Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment

IF 9.5 2区 材料科学 Q1 CHEMISTRY, PHYSICAL Nano Research Pub Date : 2024-09-07 DOI:10.1007/s12274-024-6946-1
Yaoyao Tang, Quan Xu, Xinyao Zhang, Rongye Zhu, Nuo Zhao, Juncheng Wang
{"title":"Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment","authors":"Yaoyao Tang, Quan Xu, Xinyao Zhang, Rongye Zhu, Nuo Zhao, Juncheng Wang","doi":"10.1007/s12274-024-6946-1","DOIUrl":null,"url":null,"abstract":"<p>Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co<sup>2+</sup> and Fe<sup>3+</sup>, with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction.</p>","PeriodicalId":713,"journal":{"name":"Nano Research","volume":"460 1","pages":""},"PeriodicalIF":9.5000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Research","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s12274-024-6946-1","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co2+ and Fe3+, with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用机器学习的主动自适应方法加速碳点合成及在牙科诊断和治疗中的应用
以最少的测试次数合成功能性纳米结构对推动材料发展至关重要。然而,包含多变量的合成方法会导致高不确定性、繁琐的尝试和高昂的人力成本。机器学习(ML)的兴起激发了人们对合理设计和合成材料的兴趣。在此,我们收集了纳米功能材料碳点(CD)的合成参数和光学特性数据集。通过建立主动自适应方法(AAM),包括模型选择、最大点筛选和实验验证,将 ML 应用于辅助合成过程,以提高光致发光量子产率(QY)。在 AAM 中首次考虑了交互式迭代策略,即通过不断获取所提供的数据来完善模型。与通过 AAM 引导获得的原始值相比,CD 显示出强烈的红色发射,QY 高达 23.3%,增强率约为 200%。此外,引导的 CD 还可用作 Co2+ 和 Fe3+ 的金属离子探针,浓度范围分别为 0-120 µM 和 0-150 µM,检测限分别为 1.17 µM 和 0.06 µM。此外,我们还利用出色的光学能力将 CD 应用于牙科诊断和治疗。它能有效地检测早期龋齿,治疗矿化结合凝胶。研究表明,在 AAM 的有效反馈环路作用下,实验验证误差逐渐减小,QY 成倍提高,这表明利用 ML 指导新型材料合成具有巨大潜力。最后,该代码是开源的,可供进一步研究新型无机材料预测时参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Nano Research
Nano Research 化学-材料科学:综合
CiteScore
14.30
自引率
11.10%
发文量
2574
审稿时长
1.7 months
期刊介绍: Nano Research is a peer-reviewed, international and interdisciplinary research journal that focuses on all aspects of nanoscience and nanotechnology. It solicits submissions in various topical areas, from basic aspects of nanoscale materials to practical applications. The journal publishes articles on synthesis, characterization, and manipulation of nanomaterials; nanoscale physics, electrical transport, and quantum physics; scanning probe microscopy and spectroscopy; nanofluidics; nanosensors; nanoelectronics and molecular electronics; nano-optics, nano-optoelectronics, and nano-photonics; nanomagnetics; nanobiotechnology and nanomedicine; and nanoscale modeling and simulations. Nano Research offers readers a combination of authoritative and comprehensive Reviews, original cutting-edge research in Communication and Full Paper formats. The journal also prioritizes rapid review to ensure prompt publication.
期刊最新文献
High-performance thermal interface materials enabled by vertical alignment of lightweight and soft graphene foams Precise synthesis of dual atom sites for electrocatalysis Liquid-encapsulated quantum dot for enhanced UV and thermal stability of quantum dot color conversion films Rational design and structural regulation of near-infrared silver chalcogenide quantum dots Exploring the potential of simple automation concepts for quantifying functional groups on nanomaterials with optical assays
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1