4D Energy-Flow Tomography: Correlating Force, Chemical State, and Emission in a Working Molecular Machine

Fabricate nanoscale plasmonic antennas around a single molecular rotor or DNA motor using AFM tip-based nanomachining to enhance optical readouts. Simultaneously perform quantitative electrostatic force tomography and fuse MALDI/ToF-SIMS chemical maps via physically constrained machine learning to infer time-resolved torque, local fields, and chemical state. Use these data to fit information-thermodynamic models and test nonlinear response predictions. This platform uniquely unifies mechanical, electrostatic, and chemical observables in situ, closing the loop between experiment and theory for soft molecular machines. It operationalizes the 'flow of energy and information' framework by directly measuring multiple variables in a single device and leverages nanoarchitected plasmonics to enhance signal-to-noise without altering machine chemistry. Complementary to π–M–π rotors and DNA machines, it provides a universal metrology toolkit. The approach promises calibrated, multimodal data to extract dissipation channels, quantify efficiency at maximum power, and identify hidden state transitions controlling performance. Potential impact includes a gold-standard methodology for designing and validating next-generation nanoscale machines, accelerating materials-by-design and AI-guided optimization.

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