掺铒碲酸盐玻璃的上转换发光与配位场系数关系研究

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"该研究论文探讨了配位场系数对掺铒碲酸盐玻璃上转换发光的影响,作者为苏方宁和邓再德,发表在华南理工大学材料科学与工程学院光学通信材料研究所。文章中,他们研究了Er3+掺杂的碲酸盐玻璃(MKT:TeO2-MgO-K2O)的上转换发光特性,观察到两个绿色发射带分别位于521纳米和550纳米,对应于2H11/2→4I15/2和4S3/2→4I15/2的跃迁。首次提出了基于保罗规则和扎查里斯随机网络理论推导出的配位场指数,用于解释上转换发光强度的显著变化。关键词包括配位场指数、保罗规则、扎查里斯随机网络理论、上转换发光和碲酸盐玻璃。该研究对于短波长紧凑型激光器的制造方法之一——上转换光纤激光器有潜在的应用价值。" 详细说明: 在本文中,研究者苏方宁和邓再德关注的是掺铒碲酸盐玻璃的上转换发光现象,这是一种重要的光学性质,特别是在光纤激光器领域。上转换发光是指在低能量光子作用下,通过多光子吸收过程,将多个低能光子转化为一个高能光子,这在短波长激光器的研制中具有重要意义。 研究对象是Er3+掺杂的碲酸盐玻璃(MKT: TeO2-MgO-K2O),其中Er3+是一种常见的稀土离子,因其在特定波长下的高效上转换能力而被广泛研究。实验观察到两个主要的绿色发射峰,分别位于521纳米和550纳米,这两个发射带对应于Er3+离子的两个不同电子跃迁,即2H11/2能级到4I15/2能级以及4S3/2能级到4I15/2能级的跃迁。 文章创新之处在于引入了配位场指数的概念,这是基于保罗规则和扎查里斯随机网络理论推导出来的。保罗规则是化学键合理论的基础,用于预测和解释离子在晶体中的配位环境和电子结构;而扎查里斯随机网络理论则是一种描述无序玻璃结构的模型。配位场指数的提出,为理解掺铒碲酸盐玻璃中上转换发光强度的变化提供了一个新的理论框架,有助于优化材料的性能。 此外,该研究对上转换光纤激光器的发展具有潜在的贡献。上转换光纤激光器可以将红外光转换为可见光或紫外光,对于微型化、高功率激光系统的设计至关重要,尤其是在生物成像、光谱分析、激光雷达和光通信等领域。 这项工作揭示了配位场系数对掺铒碲酸盐玻璃上转换发光性能的具体影响,并为理解和调控此类材料的光学性质提供了新的理论工具。

用代码解决这个问题The program committee of the school programming contests, which are often held at the Ural State University, is a big, joyful, and united team. In fact, they are so united that the time spent together at the university is not enough for them, so they often visit each other at their homes. In addition, they are quite athletic and like walking. Once the guardian of the traditions of the sports programming at the Ural State University decided that the members of the program committee spent too much time walking from home to home. They could have spent that time inventing and preparing new problems instead. To prove that, he wanted to calculate the average distance that the members of the program committee walked when they visited each other. The guardian took a map of Yekaterinburg, marked the houses of all the members of the program committee there, and wrote down their coordinates. However, there were so many coordinates that he wasn't able to solve that problem and asked for your help. The city of Yekaterinburg is a rectangle with the sides parallel to the coordinate axes. All the streets stretch from east to west or from north to south through the whole city, from one end to the other. The house of each member of the program committee is located strictly at the intersection of two orthogonal streets. It is known that all the members of the program committee walk only along the streets, because it is more pleasant to walk on sidewalks than on small courtyard paths. Of course, when walking from one house to another, they always choose the shortest way. All the members of the program committee visit each other equally often. Input The first line contains the number n of members of the program committee (2 ≤ n ≤ 105). The i-th of the following n lines contains space-separated coordinates xi, yi of the house of the i-th member of the program committee (1 ≤ xi, yi ≤ 106). All coordinates are integers. Output Output the average distance, rounded down to an integer, that a member of the program committee walks from his house to the house of his colleague.

2023-05-26 上传

翻译Agent 𝑐 𝑖 . In this paper, we regard each charging station 𝑐 𝑖 ∈ 𝐶 as an individual agent. Each agent will make timely recommendation decisions for a sequence of charging requests 𝑄 that keep coming throughout a day with multiple long-term optimization goals. Observation 𝑜 𝑖 𝑡 . Given a charging request 𝑞𝑡 , we define the observation 𝑜 𝑖 𝑡 of agent 𝑐 𝑖 as a combination of the index of 𝑐 𝑖 , the real-world time 𝑇𝑡 , the number of current avail able charging spots of 𝑐 𝑖 (supply), the number of charging requests around 𝑐 𝑖 in the near future (future demand), the charging power of 𝑐 𝑖 , the estimated time of arrival (ETA) from location 𝑙𝑡 to 𝑐 𝑖 , and the CP of 𝑐 𝑖 at the next ETA. We further define 𝑠𝑡 = {𝑜 1 𝑡 , 𝑜2 𝑡 , . . . , 𝑜𝑁 𝑡 } as the state of all agents at step 𝑡. Action 𝑎 𝑖 𝑡 . Given an observation 𝑜 𝑖 𝑡 , an intuitional design for the action of agent𝑐 𝑖 is a binary decision, i.e., recommending 𝑞𝑡 to itself for charging or not. However, because one 𝑞𝑡 can only choose one station for charging, multiple agents’ actions may be tied together and are difficult to coordinate. Inspired by the bidding mechanism, we design each agent 𝑐 𝑖 offers a scalar value to "bid" for 𝑞𝑡 as its action 𝑎 𝑖 𝑡 . By defining 𝑢𝑡 = {𝑎 1 𝑡 , 𝑎2 𝑡 , . . . , 𝑎𝑁 𝑡 } as the joint action, 𝑞𝑡 will be recommended to the agent with the highest "bid" value, i.e., 𝑟𝑐𝑡 = 𝑐 𝑖 , where 𝑖 = arg max(𝑢𝑡)

2023-07-11 上传