Evidence map›Paper›PMID 36475589›Full record

ArticleACS nano2022

Etch and Print: Graphene-Based Diodes for Silicon Technology.

Alessandro Grillo, Zixing Peng, Aniello Pelella, Antonio Di Bartolomeo, Cinzia Casiraghi

Open access · hybridAbstract read
In one paragraph

Article in ACS nano, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

5 citing papers in PubMed, 27 citations in OpenAlex.

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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

5 authors at 2 institutions in 2 countries.

Alessandro GrilloDepartment of Chemistry, University of Manchester, ManchesterM13 9PL, United Kingdom.ORCID 0000-0002-8909-9865
Zixing PengDepartment of Chemistry, University of Manchester, ManchesterM13 9PL, United Kingdom.
Aniello PelellaPhysics Department "E. R. Caianiello", University of Salerno, via Giovanni Paolo II n. 132, Fisciano84084, Salerno, Italy.ORCID 0000-0002-3831-0210
Antonio Di BartolomeoPhysics Department "E. R. Caianiello", University of Salerno, via Giovanni Paolo II n. 132, Fisciano84084, Salerno, Italy.ORCID 0000-0002-3629-726X
Cinzia CasiraghiDepartment of Chemistry, University of Manchester, ManchesterM13 9PL, United Kingdom.ORCID 0000-0001-7185-0377
University of Manchester · GBUniversity of Salerno · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The graphene-silicon junction is one of the simplest conceivable interfaces in graphene-integrated semiconductor technology that can lead to the development of future generation of electronic and optoelectronic devices. However, graphene's integration is currently expensive and time-consuming and shows several challenges in terms of large-scale device fabrication, effectively preventing the possibility of implementing this technology into industrial processes. Here, we show a simple and cost-effective fabrication technique, based on inkjet printing, for the realization of printed graphene-silicon rectifying devices. The printed graphene-silicon diodes show an ON/OFF ratio higher than 3 orders of magnitude and a significant photovoltaic effect, resulting in a fill factor of ∼40% and a photocurrent efficiency of ∼2%, making the devices suitable for both electronic and optoelectronic applications. Finally, we demonstrate large-area pixeled photodetectors and compatibility with back-end-of-line fabrication processes.

Indexed as

back-end-of-line processgraphene−silicon junctionsinkjet printingphotodiodesSchottky diodes

Identifiers

PMID36475589
PMCPMC9878974
OpenAlexW4311808524

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.