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FeF_(x) and Fe_(2)ZrO_(5) Co-modified hematite for highly efficient solar water splitting

Journal of Energy Chemistry 2022年第6期31卷 414-420,I0012页

作者： Xiaoquan Zhao Cheng Lu Shuo Li Yufeng Chen gaoteng zhang Duo zhang Kun Feng Jun Zhong Institute of Functional Nano and Soft Materials Laboratory(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123JiangsuChina State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and ProtectionSoochow UniversitySuzhou 215123JiangsuChina

Hematite is an excellent catalyst for photoelectrochemical (PEC) water splitting but its performance has been highly limited by poor conductivity and high charge recombination.Here by a Zr-based treatment to create bulk Fe_(2)ZrO_(5) in hematite and a F-based treatment to form an ultrathin surface FeF_(x) layer,the charge transfer can be highly improved and the charge recombination can be significantly suppressed.As a result,the FeF_(x) /Zr-Fe_(2)O_(3) photoanode presents an enhanced PEC performance with a photocurrent density of 2.43 m A/cm^(2)at 1.23 V vs.RHE,which is around 3 times higher than that of the pristine Fe_(2)O_(3) .The FeF_(x) /Zr-Fe_(2)O_(3) photoanode also shows a low onset potential of 0.77 V vs.RHE (100 mV lower than the pristine hematite).The performance is much higher than that of the sample treated by Zr or F alone,suggesting the synergistic effect between bulk Fe_(2)ZrO_(5) and surface FeF_(x) .By coupling with the FeNiOOH co-catalyst,the final photoanode can achieve a high photocurrent density of 2.81 mA/cm^(2) at 1.23 V vs.RHE.The novel design of Zr and F co-modified hematite can be used as a promising way to prepare efficient catalysts for solar water splitting.

关键词： Hematite Fe_(2)ZrO_(5) Fluorination Solar water splitting

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Soft X-Ray Absorption Spectroscopy of Advanced Two-Dimensional Photo/Electrocatalysts for Water Splitting

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Chinese Journal of Structural Chemistry 2022年第10期41卷 16-28页

作者： Xiaoxin Lv Guoqing Li gaoteng zhang Kun Feng Jiujun Deng Jun Zhong Institute for Energy Research Automotive Engineering Research InstituteJiangsu UniversityZhenjiang 212013China Institute of Functional Nano and Soft Materials Laboratory(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123China

Photo/electrocatalytic water splitting has been considered as one of the most promising approaches for the clean hydrogen production. Among various photo/electrocatalysts,2D nanomaterials exhibit great potential because of their conspicuous properties. Meanwhile,synchrotron-based soft X-ray absorption spectroscopy(XAS) as a powerful and element-specific technique has been widely used to explore the electronic structure of 2D photo/electrocatalysts to comprehensively understand their working mechanism for the development of high-performance catalysts. In this work, the recent developments of soft XAS techniques applied in 2D photo/electrocatalysts have been reviewed, mainly focusing on identifying the surface active sites,elucidating the location of heteroatoms, and unraveling the interfacial interaction in the composite.The challenges and outlook in this research field have also been emphasized. The present review provides an in-depth understanding on how soft XAS techniques unravel the correlations between structure and performance in 2D photo/electrocatalysts, which could guide the rational design of highly efficient catalysts for photo/electrocatalytic water splitting.

关键词： two-dimensional materials photocatalyst electrocatalyst water splitting soft XAS

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融合机载LiDAR和高光谱影像的土地利用分类

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激光杂志 2023年第3期44卷 133-136页

作者：张高腾王浩宇王成武晓康桂林理工大学测绘地理信息学院广西桂林541004 中国科学院空天信息创新研究院数字地球重点实验室北京100094

高光谱影像具有丰富的波谱和纹理信息,机载LiDAR点云数据包含了地物高密度、高精度的三维信息。分别从两种数据中提取地物的光谱特征、纹理特征和高度特征,并进行不同的特征组合,然后采用随机森林分类器进行地物分类实验。结果表明,机载... 详细信息

高光谱影像具有丰富的波谱和纹理信息,机载LiDAR点云数据包含了地物高密度、高精度的三维信息。分别从两种数据中提取地物的光谱特征、纹理特征和高度特征,并进行不同的特征组合,然后采用随机森林分类器进行地物分类实验。结果表明,机载LiDAR点云和高光谱数据在地物分类方面具有很强的信息互补性;融合了LiDAR高度特征的总体分类精度和Kappa系数均优于仅使用高光谱影像,其中“PCA+NDVI+GLCM+CHM”的特征组合总体分类精度和Kappa系数最高,分别为85.96%和0.81;与未加入LiDAR特征的组合相比,总体分类精度提高了5.33%。

关键词：机载激光雷达高光谱影像数据融合随机森林地物分类

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