Evidence map›Paper›PMID 42082462›Full record

ArticleNature communications2026

Polymer-free van der Waals assembly of 2D material heterostructures using muscovite crystals.

Ian Babich, Timofey M Savilov, Natalia A Mamchik, Kristina Vaklinova, Nansi Zhou, Denis S Baranov, Dmitrii A Litvinov, Virgil Gavriliuc, Yue Yuan, Amoz Chua and 7 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

17 authors.

Ian BabichInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-0428-7259
Timofey M SavilovInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0001-7837-5441
Natalia A MamchikInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.
Kristina VaklinovaInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.
Nansi ZhouInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.
Denis S BaranovInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-1643-9854
Dmitrii A LitvinovInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.
Virgil GavriliucInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0002-4436-1372
Yue YuanInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0002-7249-0411
Amoz ChuaDepartment of Materials Science and Engineering, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0009-0000-3511-7899
Kenji WatanabeAdvanced Materials Laboratory, National Institute for Materials Science, Tsukuba, Japan.ORCID http://orcid.org/0000-0003-3701-8119
Takashi TaniguchiAdvanced Materials Laboratory, National Institute for Materials Science, Tsukuba, Japan.ORCID http://orcid.org/0000-0002-1467-3105
Mario LanzaInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-4756-8632
Maciej KoperskiInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0002-8301-914X
Kostya S NovoselovInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-4972-5371
Alexey I BerdyuginInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore. alexey@nus.edu.sg.ORCID http://orcid.org/0000-0002-7537-6227
Makars ŠiškinsInstitute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore. m.siskins@soton.ac.uk.ORCID http://orcid.org/0000-0003-4295-2221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of van der Waals (vdW) heterostructures has enabled formation of bespoke materials with atomic precision, where numerous quantum and topological phenomena have already been discovered. This atomic-layer tunability, however, comes at a cost: individual 2D layers must be picked up, moved, and placed in a deterministic manner while keeping their interfaces atomically clean. Recent advances in machine learning and robotics place even stronger emphasis on the deterministic aspect of vdW assembly. Current polymer-based transfer methods satisfy neither the determinism nor cleanliness requirements. To this end, solutions are needed where adhesion can be dynamically and deterministically controlled without leaving organic contamination. Here, we present a polymer-free transfer technique employing thin muscovite (mica) crystals. Temperature control over mica adhesion enables deterministic pick-up, stacking, and release of 2D materials, while their crystalline, inorganic nature ensures pristine interfaces and suppresses strain. Fully compatible with existing fabrication workflows, this approach enables the assembly of demanding vdW heterostructures, including those with exposed conductive layers, moiré superlattices and suspended membranes. Our method represents a promising strategy for vdW heterostructure fabrication toward its automatisation.

Identifiers

PMID42082462
PMCPMC13346611

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.