在SERS感知中的AI从歧视性转向生成性
Steven M Quarin1, Der Vang1, Ruxandra I Dima1
1Department of Chemistry, University of Cincinnati, Cincinnati, OH USA.
概括
人工智能 (AI) 和机器学习 (ML) 正在推进表面增强的拉曼散射 (SERS) 传感. 未来的应用将利用人工智能来设计新的SERS材料和生物材料.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 生物医学工程 生物医学工程
背景情况:
- 表面增强拉曼散射 (SERS) 是一种强大的化学检测技术.
- 当前的SERS应用程序通常依赖于歧视性分析方法.
- 开发先进的SERS能力需要创新的材料设计和增强的诊断工具.
研究的目的:
- 审查人工智能和机器学习在SERS传感中的当前和未来作用.
- 突出AI在提高SERS诊断精度方面的潜力.
- 探索用于新型SERS材料发现的生成AI方法.
主要方法:
- 在SERS中对AI/ML应用的文献综述.
- 对SERS诊断的歧视性AI/ML方法的分析.
- 讨论用于SERS材料设计的生成AI/ML.
主要成果:
- 目前,AI/ML在SERS中用于歧视性分析,增强诊断能力.
- 未来的方向涉及生成人工智能,用于设计新的SERS基质和生物材料.
- 这种整合有望带来更敏感和更具体的SERS传感平台.
结论:
- 人工智能和机器学习对SERS感知具有变革性,超越了分析,进入了材料设计.
- 生成型人工智能为高级SERS应用程序创建定制材料提供了一条途径.
- 人工智能和SERS之间的协同作用将推动诊断领域及其他领域的创新.
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