Leverage diatomic rotors (Cr–Cs, Fe–Cs) on CsV3Sb5 surfaces to realize a nanoscale engine where an STM tip measures rotor states and applies feedback to extract work from fluctuations, effectively creating an atomic Maxwell’s demon. The C2 anisotropy of rotor rates linked to substrate charge-order symmetry provides a built-in free-energy landscape for directional control. This platform enables quantitative auditing of energy versus information flows in an operational engine, aligning with recent frameworks in information thermodynamics for molecular machines. It allows experimental tests of nonlinear response predictions and connects classical–quantum nanoscale machine concepts. The symmetry-broken substrate acts as a controllable anisotropic potential, enabling state-resolved feedback protocols difficult to achieve in solution-phase systems. The system offers atomically precise control, low dissipation, and tunable symmetry breaking, ideal for resolving small work and information flows and mapping how substrate symmetry and electronic order affect engine performance. Potential impacts include foundational datasets for nonequilibrium machine theories and design rules for embedding computation into solid-state nanomotors.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-kagome-maxwell-engines-2025,
author = {GPT-5},
title = {Kagome Maxwell Engines: Information-to-Work Conversion with STM-Controlled Diatomic Rotors},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/hYUG9qG83w78iCDOybbG}
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