Evidence map›Paper›PMID 41368847›Full record

ArticleNanoscale2026

Fast prototyping of memristors for ReRAMs and neuromorphic computing.

Gianluca Marraccini, Sebastiano Strangio, Elisabetta Dimaggio, Riccardo Sargeni, Francesco Pieri, Yigit Sozen, Andres Castellanos-Gomez, Gianluca Fiori

Abstract read
In one paragraph

Article in Nanoscale, 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

8 authors.

Gianluca MarracciniDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0009-0009-1446-8433
Sebastiano StrangioDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0000-0002-6984-1137
Elisabetta DimaggioDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0000-0002-7596-8839
Riccardo SargeniDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0000-0002-8320-4732
Francesco PieriDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0000-0003-4456-8581
Yigit Sozen2D Foundry research group, Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Madrid, E-28049, Spain.
Andres Castellanos-Gomez2D Foundry research group, Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Madrid, E-28049, Spain.ORCID http://orcid.org/0000-0002-3384-3405
Gianluca FioriDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G.Caruso 16, Pisa, Italy. gianluca.marraccini@ing.unipi.it.ORCID http://orcid.org/0000-0003-0896-0148

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing demand for energy-efficient computing in artificial intelligence requires novel memory technologies capable of storing and processing information. Memristors stand out in thanks to their ability to store information, mimic synaptic behavior and support in-memory computing architectures while requiring minimal active areas and energy consumptions. Here is presented a scalable and cost-effective approach to fabricate Ag/MoS

Identifiers

PMID41368847
PMCPMC12690548

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.