Evidence map›Paper›PMID 42364648›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2026

Manipulating Thermal Transport of 2D MOFs by Hierarchical Structural Design.

Xiaomei Wu, Sen Lu, Yuming Wen, Baoxin Liu, Fangxin Dong, Yajing Sun, Wen Shi

Abstract read
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiaomei WuSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0009-0002-6583-5423
Sen LuSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.
Yuming WenSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.
Baoxin LiuSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.
Fangxin DongSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.
Yajing SunKey Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, China.
Wen ShiSchool of Chemistry, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-4760-0893

Funding

Fundamental Research Funds for the Central Universities, Sun Yat-sen University 24xkjc003Guangdong Provincial Department of Science and Technology 2023QN10C518National Natural Science Foundation of China 22103099National Natural Science Foundation of China 22573120
6 · The paper itself

Abstract

Manipulating the thermal conductivity of 2D metal-organic frameworks (MOFs) via their pre-designable and well-defined structures plays a critical role in their broad applications. Nevertheless, the intricate vibrational structures and nontrivial phonon dynamics of these unconventional organic-inorganic hybrid solid-state materials challenge our understanding on the heat-transport mechanism. Here, by using multiscale simulations with data-driven deep neural-network potential, and taking nickel bis(dithiolene) and its derivatives as examples, we provide a unified understanding on how to modulate thermal conductivity of 2D MOFs by mapping the structure-function relationship. We find that hierarchical structural modifications endow them with both highly tunable thermal conductivities (spanning three orders of magnitude) and controllable anisotropy. Among them, atomic-scale coordination geometry modification, and molecular-level defect and disorder engineering enable a dramatic decline in thermal conductivity. The reason for the former is the strengthened vibrational anharmonicity, while the latter is attributed to the localized and softened vibrational structures. Furthermore, tailoring their phonon mean free path by nano-level size control markedly elevates thermal conductivity. We anticipate that these insights open up new opportunities for rationally designing MOF-based thermal functional materials.

Indexed as

2D MOFsdeep neural‐network potentialmultiscale molecular simulationsthermal conductivitythermal transport

Identifiers

PMID42364648
PMCPMC13495841

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.