Atomic Hydrogen in Hydrogenolysis: Converting and Detoxifying Carbon-Heteroatom Bonds via Paired Electrolysis

化学 电解 氢解 电化学 杂原子 降级(电信) 碳纤维 无机化学 有机化学 电极 催化作用 材料科学 戒指(化学) 复合材料 物理化学 复合数 电信 电解质 计算机科学
作者
Qiancheng Wang,Jianqiao Xu,Shuai Wu,Mu Wang,Xingyun Zhuang,Guohong Tian,Fu Xu,Jianyun Liu,Gong Zhang,Jinghong Li
出处
期刊:Environmental Science & Technology [American Chemical Society]
标识
DOI:10.1021/acs.est.4c11680
摘要

The presence of carbon-heteroatom bonds (C–N, C–O, and C–S) significantly enhances the stability and toxicity of pollutants. Hydroxyl radicals (•OH)-mediated electrochemical processes show promise; however, the bond energies associated with carbon-heteroatom bonds exceed 200 kJ/mol, which constrains the effectiveness of oxidative degradation and detoxification. We have developed a paired electrolysis process coupling hydrogen atom (H*) generation at the cathode with •OH production at the anode. The involvement of H* and •OH in this system was first confirmed by using methylene blue (MB) as an electrochemical probe. When applied to the degradation of glyphosate (GP), which contains C–N bonds, the paired electrolysis process achieved removal efficiencies for COD, TOC, and toxicity that were twice those of individual oxidation processes. The degradation kinetics also exhibited performance that was double that of individual oxidation processes. Mass spectrometry and theoretical calculations confirmed that hydrogenolysis of H* effectively attacks high-energy C–N bonds, thereby circumventing the rate-limiting steps associated with standalone •OH oxidation, enhancing pollutant degradation and reducing toxicity. When applied to pollutants containing C–O and C–S bonds, the paired electrolysis process demonstrated improvements in COD, TOC, and toxicity removal of approximately 30%, 10%, and 20%, respectively, showcasing its multifunctionality and scalability. Seven days of practical wastewater experiments further validated the effectiveness and durability of this technology.
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
1秒前
1秒前
大模型应助wxj采纳,获得10
1秒前
wanci应助由于采纳,获得10
1秒前
1秒前
1秒前
SAKURA发布了新的文献求助10
2秒前
今后应助王军采纳,获得10
2秒前
2秒前
常璐旸发布了新的文献求助10
2秒前
2秒前
3秒前
炙热的觅荷完成签到 ,获得积分10
3秒前
桐桐应助sallltyyy采纳,获得10
3秒前
3秒前
刘传世完成签到,获得积分20
4秒前
4秒前
4秒前
5秒前
缓慢思枫发布了新的文献求助10
5秒前
思源应助禹宛白采纳,获得10
6秒前
gdgk发布了新的文献求助10
6秒前
6秒前
7秒前
由于发布了新的文献求助10
7秒前
7秒前
7秒前
8秒前
8秒前
Misaka发布了新的文献求助10
9秒前
没吃饭发布了新的文献求助10
9秒前
六六发布了新的文献求助10
10秒前
YushanH完成签到,获得积分10
10秒前
番茄的蛋发布了新的文献求助10
10秒前
刘传世发布了新的文献求助10
10秒前
Youngsy发布了新的文献求助10
10秒前
畅快的大雁完成签到,获得积分10
10秒前
查查查文献完成签到,获得积分10
10秒前
LIYI完成签到,获得积分10
10秒前
11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
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443241
求助须知:如何正确求助?哪些是违规求助? 8257113
关于积分的说明 17585207
捐赠科研通 5501710
什么是DOI,文献DOI怎么找? 2900830
邀请新用户注册赠送积分活动 1877821
关于科研通互助平台的介绍 1717487