Mesoporous‐Shell Monolayer Plasmonic Architecture Enables Quantitative and Decision‐Guided SERS
- 1Hefei Cancer Hospital of CAS Institute of Health and Medical Technology Hefei Institutes of Physical Science Chinese Academy of Sciences (CAS) Hefei Anhui China
- 2University of Science and Technology of China Hefei China
- 3Beijing Institute of Radiation Medicine Beijing China
Abstract
Quantitative surface‐enhanced Raman spectroscopy (SERS) has long been impeded by stochastic hotspot formation, signal instability, and limited chemical fidelity in complex environments. Overcoming these intrinsic limitations requires structural control at the nanoscale rather than incremental improvements in signal amplification. Here, we introduce a mesoporous‐shell monolayer plasmonic architecture that enables quantitative and decision‐guided SERS by integrating deterministic hotspot geometry with regulated molecular accessibility and chemical stabilization of photosensitive analytes. This architecture produces predictable and reproducible SERS responses, shifting SERS from stochastic enhancement toward quantitative sensing. Using chemotherapeutic drugs as representative small‐molecule targets, the platform demonstrates ultrasensitive, multiplexed, and reproducible detection in standard solutions and complex biological matrices. Machine learning–assisted spectral analysis further enables accurate concentration prediction in patient blood samples in close agreement with liquid chromatography–mass spectrometry. Importantly, SERS‐guided dose adjustment in an orthotopic breast cancer mouse model maintains therapeutic efficacy while significantly reducing systemic toxicity, establishing a direct link between plasmonic sensing and decision‐guided intervention. This work defines a materials‐based framework for quantitative and decision‐enabled SERS, in which mesoporous‐shell monolayer architectures transform plasmonic nanostructures into functional platforms for real‐world molecular quantification and feedback‐controlled biomedical applications.
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BibTeX
@article{huang2026mesoporousshell,
title = {{Mesoporous‐Shell Monolayer Plasmonic Architecture Enables Quantitative and Decision‐Guided SERS}},
author = {Guangyao Huang and Ying Wang and Yue Fang and Jingyuan Shao and Sensen Hao and Mancun Zhao and Yuan Yao and Junping Wang and Wei Zhou and Hongzhi Wang},
journal = {Advanced Science},
year = {2026},
month = aug,
doi = {10.1002/advs.77299},
url = {https://doi.org/10.1002/advs.77299},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Mesoporous‐Shell Monolayer Plasmonic Architecture Enables Quantitative and Decision‐Guided SERS
AU - Guangyao Huang
AU - Ying Wang
AU - Yue Fang
AU - Jingyuan Shao
AU - Sensen Hao
AU - Mancun Zhao
AU - Yuan Yao
AU - Junping Wang
AU - Wei Zhou
AU - Hongzhi Wang
PY - 2026
DA - 2026/08/24
JO - Advanced Science
DO - 10.1002/advs.77299
UR - https://doi.org/10.1002/advs.77299
AB - Quantitative surface‐enhanced Raman spectroscopy (SERS) has long been impeded by stochastic hotspot formation, signal instability, and limited chemical fidelity in complex environments. Overcoming these intrinsic limitations requires structural control at the nanoscale rather than incremental improvements in signal amplification. Here, we introduce a mesoporous‐shell monolayer plasmonic architecture that enables quantitative and decision‐guided SERS by integrating deterministic hotspot geometry with regulated molecular accessibility and chemical stabilization of photosensitive analytes. This architecture produces predictable and reproducible SERS responses, shifting SERS from stochastic enhancement toward quantitative sensing. Using chemotherapeutic drugs as representative small‐molecule targets, the platform demonstrates ultrasensitive, multiplexed, and reproducible detection in standard solutions and complex biological matrices. Machine learning–assisted spectral analysis further enables accurate concentration prediction in patient blood samples in close agreement with liquid chromatography–mass spectrometry. Importantly, SERS‐guided dose adjustment in an orthotopic breast cancer mouse model maintains therapeutic efficacy while significantly reducing systemic toxicity, establishing a direct link between plasmonic sensing and decision‐guided intervention. This work defines a materials‐based framework for quantitative and decision‐enabled SERS, in which mesoporous‐shell monolayer architectures transform plasmonic nanostructures into functional platforms for real‐world molecular quantification and feedback‐controlled biomedical applications.
SN - 2198-3844
ER -