High-entropy ceramics (HECs), as a member of the large family of high-entropy materials (HEMs), are defined as solid solutions containing five or more cationic or anionic sublattices with high configurational entropy. HECs and high-entropy alloys (HEAs) share the similar “four major effects”, including the high-entropy effect, lattice distortion effect, hysteresis-diffusion effect, and synergistic effect. The compositional and structural complexity of HECs allows them to exhibit a diverse range of performance characteristics, which have the potential to be applied in numerous technological fields. These include, but are not limited to, wear and corrosion-resistant coatings, thermal barrier coatings, wave-absorbing coatings, solar energy-absorbing and irradiation-resistant coatings, and so on. Nevertheless, the expansive compositional space necessitates the time-consuming and costly experimental trial-and-error method as a significant factor in the development of new HECs. In the field of materials science, the discovery and identification of new compositions can be accelerated by data-driven and high-throughput methods that employ machine learning (ML) methods for phase prediction and property prediction of new materials. This paper presents a review of the functional applications of high-entropy ceramics, with a particular focus on data-driven methods and high-throughput strategies. The objective is to provide insights that can facilitate the advancement and innovation of high-entropy ceramics in functional applications.
This paper is concerned with the existence of invariant measures for stochastic delay complex Ginzburg-Landau equations defined on the entire integer set. When the nonlinear drift and diffusion terms are globally Lipschitz continous, the existence of invariant measures of the equation is proved by estiblishing the tightness of probability distributions of solutions in the space of continuous functions from a finite interval to an infinite-dimensional space, based on the idea of uniform tail-estimates, the technique of diadic division and the Arzela-Ascoli theorem.
Nonlinear optical (NLO) materials as a crucial part of the laser science have attracted increasing attention from researchers because of their wide applications in information storages, modern science and technologies, et al. Based on the anionic group theory, the n-conjugated 6-membered rings (6-MRs), B3O6-typed structures have key contributions to the superior optical properties of /3-Ba2B2O4 (/3-BBO). In recent years, the organic coplanar n-conjugated B3O6-typed structures have caught researchers' attention due to similar configurations to the (B3O6) groups, which are the potential sources for expending the (B3O6) groups with outstanding optical properties. Up to know, researchers have obtained many ultraviolet (UV) NLO crystals with excellent properties with n-conju-gated B3O6-typed groups. Herein, these B3O6-typed groups could be divided into different categories according to the atoms constituting the 6-MRs: (HxC3N3O3)x-3 (x = 0-3) (cyanurate ion), (HxC4N2O3)x-4 (x = 2, 3) (barbi-turate ion), (C3H7N6)+ (melamine ion), (C5H6ON)+ (4-hydroxypyridine cation), and (C4H6N3)+ (2-amino-pyrimidinium cation). In this review, we introduced the research advances of NLO materials with n-conjugated B3O6-typed groups, and summarized the crystal structures, synthetic methods, optical performances, as well as the relationships between structures and properties. This work provided a clear perspective for understanding the coplanar n-conjugated B3O6-typed groups with their optical functional properties and promote the searches to develop potential NLO functional crystals.
利用武汉市环境监测站有害大气污染物监测数据和气象观测站的温度、降雨、气压和湿度数据,分析武汉市2019年大气污染物在不同时间段上的变化特征,研究污染物与气象要素的关系.结果表明:1)24 h内,CO与NO2质量浓度存在双"波峰-波谷"变化特征,O3与SO2质量浓度则有单波峰形态,PM2.5与PM10质量浓度有多波峰波谷变化特点;2)污染物质量浓度24 h内的变化特征与人类活动及天气系统发展或存在明显关联;3)除O3外,气温与其余污染物的质量浓度均为负相关关系,低压有利于污染物扩散;4)降雨对污染物清除作用明显,且降雨量越大,污染物样本空间量越少;5)月尺度上,相对湿度与O3、NO2和SO2质量浓度为负相关关系,与CO、PM2.5和PM10质量浓度为正相关关系,气压与除O3外的其他污染物质量浓度均为正相关关系.
基于2016-2020年成都平原经济区城市空气质量监测数据,应用空气质量综合指数评价法、Pearson相关分析法,对成都平原经济区空气质量时空变化特征、成都市主要污染物变化特征进行了研究.结果表明:1)2016-2020年,成都平原经济区城市群中空气质量指数出现了 2个高值和2个低值中心,高值中心分别位于成都市、德阳市,2个低值中心分别位于雅安市、遂宁市,即成都市和德阳市空气质量最差,雅安市和遂宁市的空气质量最优;对于不同季节,1-12月,成都平原经济区的空气质量综合指数呈U字型变化,即空气质量6-10月较好,11月—次年2月较差.2)2016-2020年,成都市PM2.5质量浓度值皆超过了国家二级标准限值,并且以PM2.5为首要污染物出现频率最高,而O3、CO、SO2年均质量浓度在研究时段内均低于国家二级标准质量浓度限值.3)2016-2020年,成都市PM2.5质量浓度与能见度呈显著负相关;年尺度上,成都市PM2.5质量浓度与平均气温呈低度负相关,与风速、降水量呈弱负相关;季尺度上,除气温在春、夏、冬季节与PM2.5质量浓度呈弱正相关外,4个季节中PM2.5质量浓度与风速、降水量皆呈弱负相关.4)影响成都市PM2.5质量浓度的社会经济因子主要是汽车尾气、工业烟(粉)尘排放.PM2.5与地区生产总值、工业增加值、机动车保有量呈高度负相关,与工业烟(粉)尘排放量呈高度正相关.
