Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study-Reference-Cited by-同舟云学术

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

Published:2024-08-01 Issue:32 Volume:16 Page:42862-42872
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Scott-Fordsmand Janeck J.¹^ORCID,Gomes Susana I.L.²^ORCID,Pokhrel Suman³⁴^ORCID,Mädler Lutz³⁴^ORCID,Fasano Matteo⁵^ORCID,Asinari Pietro⁵⁶^ORCID,Tämm Kaido⁷,Jänes Jaak⁷,Amorim Mónica J.B.²^ORCID

Affiliation:

1. Department of Ecoscience, Aarhus University, C.F. Mo̷llers Alle 4, DK-8000 Aarhus, Denmark

2. Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal

3. Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany

4. Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany

5. Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy

6. INRIM, Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino 10135, Italy

7. Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia

Funder

H2020 Environment

European Regional Development Fund

Funda??o para a Ci?ncia e a Tecnologia

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.4c07153

Reference49 articles.

1. World market for nanomaterials: structure and trends

2. Nanotechnology from lab to industry – a look at current trends

3. Nanotechnology: A Revolution in Modern Industry

4. Nanomaterial exposure, toxicity, and impact on human health

5. Alternative test methods for (nano)materials hazards assessment: Challenges and recommendations for regulatory preparedness