Author ORCID Identifier
https://orcid.org/0000-0001-8094-5327
Date of Award
2024
Document Type
Thesis (Master's)
Department or Program
Chemistry
First Advisor
Dr. Katherine Mirica
Abstract
In this present work, we explore the metallization of textiles and sutures for the development of a wearable portable modular platform technology for non-invasive wound healing monitoring. Currently technology for the metallization of nonconductive materials have been mainly on a 2D surfaces which restricts the bulk metallization of textile surfaces which require specialized equipment and operators which are costly, energy ineffective, and prevents miniaturization. Metallization of nonconductive fabrics are achieved through a conventional electroless deposition of copper nanoparticle species on 3D surfaces to create electrodes. These conductive textiles and sutures were characterized by pXRD, ATR-IR, SEM-EDS, and XPS and were found to contain copper zero, cuprous oxide, and copper oxide aggregated into nanoparticles. The conductive metallized surfaces of fibers with resistance values in Ω-MΩ range provide a platform for the restructuring of these surfaces into Metal Organic Frameworks as sensing materials in the electroanalysis of disease related biomarker. NO and UA are key biomarkers in chronic wound healing process of homeostasis, inflammation, proliferation, and remodeling. Sensing these biomarkers will be crucial for the future novel technological approaches to eliminating painful traditional methods of wound monitoring. In our approach copper was the first metal chosen because Copper Hexahydroxytriphenylene, Cu3(HHTP)2, a known electrochemical sensor of NO and UA. Our data suggest that electroless deposition of copper is a great candidate for creating conductive fibers for electrochemical sensing.
Recommended Citation
Williams, Lori-Ann, "Building Smart Textile Sensors from Bottom-up: Chemically Precise Self Assembled Materials on Textiles as a Modular Platform for Electroanalysis" (2024). Dartmouth College Master’s Theses. 175.
https://digitalcommons.dartmouth.edu/masters_theses/175
