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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

433
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
433
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

421
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
421
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

758
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
758
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

392
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
392

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Artificial Intelligence for Surface-Enhanced Raman Spectroscopy.

Xinyuan Bi1, Li Lin1, Zhou Chen1

  • 1State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.

Small Methods
|October 27, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances surface-enhanced Raman spectroscopy (SERS) for more sensitive and robust analysis. AI integration accelerates optimization and deepens understanding in SERS applications.

Keywords:
artificial intelligencebiomedicineenvironmental protectionfood safetysensingsurface-enhanced Raman spectroscopy

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Surface-enhanced Raman spectroscopy (SERS) is a sensitive analytical technique with broad applications in biomedicine, environmental protection, and food safety.
  • Continuous advancements are sought in SERS for improved sensitivity, robustness, and comprehensive sensing and imaging capabilities.
  • The complexity of SERS, involving numerous factors and large datasets, necessitates advanced computational approaches.

Purpose of the Study:

  • To review recent progress in Surface-enhanced Raman spectroscopy (SERS) by integrating Artificial Intelligence (AI).
  • To provide new insights into the challenges and future perspectives of AI-driven SERS.
  • To accelerate the development and application of SERS technology.

Main Methods:

  • Review of recent literature on the integration of AI in various aspects of SERS.
  • Analysis of AI's role in SERS substrate design, reporter molecule selection, synthesis, instrumentation, and data analysis.
  • Exploration of AI's capability in pattern recognition and high-level representation learning for spectral data.

Main Results:

  • AI demonstrates elite efficiency in accelerating systematic optimization of SERS.
  • AI facilitates a deeper understanding of fundamental physics and spectral data in SERS.
  • AI integration significantly surpasses human labor and conventional computation in SERS development.

Conclusions:

  • AI is increasingly leveraged across the SERS pipeline, from substrate design to data analysis.
  • AI offers powerful tools for handling complex SERS data and optimizing analytical performance.
  • The synergy between AI and SERS promises to fast-track advancements in sensitive and robust analytical techniques.