Hafiz Ali Rizwan , Muhammad Usman Khan , Abida Anwar , Munazza Idrees , Nasir A. Siddiqui
{"title":"基于DFT-D3的原始和锂掺杂B16N16纳米笼传感g系列神经毒剂的分子建模研究","authors":"Hafiz Ali Rizwan , Muhammad Usman Khan , Abida Anwar , Munazza Idrees , Nasir A. Siddiqui","doi":"10.1016/j.jmgm.2025.109069","DOIUrl":null,"url":null,"abstract":"<div><div>The detection and removal of toxic warfare agents, such as G-series nerve agents, is a critical area of research for environmental safety and public health. This research uses density functional theory (DFT) to address the gap in understanding the molecular-level interactions of G-series nerve agents with boron nitride nanocages (BNNC) and lithium-doped boron nitride nanocages (Li-BNNC). The investigated nanostructures exhibited high negative adsorption energies, allowing the G-series nerve agents to adsorb strongly onto the BNNC and Li-BNNC surfaces. The Li-BNNC complexes undergo the chemisorption process with the adsorption energy, ranging from −31.819 kcal/mol to −33.635 kcal/mol. The findings of frontier molecular orbitals (FMOs) and density of states (DOS) indicated that the electronic characteristics of GS@BNNC and GS@Li-BNNC had been significantly changed, resulting in a smaller energy gap and higher conductivity. The Li-doping results in much lower energy gaps in Li-BNNC systems, such as 2.707 eV for Tabun@Li-BNNC, that cause higher electrical conductivity. Tabun@Li-BNNC has the highest electrical conductivity of 4.60 × 10<sup>12</sup> among Li-doped systems, and Tabun@BNNC has a high conductivity of 2.84 × 10<sup>12</sup> among undoped BNNC systems. Li-BNNC systems have higher electrical conductivity, which makes them good sensors for detecting G-series nerve agents. These findings provide a molecular-level understanding of the effect of Li-doping on BNNC-based nanomaterials and their potential for advancing nanotechnology-driven gas sensors.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"139 ","pages":"Article 109069"},"PeriodicalIF":2.7000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A molecular modeling study of pristine and Li-doped B16N16 nanocages for sensing G-series nerve agents using DFT-D3\",\"authors\":\"Hafiz Ali Rizwan , Muhammad Usman Khan , Abida Anwar , Munazza Idrees , Nasir A. Siddiqui\",\"doi\":\"10.1016/j.jmgm.2025.109069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The detection and removal of toxic warfare agents, such as G-series nerve agents, is a critical area of research for environmental safety and public health. This research uses density functional theory (DFT) to address the gap in understanding the molecular-level interactions of G-series nerve agents with boron nitride nanocages (BNNC) and lithium-doped boron nitride nanocages (Li-BNNC). The investigated nanostructures exhibited high negative adsorption energies, allowing the G-series nerve agents to adsorb strongly onto the BNNC and Li-BNNC surfaces. The Li-BNNC complexes undergo the chemisorption process with the adsorption energy, ranging from −31.819 kcal/mol to −33.635 kcal/mol. The findings of frontier molecular orbitals (FMOs) and density of states (DOS) indicated that the electronic characteristics of GS@BNNC and GS@Li-BNNC had been significantly changed, resulting in a smaller energy gap and higher conductivity. The Li-doping results in much lower energy gaps in Li-BNNC systems, such as 2.707 eV for Tabun@Li-BNNC, that cause higher electrical conductivity. Tabun@Li-BNNC has the highest electrical conductivity of 4.60 × 10<sup>12</sup> among Li-doped systems, and Tabun@BNNC has a high conductivity of 2.84 × 10<sup>12</sup> among undoped BNNC systems. Li-BNNC systems have higher electrical conductivity, which makes them good sensors for detecting G-series nerve agents. These findings provide a molecular-level understanding of the effect of Li-doping on BNNC-based nanomaterials and their potential for advancing nanotechnology-driven gas sensors.</div></div>\",\"PeriodicalId\":16361,\"journal\":{\"name\":\"Journal of molecular graphics & modelling\",\"volume\":\"139 \",\"pages\":\"Article 109069\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of molecular graphics & modelling\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1093326325001299\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of molecular graphics & modelling","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1093326325001299","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
A molecular modeling study of pristine and Li-doped B16N16 nanocages for sensing G-series nerve agents using DFT-D3
The detection and removal of toxic warfare agents, such as G-series nerve agents, is a critical area of research for environmental safety and public health. This research uses density functional theory (DFT) to address the gap in understanding the molecular-level interactions of G-series nerve agents with boron nitride nanocages (BNNC) and lithium-doped boron nitride nanocages (Li-BNNC). The investigated nanostructures exhibited high negative adsorption energies, allowing the G-series nerve agents to adsorb strongly onto the BNNC and Li-BNNC surfaces. The Li-BNNC complexes undergo the chemisorption process with the adsorption energy, ranging from −31.819 kcal/mol to −33.635 kcal/mol. The findings of frontier molecular orbitals (FMOs) and density of states (DOS) indicated that the electronic characteristics of GS@BNNC and GS@Li-BNNC had been significantly changed, resulting in a smaller energy gap and higher conductivity. The Li-doping results in much lower energy gaps in Li-BNNC systems, such as 2.707 eV for Tabun@Li-BNNC, that cause higher electrical conductivity. Tabun@Li-BNNC has the highest electrical conductivity of 4.60 × 1012 among Li-doped systems, and Tabun@BNNC has a high conductivity of 2.84 × 1012 among undoped BNNC systems. Li-BNNC systems have higher electrical conductivity, which makes them good sensors for detecting G-series nerve agents. These findings provide a molecular-level understanding of the effect of Li-doping on BNNC-based nanomaterials and their potential for advancing nanotechnology-driven gas sensors.
期刊介绍:
The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design.
As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.