Self-optimising continuous-flow hydrothermal reactor for nanoparticle synthesis†

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Cameron Jackson, Karen Robertson, Vitaliy Sechenyh, Thomas W. Chamberlain, Richard A. Bourne and Edward Lester
{"title":"Self-optimising continuous-flow hydrothermal reactor for nanoparticle synthesis†","authors":"Cameron Jackson, Karen Robertson, Vitaliy Sechenyh, Thomas W. Chamberlain, Richard A. Bourne and Edward Lester","doi":"10.1039/D4RE00339J","DOIUrl":null,"url":null,"abstract":"<p >An autonomous continuous-flow, hydrothermal synthesis reactor, capable of self-optimising nanoparticle size using an in-line characterisation technique and machine learning is presented. The developed system is used for synthesis of hematite (α-Fe<small><sub>2</sub></small>O<small><sub>3</sub></small>) nanoparticles across three process variables, optimising for a target particle size. Optimisation is achieved in under 7 h with only 500 ml of 0.1 M Fe(NO<small><sub>3</sub></small>)<small><sub>3</sub></small>·9H<small><sub>2</sub></small>O aqueous stock solution used, and without human intervention.</p>","PeriodicalId":101,"journal":{"name":"Reaction Chemistry & Engineering","volume":" 3","pages":" 511-514"},"PeriodicalIF":3.4000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/re/d4re00339j?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reaction Chemistry & Engineering","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/re/d4re00339j","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

An autonomous continuous-flow, hydrothermal synthesis reactor, capable of self-optimising nanoparticle size using an in-line characterisation technique and machine learning is presented. The developed system is used for synthesis of hematite (α-Fe2O3) nanoparticles across three process variables, optimising for a target particle size. Optimisation is achieved in under 7 h with only 500 ml of 0.1 M Fe(NO3)3·9H2O aqueous stock solution used, and without human intervention.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Reaction Chemistry & Engineering
Reaction Chemistry & Engineering Chemistry-Chemistry (miscellaneous)
CiteScore
6.60
自引率
7.70%
发文量
227
期刊介绍: Reaction Chemistry & Engineering is a new journal reporting cutting edge research into all aspects of making molecules for the benefit of fundamental research, applied processes and wider society. From fundamental, molecular-level chemistry to large scale chemical production, Reaction Chemistry & Engineering brings together communities of chemists and chemical engineers working to ensure the crucial role of reaction chemistry in today’s world.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信