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| 1 | Angular-based spatially resolved laser-induced breakdown spectroscopy: a new technique for the effective enhancement of signals without an external time delay system显示文摘In this study, we developed a novel angularbased spatially resolved laser-induced breakdown spectroscopy(ABSR-LIBS) technique. This new method allows the spatially resolved spectroscopic properties of laserinduced plasma to be investigated using different angles and polar distances with a spatially adjustable fiber optics probe. The experimental evaluations suggested that the intensity of the Al signal distributions in the ‘‘side-view''depended greatly on the angle of the collection probe. The location of a collection probe at 50° would facilitate efficient spectral collection and minimize the disturbance of the plasma plume. The spectral signals in the plasma emissions had different distribution at various polar distances using the same collection angle, and the highest signal intensity was observed between 15 and 35 mm polar distances. This ABSR-LIBS technique improved both the LIBS signal and the maximum emission intensity of Al atoms by several times at the best spatial position in our experimental conditions. Furthermore, we successfully obtained well-resolved line emission spectroscopy signals without any external time delay using a nongated spectrometer based on the ABSR-LIBS system. We consider that the ABSR-LIBS technique has great potential for obtaining new insights with laser-induced plasma instruments and it could facilitate the development of a low-cost instrument. | Qingyu Lin Xu Wang Guanghui Niu Hongjun Lai Xiaoqin Zhu Kunping Liu Tao Xu Yixiang Duan | 2014 | Chinese Science Bulletin2014，59，27： | 3 |
| 2 | Recent progress of quantum dots for energy storage applications显示文摘The environmental problems of global warming and fossil fuel depletion are increasingly severe,and the demand for energy conversion and storage is increasing.Ecological issues such as global warming and fossil fuel depletion are increasingly stringent,increasing energy conversion and storage needs.The rapid development of clean energy,such as solar energy,wind energy and hydrogen energy,is expected to be the key to solve the energy problem.Several excellent literature works have highlighted quantum dots in supercapacitors,lithium-sulfur batteries,and photocatalytic hydrogen production.Here,we outline the latest achievements of quantum dots and their composites materials in those energy storage applications.Moreover,we rationally analyze the shortcomings of quantum dots in energy storage and conversion,and predict the future development trend,challenges,and opportunities of quantum dots research. | Quan Xu Yingchun Niu Jiapeng Li Ziji Yang Jiajia Gao Lan Ding Huiqin Ni Peide Zhu Yinping Liu Yaoyao Tang Zhong-Peng Lv Bo Peng Travis Shihao Hu Hongjun Zhou Chunming Xu | 2022 | Carbon Neutrality2022，1，1： | 1 |
| 3 | Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma显示文摘Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health globally.With advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical issues.Conventional radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies.During the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis prediction.In this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC. | Jingwei Wei Meng Niu Ouyang Yabo Yu Zhou Xiaoke Ma Xue Yang Hanyu Jiang Hui Hui Hongyi Cao Binwei Duan Hongjun Li Dawei Ding Jie Tian | 2022 | iLIVER2022，1，1： | 0 |
| 4 | 11S蛋白酶体PSME3通过正反馈调节NF-κB信号通路调节机体抗菌反应与宿主防御显示文摘巨噬细胞在宿主防御以及机体内环境稳定的维持中发挥着至关重要的作用。当机体受到外来病原微生物入侵时,巨噬细胞能够通过分泌促炎因子和杀伤物质,杀死入侵的病原菌。然而,关于巨噬细胞在宿主防御过程中是如何被严格调控的,至今仍不是很清楚。 | Jinxia Sun Yi Luan Dong Xiang Xiao Tan Hui Chen Qi Deng Jiaojiao Zhang Minghui Chen Hongjun Huang Weichao Wang Tingting Niu Wenjie Li Hu Peng Shuangxi Li Lei Li Wenwen Tang 李晓涛 Dianqing Wu 王平 | 2017 | 科学新闻2017，19，4： | 0 |
| 5 | A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning显示文摘The application of machine learning models to predict material properties is determined by the availability of high-quality data.We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c.impedance spectroscopy.This dataset has 820 entries collected from 214 sources;entries contain a chemical composition,an expert-assigned structural label,and ionic conductivity at a specific temperature(from 5 to 873°C).There are 403 unique chemical compositions with an associated ionic conductivity near room temperature(15–35°C).The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance.This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity.This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors. | Cameron J.Hargreaves Michael W.Gaultois Luke M.Daniels Emma J.Watts Vitaliy A.Kurlin Michael Moran Yun Dang Rhun Morris Alexandra Morscher Kate Thompson Matthew A.Wright Beluvalli-Eshwarappa Prasad Frédéric Blanc Chris M.Collins Catriona A.Crawford Benjamin B.Duff Jae Evans Jacinthe Gamon Guopeng Han Bernhard T.Leube Hongjun Niu Arnaud J.Perez Aris Robinson Oliver Rogan Paul M.Sharp Elvis Shoko Manel Sonni William J.Thomas Andrij Vasylenko Lu Wang Matthew J.Rosseinsky Matthew S.Dyer | 2023 | npj Computational Materials2023，，1： | 0 |
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