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Thalyta T. Martins

Publications and source records attributed to Thalyta T. Martins.

2 recordsLinked to original sources

Role of inertia on the performance of Brownian gyrators

Understanding the role of inertia in nanoscale heat transport is fundamental to the design of efficient nano-thermodynamics systems. In this work, we experimentally address the non-equilibrium dynamics of a Brownian gyrator, a paradigmatic model for nano-heat machines, that converts heat flow between two thermal baths into steady-state rotation. Using an optically levitated nanoparticle in a controlled vacuum environment, we study the transition from overdamped to underdamped dynamics of the gyrator. We demonstrate that, while the spatial signature of the non-equilibrium steady state vanishes as damping decreases, the rotational dynamics and energetics are optimized at a critical damping. Our findings reveal the importance of inertia for maximising the performance of nanoscale machines and provide fundamental insights into the design of efficient nano heat engines and processes.

cond-mat.stat-mech↗

Fluctuation theorems with optical tweezers: theory and practice

Fluctuation theorems, such as the Jarzynski equality and the Crooks relation, are effective tools connecting non-equilibrium work statistics and equilibrium free energy differences. However, detailed hands-on, reproducible protocols for implementing and analyzing these relations in real experiments remain scarce. This tutorial provides an end-to-end workflow for measuring, validating, and applying fluctuation theorems using a single-beam optical tweezers setup. It introduces the foundational ideas and consolidates practical calibration (PSD-based trap stiffness and position sensitivity), protocol design (forward/reverse finite-time drives over multiple amplitudes and durations), and robust estimators for free-energy difference and dissipated work, highlighting finite-sampling and rare-event effects. We demonstrate the procedures using an extensive set of measured trajectories under different conditions and provide openly accessible datasets and Python code, enabling new researchers or educators to reproduce the results with minimal effort. Beyond pedagogical validation, we discuss how these recipes translate to broader soft-matter and mesoscopic contexts. By combining user-friendly instruments with clear and transparent analysis, this work promotes the education and reliable adoption of stochastic thermodynamic methods in the curricula of physics and chemistry, as well as among emerging research teams.

cond-mat.stat-mech↗