Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran

电子电离 电离 碎片(计算) 化学 质谱法 离解(化学) 质谱 原子物理学 电子 离子 分子 分析化学(期刊) 物理 物理化学 有机化学 核物理学 色谱法 计算机科学 操作系统
作者
Tomasz J. Wąsowicz,M. Jurkowski,Allison Harris,Ivan Ljubić
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:161 (6) 被引量:1
标识
DOI:10.1063/5.0218160
摘要

The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10–90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10−16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5–140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm.
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
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
陈静怡发布了新的文献求助10
1秒前
hbgsns完成签到,获得积分10
2秒前
2秒前
无花果应助limz采纳,获得10
2秒前
WN发布了新的文献求助10
3秒前
等待汉堡完成签到,获得积分10
3秒前
4秒前
12345678完成签到 ,获得积分20
4秒前
5秒前
迷人的妙晴完成签到,获得积分20
5秒前
6秒前
zxz发布了新的文献求助30
7秒前
科研通AI5应助细心悟空采纳,获得10
8秒前
刘总完成签到,获得积分10
8秒前
yfy发布了新的文献求助10
9秒前
炸炸桃发布了新的文献求助10
9秒前
bbbbb完成签到,获得积分10
9秒前
晚庭落秋风应助GAO采纳,获得10
10秒前
蔺契完成签到 ,获得积分10
10秒前
11秒前
沃研完成签到 ,获得积分10
11秒前
CCCC完成签到,获得积分10
13秒前
科目三应助D_D采纳,获得10
13秒前
vivian完成签到,获得积分10
14秒前
ethanza发布了新的文献求助10
15秒前
李爱国应助淡定尔安采纳,获得10
15秒前
汉堡包应助陈嘻嘻嘻嘻采纳,获得10
16秒前
Hello应助mai采纳,获得10
16秒前
EED发布了新的文献求助10
17秒前
17秒前
孟婆的碗完成签到,获得积分20
17秒前
大模型应助不安的念采纳,获得10
18秒前
Jasper应助妮妮采纳,获得10
18秒前
18秒前
19秒前
善学以致用应助bububu采纳,获得10
19秒前
吭哧吭哧发布了新的文献求助10
19秒前
iris关注了科研通微信公众号
19秒前
苏卿应助NL采纳,获得10
19秒前
20秒前
高分求助中
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Population Genetics 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Theory of Block Polymer Self-Assembly 750
지식생태학: 생태학, 죽은 지식을 깨우다 700
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3495290
求助须知:如何正确求助?哪些是违规求助? 3080457
关于积分的说明 9163920
捐赠科研通 2773604
什么是DOI,文献DOI怎么找? 1522069
邀请新用户注册赠送积分活动 705687
科研通“疑难数据库(出版商)”最低求助积分说明 703012