Ground-Based Remote Sensing and Machine Learning for in Situ and Noninvasive Monitoring and Identification of Salts and Moisture in Historic Buildings.
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| Title: | Ground-Based Remote Sensing and Machine Learning for in Situ and Noninvasive Monitoring and Identification of Salts and Moisture in Historic Buildings. |
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| Authors: | Kogou S; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Li Y; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Cheung CS; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Han XN; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Liggins F; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Shahtahmassebi G; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K., Thickett D; English Heritage, Rangers House, Chesterfield Walk, London SE10 8QX, U.K., Liang H; School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K. |
| Source: | Analytical chemistry [Anal Chem] 2025 Mar 11; Vol. 97 (9), pp. 5008-5013. Date of Electronic Publication: 2025 Feb 25. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 0370536 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-6882 (Electronic) Linking ISSN: 00032700 NLM ISO Abbreviation: Anal Chem Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1520-6882 |
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| DOI: | 10.1021/acs.analchem.4c05581 |
