Thermal resistance of an interfacial molecular layer by first-principles molecular dynamics.

J Chem Phys

Univ. Lille, CNRS, Centrale Lille, Yncréa ISEN, Univ. Polytechnique Hauts-de-France, UMR 8520, IEMN, F-59000 Lille, France.

Published: August 2020

The approach-to-equilibrium molecular dynamics (AEMD) methodology is applied in combination with first-principles molecular dynamics to investigate the thermal transfer between two silicon blocks connected by a molecular layer. Our configuration consists of alkanes molecules strongly coupled to the silicon surfaces via covalent bonds. In phase 1 of AEMD, the two Si blocks are thermalized at high and low temperatures to form the hot and cold reservoirs. During phase 2 of AEMD, a transfer between reservoirs occurs until thermal equilibrium is reached. The transfer across the interface dominates the transient over heat conduction within the reservoirs. The value of the thermal interface conductance is in agreement with the experimental data obtained for analogous bonding cases between molecules and reservoirs. The dependence on the length of the thermal interface resistance features two contributions. One is constant (the resistance at the silicon/molecule interface), while the other varies linearly with the length of the molecular chains (diffusive transport). The corresponding value of the thermal conductivity agrees well with experiments.

Download full-text PDF

Source
http://dx.doi.org/10.1063/5.0014232DOI Listing

Publication Analysis

Top Keywords

molecular dynamics
12
molecular layer
8
first-principles molecular
8
phase aemd
8
thermal interface
8
thermal
6
molecular
6
thermal resistance
4
resistance interfacial
4
interfacial molecular
4

Similar Publications

Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.

View Article and Find Full Text PDF

Steroids are organic compounds found in all forms of biological life. Besides their structural roles in cell membranes, steroids act as signalling molecules in various physiological processes and are used to treat inflammatory conditions. It has been hypothesised that in addition to their well-characterised genomic and non-genomic pathways, steroids exert their biological or pharmacological activities an indirect, nonreceptor-mediated membrane mechanism caused by steroid-induced changes to the physicochemical properties of cell membranes.

View Article and Find Full Text PDF

Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective.

Natl Sci Rev

January 2025

Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.

The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.

View Article and Find Full Text PDF

Introduction: WhiA is a conserved protein found in numerous bacteria. It consists of an HTH DNA-binding domain linked with a homing endonuclease (HEN) domain. WhiA is one of the most conserved transcription factors in reduced bacteria of the class Mollicutes.

View Article and Find Full Text PDF

Spontaneous tumor regression is a recognized phenomenon across various cancer types. Recent research emphasizes the alterations in autoantibodies against carbonic anhydrase I (CA I) (anti-CA I) levels as potential prognostic markers for various malignancies. Particularly, autoantibodies targeting CA I and II appear to induce cellular damage by inhibiting their respective protein's catalytic functions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!