journal article·2018-07-11

Flexible nanoenergy harvester using piezo-tribo functional polymer and carbon fibre as electrodes

Sreenidhi Prabha Rajeev, Subash Cherumannil Karimuthil, Lokasani Bhanuprakash, Soney Varghese

Materials Research Express· Volume 5· Issue 7
Publisher page

Abstract

Micro-energy harvesting has gained immense interest among the research community due to the requirement for self-powering of sensor units in unapproachable environments. Use of simple, low cost, flexible energy harvester aids generation of energy from ambient environments. The present work proposes two devices under test(DUT) with different electrodes namely copper(DUT1) and carbon fibre(DUT2), polymers Polyvinylidene fluoride (PVDF), Polydimethylsiloxane (PDMS) as a functional charge generating materials. The piezoelectric and triboelectric characterisation of polymer films is performed using Dynamic contact mode electrostatic force microscopy(DC-EFM). The proposed DUT1 and DUT2 generates an 8 Vpp and 9 Vpp during human finger tapping. The qualitative and quantitative analysis of DUT1 and DUT2 generated an output voltage of (8.5 V, 12.35 V) (high pressure),(13.5 V, 14.1 V) (285 rpm) respectively. The carbon fibre electrode based flexible device generated an output voltage of 9 V, 10 V and 2.275 V when subjected to biomechanical operations such as finger assisted tapping, press and release as well as bending operations. The device with copper electrode generated 0.32 nJ of energy and power density per unit area of 2.37 pW cm−2 in a single tap cycle. The DUT2 generated thrice the energy and power density than that of DUT1. The DUT2 thus promises to be an efficient, low-cost, flexible energy harvester with PVDF as well as PDMS polymers as a functional layer.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Innovative Energy Harvesting Technologies · Conducting polymers and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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