Flexible metal-free hybrid hydrogel thermoelectric fibers
Source abstract
Abstract
Highly conductive poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) fiber has been developed as a more promising candidate compared with bulk and film to achieve wearable thermoelectric energy harvesting. Single-walled carbon nanotubes (SWCNTs) with nanostructures are considered as an effective conductive filter for the further improvement in the thermoelectric (TE) performance of PEDOT:PSS fibers. However, the previous research primarily focused on PEDOT:PSS/SWCNT films instead of fibers. In this study, PEDOT:PSS/SWCNT hybrid fibers were synthesized via gelation process, which presents a 30% enhancement of the electrical conductivity with negligible changes in Seebeck coefficient. Moreover, there was a significant increase in the Young’s modulus in accordance with the addition of an appropriate amount of SWCNTs. Thereafter, the as-prepared hybrid fibers were treated using ethylene glycol (EG) to further optimize the TE performance. Moreover, the influence of the treatment time and temperature was systematically investigated. The EG treatment resulted in a significant improvement in the electrical conductivity without a significant decrease in the Seebeck coefficient. Furthermore, the hybrid fibers were subject to EG treatment at elevated temperature, whose optimal power factor was approximately 30% higher than that of the EG-treated PEDOT:PSS/SWCNT fibers at 25 °C. This indicates that the solvent treatment at higher temperature improves the TE performance of hybrid fibers. The findings of this study can serve as a guide for the preparation of flexible and metal-free hybrid fiber with enhanced TE performance and Young’s modulus.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (62)
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