Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces-Reference-Cited by-同舟云学术

Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces

Published:2023-09-21 Issue:39 Volume:15 Page:46460-46469
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Chung Tsai-Chun¹²,Hsu Ya-Hsin³,Chen Tianle¹,Li Yang¹,Yang Haochen¹,Yu Jin-Xiu³,Lee I-Chi⁴^ORCID,Lai Ping-Shan²,Li Yi-Chen Ethan³^ORCID,Chen Po-Yen¹⁵^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, United States

2. Department of Chemistry, National Chung Hsing University, Taichung 402202, Taiwan

3. Department of Chemical Engineering, Feng Chia University, Taichung 407102, Taiwan

4. Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan

5. Maryland Robotics Center, College Park, Maryland 20742, United States

Funder

Air Force Office of Scientific Research

Maryland Energy Innovation Institute, University of Maryland

University of Maryland

Ministry of Science and Technology, Taiwan

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c08070

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