Revealing the intrinsic nature of Ni-, Mn-, and Y-doped CeO2 catalysts with positive, additive, and negative effects on CO oxidation using operando DRIFTS-MS

催化作用 兴奋剂 化学 无机化学 内在活性 材料科学 有机化学 生物化学 光电子学 受体 兴奋剂
作者
Shiyu Fang,Yan Sun,Jiacheng Xu,Tiantian Zhang,Zuliang Wu,Jing Li,Erhao Gao,Wei Wang,Jiali Zhu,Lian‐Xin Dai,Weihua Liu,Buhe Zhang,Junwei Zhang,Shuiliang Yao
出处
期刊:Dalton Transactions [Royal Society of Chemistry]
卷期号:52 (45): 16911-16919 被引量:5
标识
DOI:10.1039/d3dt03001f
摘要

The catalytic activity of a transition metal (host) oxide can be influenced by doping with a second cation (dopant), but the key factors dominating the activity of the doped catalyst are still controversial. Herein, CeO2 doped with Ni, Mn, and Y catalysts prepared using aerosol pyrolysis were used to demonstrate the positive, negative, and additive effects on CO oxidation as a model reaction. Various characterization results indicated that Ni, Mn, and Y had been successfully doped into the CeO2 lattice. The catalytic activities of each catalyst for CO conversion were in the order of Ni-CeO2 > Mn-CeO2 > CeO2 > Y-CeO2. Operando DRIFTS-MS and various characterization methods were applied to reveal the intrinsic nature of the doping effects. The accumulation rate of the surface bidentate carbonates determined the CO oxidation. A definition to evaluate the doping effect was proposed, which is anticipated to be useful for developing a rational catalyst with a high CO oxidation activity. The CO oxidation reactivities displayed strong correlations with the surface factors obtained from operando DRIFTS-MS analysis and the structure factors from XPS and Raman analyses.

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
UHPC完成签到,获得积分10
刚刚
大橙子完成签到,获得积分10
1秒前
li完成签到,获得积分10
1秒前
陈大侠发布了新的文献求助10
2秒前
2秒前
巫青丝发布了新的文献求助10
2秒前
着急的菠萝完成签到,获得积分10
2秒前
2秒前
3秒前
zsl完成签到,获得积分10
3秒前
jsdiohfsiodhg完成签到,获得积分10
3秒前
3秒前
YPP发布了新的文献求助30
4秒前
隐形曼青应助务实的果汁采纳,获得10
5秒前
5秒前
6秒前
怕黑若云完成签到,获得积分10
6秒前
在水一方应助孤独雪柳采纳,获得10
6秒前
8秒前
8秒前
痴情的夏旋完成签到,获得积分10
8秒前
禹剑完成签到,获得积分10
9秒前
朵朵发布了新的文献求助10
9秒前
李洁完成签到,获得积分10
9秒前
Lidanni完成签到 ,获得积分10
10秒前
Dr_JennyZ应助深情的热狗采纳,获得10
10秒前
科研人发布了新的文献求助10
10秒前
11秒前
11秒前
向阳而生发布了新的文献求助30
11秒前
臭小子发布了新的文献求助30
12秒前
12秒前
P4完成签到,获得积分10
12秒前
13秒前
汉堡包应助YPP采纳,获得10
14秒前
CodeCraft应助清河聂氏采纳,获得10
14秒前
田様应助sanshi100采纳,获得10
14秒前
斯文败类应助朵朵采纳,获得10
14秒前
15秒前
15秒前
高分求助中
Overcoming Stigma and Bias in Obesity Management 1200
Signals, Systems, and Signal Processing 610
Software that combines deep learning,3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging 500
Bounds for Statistical Estimation in Semiparametric Models 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Ideology and Meaning-Making under the Putin Regime 450
Adhesion Science: Principles & Practice 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 6493201
求助须知:如何正确求助?哪些是违规求助? 8290657
关于积分的说明 17691570
捐赠科研通 5585361
什么是DOI,文献DOI怎么找? 2915586
邀请新用户注册赠送积分活动 1892651
关于科研通互助平台的介绍 1751038