Physics-Informed Machine Learning Overcomes Particle-Size Nonlinearities That Confound the Raman Spectrometry of Granular Carbonaceous Materials.
- DOI
- 10.1021/acs.analchem.6c02307
- Published
- 2026 Oct 20
- Container
- Analytical chemistry
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.analchem.6c02307,
title = {Physics-Informed Machine Learning Overcomes Particle-Size Nonlinearities That Confound the Raman Spectrometry of Granular Carbonaceous Materials.},
author = {Brown GW and Solomatova NV and Kowal MD and Larose M and Cole C and Jurinovic N and Shokatian S and Grant E},
year = {2026},
journal = {Analytical chemistry},
doi = {10.1021/acs.analchem.6c02307},
url = {https://doi.org/10.1021/acs.analchem.6c02307}
}RIS
TY - JOUR TI - Physics-Informed Machine Learning Overcomes Particle-Size Nonlinearities That Confound the Raman Spectrometry of Granular Carbonaceous Materials. AU - Brown GW AU - Solomatova NV AU - Kowal MD AU - Larose M AU - Cole C AU - Jurinovic N AU - Shokatian S AU - Grant E PY - 2026 JO - Analytical chemistry DO - 10.1021/acs.analchem.6c02307 UR - https://doi.org/10.1021/acs.analchem.6c02307 ER -
APA
GW, B., NV, S., MD, K., M, L., C, C., N, J., S, S., & E, G. (2026). Physics-Informed Machine Learning Overcomes Particle-Size Nonlinearities That Confound the Raman Spectrometry of Granular Carbonaceous Materials.. Analytical chemistry. https://doi.org/10.1021/acs.analchem.6c02307
Source records
- pubmed · retrieved 2026-11-10T13:03:41.365Z