Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions

IF 0.4 4区 物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY Moscow University Physics Bulletin Pub Date : 2024-01-17 DOI:10.3103/s0027134923070287
O. E. Sarmanova, G. N. Chugreeva, K. A. Laptinskiy, S. A. Burikov, S. A. Dolenko, T. A. Dolenko
{"title":"Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions","authors":"O. E. Sarmanova, G. N. Chugreeva, K. A. Laptinskiy, S. A. Burikov, S. A. Dolenko, T. A. Dolenko","doi":"10.3103/s0027134923070287","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this study, to create a carbon dots-based multimodal nanosensor of metal ions, a new approach to solving the inverse problem of fluorescence spectroscopy is presented. The problem is to simultaneously determine the concentration of heavy metal ions Cr<span>\\({}^{3+}\\)</span>, Ni<span>\\({}^{2+}\\)</span>, Cu<span>\\({}^{2+}\\)</span>, and nitrate anions NO<span>\\({}^{-}_{3}\\)</span> in water by carbon dots (CDs) fluorescence spectra. A method of spectral data augmentation is proposed. It is based on the generation of excitation-emission matrices of CDs fluorescence from the noise vector using variational autoencoders and further determination of ion concentration corresponding to the generated matrices with convolutional neural networks. Implementing the proposed approach allowed reducing the mean absolute error in determining the concentration of ions by 60<span>\\(\\%\\)</span> for Cr<span>\\({}^{3+}\\)</span>, by 41<span>\\(\\%\\)</span> for Ni<span>\\({}^{2+}\\)</span>, by 62<span>\\(\\%\\)</span> for Cu<span>\\({}^{2+}\\)</span>, and by 48<span>\\(\\%\\)</span> for NO<span>\\({}^{-}_{3}\\)</span>.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Moscow University Physics Bulletin","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3103/s0027134923070287","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

In this study, to create a carbon dots-based multimodal nanosensor of metal ions, a new approach to solving the inverse problem of fluorescence spectroscopy is presented. The problem is to simultaneously determine the concentration of heavy metal ions Cr\({}^{3+}\), Ni\({}^{2+}\), Cu\({}^{2+}\), and nitrate anions NO\({}^{-}_{3}\) in water by carbon dots (CDs) fluorescence spectra. A method of spectral data augmentation is proposed. It is based on the generation of excitation-emission matrices of CDs fluorescence from the noise vector using variational autoencoders and further determination of ion concentration corresponding to the generated matrices with convolutional neural networks. Implementing the proposed approach allowed reducing the mean absolute error in determining the concentration of ions by 60\(\%\) for Cr\({}^{3+}\), by 41\(\%\) for Ni\({}^{2+}\), by 62\(\%\) for Cu\({}^{2+}\), and by 48\(\%\) for NO\({}^{-}_{3}\).

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
用卷积神经网络解码碳点水溶液的荧光激发-发射矩阵,创建金属离子多模态纳米传感器
摘要 在本研究中,为了创建基于碳点的金属离子多模态纳米传感器,提出了一种解决荧光光谱逆问题的新方法。问题是通过碳点荧光光谱同时测定水中重金属离子 Cr\({}^{3+}\), Ni\({}^{2+}\), Cu\({}^{2+}\) 和硝酸根阴离子 NO\({}^{-}_{3}\) 的浓度。本文提出了一种光谱数据增强方法。该方法的基础是利用变异自动编码器从噪声矢量中生成碳点荧光的激发-发射矩阵,并利用卷积神经网络进一步确定与生成的矩阵相对应的离子浓度。采用所提出的方法可以将确定离子浓度的平均绝对误差减少60(\%)(对于Cr({}^{3+}\)),减少41(\%)(对于Ni({}^{2+}\)),减少62(\%)(对于Cu({}^{2+}\)),减少48(\%)(对于NO({}^{-}_{3}\))。
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
Moscow University Physics Bulletin
Moscow University Physics Bulletin PHYSICS, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
129
审稿时长
6-12 weeks
期刊介绍: Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.
期刊最新文献
Existence and Stability of a Stationary Solution in a Two-Dimensional Reaction-Diffusion System with Slow and Fast Components Machine Learning in the Problem of Extrapolating Variational Calculations in Nuclear Physics New Version of the Experimental Setup for the Measurement of $${{\gamma}}$$ -Quantum Emission Cross Sections in Nuclear Reactions Induced by 14.1 MeV Neutrons Calculation of Surface Binding Energy in Ni $${}_{\boldsymbol{x}}$$ Pd $${}_{\boldsymbol{y}}$$ Alloys Using Density Functional Theory Effect of Cluster Ion Bombardment on the Roughly Polished Surface of Single-Crystal Germanium Wafers
×
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