Evidence map›Paper›PMID 41195661›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2025

Decoupling Junction and Nanosheet Transport in Graphene Networks via Simple DC Temperature-Dependent Measurements.

Emmet Coleman, Luke Doolan, Anthony Dawson, Eoin Caffrey, Cian Gabbett, Weimiao Wang, Kevin Synnatschke, Jagdish K Vij, Jonathan N Coleman

Abstract read
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2025. 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. Article
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.

Emmet ColemanSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-9312-5472
Luke DoolanSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.
Anthony DawsonSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.
Eoin CaffreySchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-0174-383X
Cian GabbettSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0003-0957-7714
Weimiao WangSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.
Kevin SynnatschkeCenter for Advancing Electronics Dresden (CfAED) and Faculty of Chemistry and Food Chemistry, Technische Universität Dresden, 01062, Dresden, Germany.
Jagdish K VijDepartment of Electronic & Electrical Engineering, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0001-7918-126X
Jonathan N ColemanSchool of Physics, CRANN & AMBER Research Centers, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0001-9659-9721

Funding

HORIZON EUROPE Digital, Industry and Space 2D-PRINTABLEScience Foundation Ireland SFI/12/RC/2278_P2
6 · The paper itself

Abstract

Printed nanosheet networks are important for various applications in electronics, sensing, and energy storage. Understanding charge transport in such systems requires separately assessing the contributions of both the nanosheets themselves as well as the inter-nanosheet junctions. However, achieving this using standard electrical characterization methods can be challenging. Here, a broadly applicable method is presented for the separation and extraction of the temperature-dependent junction resistance (R

Indexed as

conductivitiesinter‐particlenanosheetssolution‐processed

Identifiers

PMID41195661
PMCPMC12710209

What OpenQuestion holds

Textmetadata
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.