Physics-governed executable modelling of triboelectric nanogenerators

Researchers have developed TENG-CLAW, a unified computational framework for simulating triboelectric nanogenerators that bridges analytical theories and finite-geometry numerical solvers. The physics-governed platform establishes a charge-defined hierarchy to enable reproducible, traceable TENG research and device design across disparate simulation workflows.

Triboelectric nanogenerators represent an emerging energy harvesting technology with potential applications in wearable devices, IoT sensors, and sustainable power generation. The fragmentation of TENG research across disconnected analytical and numerical approaches has hindered quantitative advancement in the field. This work addresses a fundamental infrastructure gap by consolidating disparate modeling methodologies into a single executable framework.

The TENG-CLAW platform establishes a self-consistent electrostatic hierarchy using charge states as defining variables, connecting analytical approximations for uniform fields with numerical methods required for complex geometries. This approach enables researchers to trace outputs directly to explicit physical parameters, boundary conditions, and computational routes—addressing reproducibility challenges endemic to computational materials science. The framework converts research requests into physically admissible tasks, ensuring generated simulations maintain physical validity.

For the broader energy-harvesting and materials science communities, this infrastructure advance enables systematic device optimization and accelerates the transition from laboratory prototypes to engineered systems. Reproducible simulation frameworks reduce experimental iteration cycles and improve design predictability. The platform's emphasis on physics-governed workflows positions it as a reference implementation for computational materials research.

Future development should focus on extending TENG-CLAW's capabilities to encompass material nonlinearities, temperature dependencies, and coupling with external circuit elements. Integration with machine learning frameworks for inverse design and parameter optimization could further democratize TENG research. Community adoption will determine whether this becomes the standard reference platform or remains specialized software within niche research groups.

• →TENG-CLAW unifies fragmented analytical and numerical approaches into a single physics-governed simulation platform for triboelectric nanogenerators.
• →The framework uses charge-defined state variables to connect infinite-plate analytical limits with finite-geometry numerical formulations.
• →Explicit traceability to physical parameters and boundary conditions enables reproducible research infrastructure previously absent in TENG modeling.
• →The platform accelerates quantitative TENG research by automating conversion of research requests into physically admissible simulation tasks.
• →Reproducible simulation infrastructure reduces experimental iteration cycles and enables physics-guided device design optimization.