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Work and Energy for Variable Forces01:10

Work and Energy for Variable Forces

When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...

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Related Experiment Video

Updated: May 14, 2026

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM
08:59

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM

Published on: January 23, 2013

Integrating KPFM Characterisation, COMSOL Multiphysics Simulation and Physics-Informed cVAE for Multi-Polymer

T Pavan Rahul1, P S Rama Sreekanth1

  • 1School of Mechanical Engineering, VIT-AP University, Amravati 522237, Andhra Pradesh, India.

Materials (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces a new framework combining AFM, simulations, and AI to optimize triboelectric nanogenerators (TENGs). The LDPE/TPU pair shows the highest energy output, with sliding mode outperforming contact-separation.

Keywords:
COMSOL Multiphysicsatomic force microscopyconditional variational autoencoder (cVAE)triboelectric nanogenerators (TENGs)

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Area of Science:

  • Materials Science
  • Energy Harvesting
  • Nanotechnology

Background:

  • Triboelectric nanogenerators (TENGs) are promising for microscale energy harvesting.
  • Optimizing TENG design is complex due to material, geometry, and operating mode interactions.

Purpose of the Study:

  • To develop an integrated framework for predicting and optimizing TENG performance.
  • To accelerate the selection of materials and geometries for efficient TENGs.

Main Methods:

  • Utilized Atomic Force Microscopy (AFM) and Kelvin Probe Force Microscopy (KPFM) for material characterization.
  • Employed COMSOL Multiphysics simulations with KPFM-derived boundary conditions.
  • Developed a physics-informed conditional variational autoencoder (cVAE) for performance prediction.

Main Results:

  • The LDPE/TPU pair at 50 µm thickness demonstrated superior electrical output in both contact-separation and sliding modes.
  • Sliding mode TENGs achieved 25-30% higher voltages compared to contact-separation modes.
  • The cVAE model showed high accuracy (R² ≥ 0.94) and enabled generative predictions.

Conclusions:

  • The integrated framework offers a data-efficient methodology for TENG optimization.
  • This approach accelerates the discovery of high-performance TENG materials and designs.
  • The findings provide a pathway for advancing self-powered microelectronic devices.