Temporal Noise as a Battery-Efficiency Booster: Design Rules from the Scalar Temporal Field Ontology (STFO)

This work applies the Scalar Temporal Field Ontology (STFO) to electrochemical systems, showing that temporal noise can improve charge transport uniformity, suppress dendrite formation, and increase cycle efficiency. Bandwidth-like suppression increases local density of states along ionic pathways, while τ-exchange mediates a retardation effect analogous to phonon-assisted conduction. We propose engineering knobs including scalar coupling γτ, controllable noise variance Var(Δτ), and τ-gradient alignment to enhance Li/Na mobility in solid state batteries. Predicted signatures include reduced overpotential, smoother plating fronts, and T-dependent noise-assisted activation. This preprint forms a conceptual and mathematical step linking STFO condensed-matter predictions to battery engineering and electrochemical optimization.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-11
DOI
https://doi.org/10.5281/zenodo.17576721
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Temporal Noise as a Battery-Efficiency Booster: Design Rules from the Scalar Temporal Field Ontology (STFO)

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Temporal Noise as a Battery-Efficiency Booster: Design Rules from the Scalar Temporal Field Ontology (STFO)

article en

Abstract

This work applies the Scalar Temporal Field Ontology (STFO) to electrochemical systems, showing that temporal noise can improve charge transport uniformity, suppress dendrite formation, and increase cycle efficiency. Bandwidth-like suppression increases local density of states along ionic pathways, while τ-exchange mediates a retardation effect analogous to phonon-assisted conduction. We propose engineering knobs including scalar coupling γτ, controllable noise variance Var(Δτ), and τ-gradient alignment to enhance Li/Na mobility in solid state batteries. Predicted signatures include reduced overpotential, smoother plating fronts, and T-dependent noise-assisted activation. This preprint forms a conceptual and mathematical step linking STFO condensed-matter predictions to battery engineering and electrochemical optimization.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
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Machine Learning in Materials Science
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