TEGs) are introduced. In addition, we systematically summarize the recent progress in solar cell-based hybrid energy harvesters (SCHEHs) with a focus on their structure designs and the corresponding applications. Three hybridization designs through unique combinations of TENG, PENG, and TEG with solar cells are elaborated in detail. Finally, the main challenges and perspectives for the future development of SCHEHs are discussed.","PeriodicalId":208845,"journal":{"name":"Opto-Electronic Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125305857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking deep learning-based models on nanophotonic inverse design problems 基于深度学习的纳米光子逆设计模型的标杆测试

Opto-Electronic Science Pub Date : 1900-01-01 DOI: 10.29026/oes.2022.210012

Taigao Ma, Mustafa Tobah, Haozhu Wang, L. J. Guo

{"title":"Benchmarking deep learning-based models on nanophotonic inverse design problems","authors":"Taigao Ma, Mustafa Tobah, Haozhu Wang, L. J. Guo","doi":"10.29026/oes.2022.210012","DOIUrl":"https://doi.org/10.29026/oes.2022.210012","url":null,"abstract":"Photonic inverse design concerns the problem of finding photonic structures with target optical properties. However, tra-ditional methods based on optimization algorithms are time-consuming and computationally expensive. Recently, deep learning-based approaches have been developed to tackle the problem of inverse design efficiently. Although most of these neural network models have demonstrated high accuracy in different inverse design problems, no previous study has examined the potential effects under given constraints in nanomanufacturing. Additionally, the relative strength of different deep learning-based inverse design approaches has not been fully investigated. Here, we benchmark three commonly used deep learning models in inverse design: Tandem networks, Variational Auto-Encoders, and Generative Adversarial Networks. We provide detailed comparisons in terms of their accuracy, diversity, and robustness. We find that tandem networks and Variational Auto-Encoders give the best accuracy, while Generative Adversarial Networks lead to the most diverse predictions. Our findings could serve as a guideline for researchers to select the model that can best suit their design criteria and fabrication considerations. In addition, our code and data are publicly available, which could be used for future inverse design model development and benchmarking. nanophotonic inverse design problems. Opto-Electron Sci 210012","PeriodicalId":208845,"journal":{"name":"Opto-Electronic Science","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126171301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30