重要提醒:2025.12.15 12:00-12:50期间发布的求助,下载出现了问题,现在已经修复完毕,请重新下载即可。如非文件错误,请不要进行驳回。

Distinguishing between chemical bonding and physical binding using electron localization function (ELF)

范德瓦尔斯力 电子定域函数 化学键 离子键合 化学物理 结合能 氢键 化学 非共价相互作用 密度泛函理论 电子密度 伦敦分散部队 电子 分子 分子中的原子 原子物理学 共价键 计算化学 物理 离子 量子力学 有机化学
作者
Κωνσταντίνος Κουμπούρας,J. Andreas Larsson
出处
期刊:Journal of Physics: Condensed Matter [IOP Publishing]
卷期号:32 (31): 315502-315502 被引量:298
标识
DOI:10.1088/1361-648x/ab7fd8
摘要

Abstract To distinguish between chemical bonding and physical binding is usually simple. They differ, in the normal case, in both interaction strength (binding energy) and interaction length (structure). However, chemical bonding can be weak (e.g. in some metallic bonding) and physical binding can be strong (e.g. due to permanent electrostatic moments, hydrogen binding, etc) making differentiation non-trivial. But since these are shared-electron or unshared-electron interactions, respectively, it is in principle possible to distinguish the type of interaction by analyzing the electron density around the interaction point(s)/interface. After all, the former should be a contact while the latter should be a tunneling barrier. Here, we investigate within the framework of density functional theory typical molecules and crystals to show the behaviour of the electron localization function (ELF) in different shared-electron interactions, such as chemical (covalent) and metallic bonding and compare to unshared-electron interactions typical for physical binding, such as ionic, hydrogen and Keesom, dispersion (van der Waals) binding and attempt to categorise them only by the ELF and the electron population in the interaction region. It is found that the ELF method is not only useful for the characterization of covalent bonds but a lot of information can be extracted also for weaker types of binding. Furthermore, the charge integration over the interaction region(s) and tracing the ELF profile can reveal the strength of the bonding/binding ranging from the triple bonds to weak dispersion.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
Fang完成签到,获得积分10
刚刚
cc发布了新的文献求助10
刚刚
1秒前
自尊的腐都胖子完成签到,获得积分10
1秒前
李雯雯完成签到,获得积分10
1秒前
Rocky_Qi发布了新的文献求助10
1秒前
是苗苗丫发布了新的文献求助10
1秒前
纯真寻冬发布了新的文献求助10
1秒前
3秒前
3秒前
Akim应助123采纳,获得10
4秒前
猫毛发布了新的文献求助30
4秒前
4秒前
打打应助Sandy采纳,获得10
4秒前
七少爷完成签到,获得积分10
4秒前
Fang发布了新的文献求助10
5秒前
华仔应助yuehui采纳,获得10
5秒前
6秒前
乌龙茶ICE完成签到,获得积分10
6秒前
6秒前
bioai发布了新的文献求助10
6秒前
ggjy完成签到,获得积分10
6秒前
T_完成签到,获得积分10
6秒前
7秒前
7秒前
好名字发布了新的文献求助10
7秒前
david发布了新的文献求助10
7秒前
量子星尘发布了新的文献求助10
7秒前
领导范儿应助月亮0927采纳,获得10
7秒前
8秒前
思源应助幽默与研采纳,获得10
8秒前
yuewang完成签到,获得积分10
8秒前
8秒前
chen987完成签到,获得积分10
8秒前
在水一方应助科研通管家采纳,获得10
9秒前
xxfsx应助科研通管家采纳,获得20
9秒前
9秒前
NexusExplorer应助科研通管家采纳,获得10
9秒前
慕青应助科研通管家采纳,获得10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1001
On the application of advanced modeling tools to the SLB analysis in NuScale. Part I: TRACE/PARCS, TRACE/PANTHER and ATHLET/DYN3D 500
L-Arginine Encapsulated Mesoporous MCM-41 Nanoparticles: A Study on In Vitro Release as Well as Kinetics 500
Haematolymphoid Tumours (Part A and Part B, WHO Classification of Tumours, 5th Edition, Volume 11) 400
Virus-like particles empower RNAi for effective control of a Coleopteran pest 400
Unraveling the Causalities of Genetic Variations - Recent Advances in Cytogenetics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5466072
求助须知:如何正确求助?哪些是违规求助? 4570135
关于积分的说明 14322892
捐赠科研通 4496608
什么是DOI,文献DOI怎么找? 2463448
邀请新用户注册赠送积分活动 1452319
关于科研通互助平台的介绍 1427516