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Rapid SERS Analysis: From Laboratory to Real Sample.

Haoyu Guo^1,2, Shengfu Zhi¹, Zeyu Zhao^1,2

¹State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.

ACS Applied Materials & Interfaces

|June 2, 2025

Summary

This summary is machine-generated.

This review focuses on rapid surface-enhanced Raman spectroscopy (SERS) analysis for real-world samples. It outlines key steps including reliable lab analysis, fast sample preparation, and AI integration for improved results.

Keywords:

SERS artificial intelligence molecular interaction rapid analysis rapid sample pretreatment reliable qualitative and selectively sensitive analysis spectrum-structure correlation

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Area of Science:

Analytical Chemistry
Spectroscopy
Nanotechnology

Background:

Surface-enhanced Raman spectroscopy (SERS) has seen extensive reviews on its history, mechanisms, and applications.
A gap exists in summarizing methods for rapid SERS analysis of real samples.

Purpose of the Study:

To consolidate critical advancements in developing rapid SERS analysis for real-world applications.
To provide a focused overview of essential steps for practical SERS implementation.

Main Methods:

Reviewing literature on laboratory-based SERS analysis.
Examining techniques for rapid sample pretreatment.
Investigating the role of Artificial Intelligence (AI) in SERS data analysis.

Main Results:

Identification of three pivotal steps for rapid SERS analysis.
Emphasis on establishing reliable and sensitive SERS methods.
Highlighting the necessity of efficient sample preparation and AI integration.

Conclusions:

Rapid SERS analysis requires robust laboratory methods, streamlined pretreatment, and AI enhancement.
This review offers a roadmap for accelerating SERS adoption in real-sample analysis.
Future directions emphasize AI-driven improvements for qualitative and quantitative SERS.