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抗菌和等离子体 Ag-CuO 纳米结构上细菌的崩解和机器学习辅助鉴定。

Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuO Nanostructures.

机构信息

ERNAM─Erciyes University Nanotechnology Application and Research Center, Kayseri 38039, Turkey.

Department of Electronic Engineering, Trinity College Dublin, Dublin 2 College Green, Dublin 2, Ireland.

出版信息

ACS Appl Mater Interfaces. 2023 Mar 8;15(9):11563-11574. doi: 10.1021/acsami.2c22003. Epub 2023 Feb 21.

DOI:10.1021/acsami.2c22003
PMID:36890693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9999350/
Abstract

Bacteria cause many common infections and are the culprit of many outbreaks throughout history that have led to the loss of millions of lives. Contamination of inanimate surfaces in clinics, the food chain, and the environment poses a significant threat to humanity, with the increase in antimicrobial resistance exacerbating the issue. Two key strategies to address this issue are antibacterial coatings and effective detection of bacterial contamination. In this study, we present the formation of antimicrobial and plasmonic surfaces based on Ag-CuO nanostructures using green synthesis methods and low-cost paper substrates. The fabricated nanostructured surfaces exhibit excellent bactericidal efficiency and high surface-enhanced Raman scattering (SERS) activity. The CuO ensures outstanding and rapid antibacterial activity within 30 min, with a rate of >99.99% against typical Gram-negative and Gram-positive bacteria. The plasmonic Ag nanoparticles facilitate the electromagnetic enhancement of Raman scattering and enables rapid, label-free, and sensitive identification of bacteria at a concentration as low as 10 cfu/mL. The detection of different strains at this low concentration is attributed to the leaching of the intracellular components of the bacteria caused by the nanostructures. Additionally, SERS is coupled with machine learning algorithms for the automated identification of bacteria with an accuracy that exceeds 96%. The proposed strategy achieves effective prevention of bacterial contamination and accurate identification of the bacteria on the same material platform by using sustainable and low-cost materials.

摘要

细菌会引起许多常见感染,也是历史上许多导致数百万人丧生的疫情爆发的罪魁祸首。临床、食物链和环境中无生命表面的污染对人类构成了重大威胁,而抗菌药物耐药性的增加使问题更加严重。解决这个问题的两个关键策略是抗菌涂层和有效检测细菌污染。在这项研究中,我们使用绿色合成方法和低成本的纸张基底,展示了基于 Ag-CuO 纳米结构的抗菌和等离子体表面的形成。所制造的纳米结构表面表现出优异的杀菌效率和高表面增强拉曼散射(SERS)活性。CuO 确保了在 30 分钟内具有出色和快速的抗菌活性,对典型的革兰氏阴性菌和革兰氏阳性菌的杀菌率>99.99%。等离子体 Ag 纳米粒子促进了拉曼散射的电磁增强,并能够在低至 10 cfu/mL 的浓度下快速、无标记和灵敏地识别细菌。在如此低的浓度下对不同菌株的检测归因于纳米结构导致的细菌细胞内成分的浸出。此外,SERS 与机器学习算法相结合,可实现对细菌的自动识别,准确率超过 96%。该策略通过使用可持续且低成本的材料,在同一材料平台上实现了对细菌的有效预防和准确识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/b50201a6b0b6/am2c22003_0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/7f7d993a857c/am2c22003_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/895240d444d8/am2c22003_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/51228da3db30/am2c22003_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/1b8eea95eb2f/am2c22003_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/dee59182daae/am2c22003_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/d082797b7822/am2c22003_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/b50201a6b0b6/am2c22003_0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/7f7d993a857c/am2c22003_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/895240d444d8/am2c22003_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/51228da3db30/am2c22003_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/1b8eea95eb2f/am2c22003_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/dee59182daae/am2c22003_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/d082797b7822/am2c22003_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f62/9999350/b50201a6b0b6/am2c22003_0008.jpg

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