AI-Driven Data Extraction and AGREE-Based Greenness Evaluation of Analytical Methods for Natural Products: Insights from Selected Studies
- DOI
- 10.1021/acs.analchem.6c02529
- Published
- 2026-07-29
- Container
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.analchem.6c02529,
title = {AI-Driven Data
Extraction and AGREE-Based Greenness
Evaluation of Analytical Methods for Natural Products: Insights from
Selected Studies},
author = {Paweł Świt and Fabian Hammerle and Markus Ganzera},
year = {2026},
journal = {Analytical Chemistry},
doi = {10.1021/acs.analchem.6c02529},
url = {https://doi.org/10.1021/acs.analchem.6c02529}
}RIS
TY - JOUR TI - AI-Driven Data Extraction and AGREE-Based Greenness Evaluation of Analytical Methods for Natural Products: Insights from Selected Studies AU - Paweł Świt AU - Fabian Hammerle AU - Markus Ganzera PY - 2026 JO - Analytical Chemistry DO - 10.1021/acs.analchem.6c02529 UR - https://doi.org/10.1021/acs.analchem.6c02529 ER -
APA
Świt, P., Hammerle, F., & Ganzera, M. (2026). AI-Driven Data Extraction and AGREE-Based Greenness Evaluation of Analytical Methods for Natural Products: Insights from Selected Studies. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.6c02529
Source records
- crossref · retrieved 2026-09-26T00:15:14.312Z