Publications

AI-Enabled Spectroscopy & Data Analytics

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1. P. Yin et al., “Decoding Complex Spectra with Machine Learning: Advances in Spectral Unmixing for Chemical Analysis.” Analytical Chemistry 98, 4432–4457 (2026). — ML-based spectral unmixing and review of analytical methodologies.

2. P. Yin et al., “SERS-Based E-Tongue Data Analysis Methods: From Spectrum Preparation to Qualitative and Quantitative Modeling.” ACS Sensors 11, 1831–1859 (2026). — Integrated spectral analysis and quantitative modeling.

3. P. Yin et al., “Quantitative Spectroscopic Characterization of Fuel Properties of Carbon-Based Materials Using Machine Learning.” Analytical Chemistry 98, 10195–10216 (2026). — ML-based prediction of material properties from spectroscopy.

4. A. Kumar et al., “Understanding SERS Spectral Shape Variability through Substrate Optics, Molecular Orientation, and Unsupervised Clustering.” Journal of Physical Chemistry C 130, 4852–4867 (2026). — Unsupervised learning and interpretation of spectral variability.

5. S. Mahmud et al., “Deep Learning-Based Cross-Device Standardization of Surface-Enhanced Raman Spectroscopy for Enhanced Bacterial Recognition.” Spectrochimica Acta Part A 347, 126931 (listed in the 2026 bibliography). — Deep-learning approaches to cross-device standardization.

6. P. Yin et al., “BioRamanNet: A Neural Network Framework for Biological Raman Spectroscopy Classification.” AI Chemistry 1, 3 (2025). — Neural-network architecture for biological Raman classification.

7. J. Cui et al., “Beyond Traditional airPLS: Improved Baseline Removal in SERS with Parameter-Focused Optimization and Prediction.” Analytical Chemistry 97, 16211–16218 (2025). — Improved spectral baseline correction.

8. T. Wang et al., “Functional Regression for SERS Spectrum Transformation Across Diverse Instruments.” Analyst 150, 460–469 (2025). — Spectral transfer and inter-instrument calibration.

9. Y. Liu et al., “Extracting True Virus SERS Spectra and Augmenting Data for Improved Virus Classification and Quantification.” ACS Sensors 10, 3941–3952 (2025). — Spectral extraction and data augmentation.

10. M. M. Bajomo et al., “Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures.” Proceedings of the National Academy of Sciences USA 119, e2211406119 (2022). — Machine-learning-based chemical signal separation.

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AI-Assisted Chemical & Biological Detection

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1. A. Kumar et al., “Label-Free Surface-Enhanced Raman Spectroscopy and Machine Learning for Rapid Classification and Quantification of Bovine Respiratory Disease Complex Viruses.” Talanta 311, 130265 (2026).

2. S. Mahmud et al., “Machine Learning-Enabled Label-Free SERS for Microbial Sensing: Toward Robust, Generalizable, and Deployable Workflows.” Talanta 311, 130241 (2026).

3. A. Kumar et al., “Detection of Bacterial Signatures from Culture Supernatants Using Surface-Enhanced Raman Spectroscopy and Machine Learning.” Sensors and Actuators Reports 11, 100480 (2026).

4. M. Hassan et al., “SERS on Analyte-Enriched Blood for Rapid, Culture-Free Sepsis Recognition and Causative Pathogen Identification with Super Operational Neural Networks.” Talanta 301, 129332 (2026).

5. J. Cui et al., “Improving Protein Quantification with SERS Superspectra and Machine Learning.” ACS Omega 11, 10843–10853 (2026).

6. Y. Liu et al., “Machine Learning-Assisted SERS Platform for Rapid and Quantitative Discrimination of Shiga Toxin-Producing E. coli Serotypes.” Biosensors 15, 740 (2025).

7. S. Belhaouari et al., “DeepRaman: Implementing Surface-Enhanced Raman Scattering Together with Cutting-Edge Machine Learning for the Differentiation and Classification of Bacterial Endotoxins.” Heliyon 11, e42550 (2025).

8. Y. Yang et al., “Multiplex Detection and Quantification of Virus Co-infections Using Label-Free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.” ACS Sensors 10, 1298–1311 (2025).

9. J. C. Rothstein et al., “Ultra-Sensitive Detection of PFASs Using Surface Enhanced Raman Scattering and Machine Learning: A Promising Approach for Environmental Analysis.” Sensors & Diagnostics 3, 1272–1284 (2024).

10. A. Kumar et al., “Precision Classification and Quantitative Analysis of Multiple Bacteria Biomarkers via Surface-Enhanced Raman Spectroscopy and Machine Learning.” Spectrochimica Acta Part A 320, 124627 (2024).

11. Y. Yang et al., “Rapid Detection of SARS-CoV-2 Variants Using ACE2-Based SERS Sensor Enhanced by CoVari Deep Learning Algorithms.” ACS Sensors 9, 3158–3169 (2024).

12. Y. Yang et al., “Advancing SERS Diagnostics in COVID-19 with Rapid, Accurate, and Label-Free Viral Load Monitoring in Clinical Specimens via SFNet Enhancement.” Advanced Materials Interfaces 11, 2400013 (2024).

13. Y. Chen et al., “Decoding PFAS Contamination via Raman Spectroscopy: A Combined DFT and Machine Learning Investigation.” Journal of Hazardous Materials 465, 133260 (2024).

14. Y. Ju et al., “Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning.” ACS Nano 17, 21251–21261 (2023).

15. Y. Yang et al., “Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.” ACS Sensors 8, 297–307 (2023).

16. Y. Yang et al., “Differentiation and Classification of Bacterial Endotoxins Based on Surface Enhanced Raman Scattering and Advanced Machine Learning.” Nanoscale 14, 8806–8817 (2022).

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AI-Driven Nanomaterial Design & Intelligent Sensors

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1. J. Zhang et al., “Deep-Learning Inversion Maps Arbitrary Design Images to Low-Cost, Efficient Nanofabrication.” ACS Nano 20, 14910–14922 (2026). — Deep-learning-assisted inverse nanofabrication.

2. X. Lin et al., “Unlocking Predictive Capability and Enhancing Sensing Performances of Plasmonic Hydrogen Sensors via Phase Space Reconstruction and Convolutional Neural Networks.” ACS Sensors 9, 3877–3888 (2024). — CNN-based prediction and enhancement of hydrogen sensor responses.

3. C. Liu et al., “Inverse Design of Plasmonic Nanohole Arrays by Combing Spectra and Structural Color in Deep Learning.” Advanced Intelligent Systems 5, 2300121 (2023). ​

AI Research Tools & Scientific Knowledge Discover

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1. F. Ma et al., “Comprehensive Open-Source Ecosystem for Raman and SERS Spectroscopy: Introducing SpectraGuru.” Analytical Chemistry 98, 11186–11196 (2026).

2. F. Ma et al., “IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review.” Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Volume 1, 674–715 (2026).

3. F. Ma et al., “SpectraGuru: A Community-Guided Path Toward Scalable Raman and SERS Analysis.” Proceedings of SPIE 13846, 1384608 (2026). DOI: 10.1117/12.3086068.