Tuning quantum-dot heat engines for optimal ecological performance

We study the performance of electron-based quantum-dot heat engines using non-equilibrium thermodynamics and optimize them using an ecological performance criterion. We first globally optimize the system, assuming that we can control both the gate and drain voltages simultaneously. We then show that less general optimizations are possible, in which we control only the drain voltage. In the final part of the paper, we demonstrate, using irreversible thermodynamics, a one-to-one correspondence between the extreme condition of the ecological function and the thermodynamic equilibrium condition of the system.

Authors

Institutions

Publication Details

Journal
Journal of Applied Physics
Published
2026-02-18
DOI
https://doi.org/10.1063/5.0305514
Primary Topic
Advanced Thermodynamics and Statistical Mechanics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tuning quantum-dot heat engines for optimal ecological performance

J. J. Fernández
Journal of Applied Physics
Advanced Thermodynamics and Statistical Mechanics
article

Tuning quantum-dot heat engines for optimal ecological performance

J. J. Fernández
article en

Abstract

We study the performance of electron-based quantum-dot heat engines using non-equilibrium thermodynamics and optimize them using an ecological performance criterion. We first globally optimize the system, assuming that we can control both the gate and drain voltages simultaneously. We then show that less general optimizations are possible, in which we control only the drain voltage. In the final part of the paper, we demonstrate, using irreversible thermodynamics, a one-to-one correspondence between the extreme condition of the ecological function and the thermodynamic equilibrium condition of the system.

Journal of Applied PhysicsVol. 139(7)
Universidad Nacional de Educación a Distancia (ES)
Life in Land
Openalex Percentile: Top 49%
Advanced Thermodynamics and Statistical Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.