Giant Thermal Conductivity Enhancement in Multilayer MoS2 under Highly Compressive Strain
Xianghai Meng (1), Tribhuwan Pandey (2), Suyu Fu (3), Jing Yang (3),, Jihoon Jeong (1), Ke Chen (1), Akash Singh (2), Feng He (1, 4), Xiaochuan, Xu (5), Abhishek K. Singh (2), Jung-Fu Lin (3, 6), Yaguo Wang (1, 4), ((1) Department of Mechanical Engineering

TL;DR
Applying high compressive strain to multilayer MoS2 significantly enhances its cross-plane thermal conductivity, transforming its heat dissipation from 2D to nearly isotropic 3D, with implications for electronic thermal management.
Contribution
This study demonstrates a novel strain engineering method to drastically increase thermal conductivity in multilayer MoS2, revealing new pathways for tuning 2D material properties.
Findings
Cross-plane thermal conductivity increases by 12 times under 9% compressive strain.
Interlayer interactions and phonon dispersions are key to the conductivity enhancement.
Thermal conductivity becomes nearly isotropic, enabling 3D heat dissipation.
Abstract
Multilayer MoS2 possesses highly anisotropic thermal conductivities along in-plane and cross-plane directions that could hamper heat dissipation in electronics. With about 9% cross-plane compressive strain created by hydrostatic pressure in a diamond anvil cell, we observed about 12 times increase in the cross-plane thermal conductivity of multilayer MoS2. Our experimental and theoretical studies reveal that this drastic change arises from the greatly strengthened interlayer interaction and heavily modified phonon dispersions along cross-plane direction, with negligible contribution from electronic thermal conductivity, despite its enhancement of 4 orders of magnitude. The anisotropic thermal conductivity in the multilayer MoS2 at ambient environment becomes almost isotropic under highly compressive strain, effectively transitioning from 2D to 3D heat dissipation. This strain tuning…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
