Multi-Scale Architecture-Driven Design of Ultra-Soft Conductive Composites for Flexible and Wearable Sensing Applications

Flexible and wearable sensing systems require materials that combine high mechanical compliance with reliable electrical performance under repeated deformation. Conventional soft conductive composites rely on stochastic percolation networks, where conductive fillers are randomly distributed within an elastomer matrix, limiting control over electrical properties, reproducibility, and sensitivity. This thesis presents a multi-scale, architecture-driven framework for designing ultra-soft conductive composites based on bottlebrush elastomers (BBE), a class of materials with elastic moduli approaching those of soft biological tissues, and investigates how conductive network organization across nano-, meso-and macroscale dimensions governs electrical and electromechanical performance. Four studies were conducted, each targeting a key design parameter. First, carbon nanotube (CNT)-embedded BBE composites were systematically evaluated as a function of CNT loading and crosslinking density, establishing compositional trade-offs between conductivity, sensitivity, and fatigue stability. A base-to-crosslinker ratio of 600:1 with 0.6 wt.% CNT was identified as the optimal baseline composition, providing balanced performance across bending, pressure, and cyclic testing. Second, hybrid silver nanowires (AgNW) and CNT-BBE sandwich structures were developed using spray-deposited interlayers to create tunable nanoscale conductive networks. Network density was controlled via deposition iterations, with eight spray cycles yielding the most balanced performance in terms of conductivity, strain sensitivity, and wearable motion sensing. Third, structured conductive mesh architectures were introduced as a mesoscale alternative to stochastic percolation networks. The mesh exhibited the lowest in-plane resistance and enhanced response under distributed deformation, while percolation-based AgNW networks showed superior sensitivity under localized, high-curvature strain, demonstrating that deformation mode is a critical factor in architecture selection. Finally, the influence of device geometry was investigated by comparing regular, thin, and double-sandwich configurations of CNT/BBE-AgNW composites. Reducing thickness and increasing interlayer density improved through-plane conductivity but reduced pressure sensitivity, reinforcing a consistent trade-off between electrical conductivity and electromechanical responsiveness. Overall, these findings demonstrate that conductive pathway organization and device architecture are as critical as material composition in determining soft sensor performance. This work establishes a set of design principles for the rational development of next-generation wearable sensing systems.

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Biomedical Engineering

Grantor

University of Houston

Year dc:date.issued

2026

Author dc:creator

• Jackson, Abby Isabel

Advisor dc:contributor.advisor

• Du, Yuncheng

Committee members dc:contributor.committeemember

• Schultz, Jerome S.
• Horton, Renita

• Silver nanowires
• Carbon nanotubes
• Piezoresistive sensors
• Stretchable electronics
• Bottlebrush elastomer (BBE)
• Conductive composites
• Wearable sensing

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/21537

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/21537