Evidence map›Paper›PMID 37396054›Full record

ArticleACS applied electronic materials2023

Toward Fabrication of Devices Based on Graphene/Oxide Multilayers.

Yuxuan Wang, Anais Guerenneur, Sami Ramadan, Jingle Huang, Sarah Fearn, Nomaan Nabi, Norbert Klein, Neil McN Alford, Peter K Petrov

Abstract read
In one paragraph

Article in ACS applied electronic materials, 2023. 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

9 authors.

Yuxuan WangImperial College London, London SW7 2AZ, U.K.
Anais GuerenneurImperial College London, London SW7 2AZ, U.K.
Sami RamadanImperial College London, London SW7 2AZ, U.K.
Jingle HuangUniversity College London, Gower Street, London WC1E 6BT, U.K.ORCID https://orcid.org/0000-0001-7187-0757
Sarah FearnImperial College London, London SW7 2AZ, U.K.
Nomaan NabiImperial College London, London SW7 2AZ, U.K.
Norbert KleinImperial College London, London SW7 2AZ, U.K.
Neil McN AlfordImperial College London, London SW7 2AZ, U.K.
Peter K PetrovImperial College London, London SW7 2AZ, U.K.ORCID https://orcid.org/0000-0003-3643-6685

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to its high electrical conductivity, low density, and flexibility, graphene has great potential for use as a building block in a wide range of applications from nanoelectronics to biosensing and high-frequency devices. For many device applications, it is required to deposit dielectric materials on graphene at high temperatures and in ambient oxygen. This has been proven to be highly challenging because these conditions cause significant degradation in graphene. In this work, we investigate the degradation of graphene at elevated temperatures in an oxygen atmosphere and possible protection mechanisms to enable the growth of oxide thin films on graphene at higher temperatures. We show that coating graphene with self-assembled monolayers of hexamethyldisilazane (HMDS) prior to a high-temperature deposition can significantly reduce the damage induced. Furthermore, a graphene sample treated with HMDS displayed a weaker doping effect due to weak interaction with oxygen species than bare graphene, and a much slower rate of electrical resistance degradation was exhibited during annealing. Thus, it is a promising approach that could enable the deposition of metal oxide materials on graphene at high temperatures without significant degradation in graphene quality, which is critical for a wide range of applications.

Identifiers

PMID37396054
PMCPMC10308813

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LicenceCC BY
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Registered trials

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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.