Engineering squandered cotton into eco-benign microarchitectured triboelectric films for sustainable and highly efficient mechanical energy harvesting
Source abstract
Abstract
Abstract Harvesting mechanical energy from daily life human activities has gained tremendous interest, along with the concept of waste to wealth which has been also a major concern from a few decades. In this regards, firstly, we fabricated microcrystalline cellulose (MCC) particles from the squandered cotton and they were further dissolved into biocompatible polyvinyl alcohol (PVA) to develop a triboelectric material. The resultant cellulose loaded PVA film (CPF) was employed as a positive triboelectric material to design a coin-cell type TENG, whereas the Kapton was used as a negative material. Design of such TENG using CPF can reduce the processing cost and is also significant to incorporate the waste cotton into an energy harvesting device. Besides, the effect of electrical output performance of CPF-based prototype TENG (CPF-pTENG) as a function of the concentration of MCC particles loaded into PVA was also systematically studied and optimized. Thus, the CPF-pTENG with the 2.5 wt% of cellulose added into PVA exhibited a stable and high electrical output. The electrical performance of CPF-pTENG was further enhanced by introducing the microarchitectures on the surface of CPF, and it exhibited comparatively very high voltage, current and power density values of ∼600 V, 50 μA and 84.5 W/m2, respectively. Finally, to demonstrate the practical or commercial applications, a MACPF-based coin-cell type TENG (i.e., MACPF-ccTENG) was developed to harvest the mechanical energies available in daily human life by placing it under the human-foot medial arch, which can be further utilized as a self-power system to drive various portable electronics.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Advanced Materials and Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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