Comparative assessment of Au- and Ag-based SERS substrates for cancer and disease diagnosis: A review on analytical performance, reproducibility, and practical challenges
4 affiliations
- 1Visva-Bharati University
- 2Tata Institute for Genetics and Society (TIGS
- 3Department of Chemical Engineering (Materials Science & Engineering
- 4Dr. Vishwanath Karad MIT World Peace University
Abstract
Surface-enhanced Raman spectroscopy (SERS), which provides ultrasensitive and molecular fingerprint analysis, has emerged as a potent analytical method for biological applications. SERS achieves signal enhancements on the order of 106 to 1015 by utilizing localized surface plasmon resonances (LSPRs) in noble metal nanoparticles, primarily gold (Au) and silver (Ag), and exhibits a femtomolar or attomolar detection limit. Developments in surface chemistry and fabrication techniques have made it easier to create a variety of plasmonic substrates, such as core-shell nanoparticles, nanowires, nanostars, and bimetallic alloys, all of which are optimized for chemical and electromagnetic enhancement. The development of Au and Ag nanoparticle-based SERS platforms and their uses in biological sensing are evaluated critically in this review, with an emphasis on development in diabetes monitoring, pathogen identification, cancer diagnostics, and biomarker detection. Reproducibility, substrate stability, and in vivo translation still present difficulties despite these developments. New strategies for overcoming these obstacles include biocompatible coatings, microfluidic integration, and spectral analysis aided by machine learning. We describe the potential uses of SERS in point-of-care and clinical diagnostics in the future, highlighting the necessity of quantitative, scalable, and repeatable sensing platforms.
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BibTeX
@misc{chatterjee2026comparative,
title = {{Comparative assessment of Au- and Ag-based SERS substrates for cancer and disease diagnosis: A review on analytical performance, reproducibility, and practical challenges}},
author = {Shrabonti Chatterjee and Joydeep Mahata and Anant Srivastava},
howpublished = {ChemRxiv},
year = {2026},
month = aug,
doi = {10.26434/chemrxiv.15008131/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008131/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Comparative assessment of Au- and Ag-based SERS substrates for cancer and disease diagnosis: A review on analytical performance, reproducibility, and practical challenges
AU - Shrabonti Chatterjee
AU - Joydeep Mahata
AU - Anant Srivastava
PY - 2026
DA - 2026/08/31
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008131/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008131/v1
AB - Surface-enhanced Raman spectroscopy (SERS), which provides ultrasensitive and molecular fingerprint analysis, has emerged as a potent analytical method for biological applications. SERS achieves signal enhancements on the order of 106 to 1015 by utilizing localized surface plasmon resonances (LSPRs) in noble metal nanoparticles, primarily gold (Au) and silver (Ag), and exhibits a femtomolar or attomolar detection limit. Developments in surface chemistry and fabrication techniques have made it easier to create a variety of plasmonic substrates, such as core-shell nanoparticles, nanowires, nanostars, and bimetallic alloys, all of which are optimized for chemical and electromagnetic enhancement. The development of Au and Ag nanoparticle-based SERS platforms and their uses in biological sensing are evaluated critically in this review, with an emphasis on development in diabetes monitoring, pathogen identification, cancer diagnostics, and biomarker detection. Reproducibility, substrate stability, and in vivo translation still present difficulties despite these developments. New strategies for overcoming these obstacles include biocompatible coatings, microfluidic integration, and spectral analysis aided by machine learning. We describe the potential uses of SERS in point-of-care and clinical diagnostics in the future, highlighting the necessity of quantitative, scalable, and repeatable sensing platforms.
ER -