Evidence map›Paper›PMID 38110352›Full record

ArticleNature communications2023

Hydrophobically gated memristive nanopores for neuromorphic applications.

Gonçalo Paulo, Ke Sun, Giovanni Di Muccio, Alberto Gubbiotti, Blasco Morozzo Della Rocca, Jia Geng, Giovanni Maglia, Mauro Chinappi, Alberto Giacomello

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Interface-Dependent Transport and Resistive Switching in TiONanomaterials (Basel, Switzerland) · 2026
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  20. Brain-inspired computing with fluidic iontronic nanochannels.Proceedings of the National Academy of Sciences of the United States of America · 2024
    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.

Gonçalo PauloDepartment of Mechanics and Aerospace Engineering, Sapienza University of Rome, Rome, 00184, Italy.
Ke SunChemical Biology Department, Groningen Biomolecular Sciences & Biotechnology Institute, Groningen, 9700 CC, The Netherlands.
Giovanni Di MuccioDepartment of Mechanics and Aerospace Engineering, Sapienza University of Rome, Rome, 00184, Italy.
Alberto GubbiottiDepartment of Mechanics and Aerospace Engineering, Sapienza University of Rome, Rome, 00184, Italy.
Blasco Morozzo Della RoccaDepartment of Biology, Tor Vergata University of Rome, Rome, 00133, Italy.ORCID 0000-0001-5491-1131
Jia GengDepartment of Laboratory Medicine, State Key Laboratory of Biotherapy and Cancer Center, Med+X Center for Manufacturing, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, 610041, China.ORCID 0000-0003-4753-1039
Giovanni MagliaChemical Biology Department, Groningen Biomolecular Sciences & Biotechnology Institute, Groningen, 9700 CC, The Netherlands.ORCID 0000-0003-2784-0811
Mauro ChinappiDepartment of Industrial Engineering, Tor Vergata University of Rome, Rome, 00133, Italy.ORCID 0000-0002-4509-1247
Alberto GiacomelloDepartment of Mechanics and Aerospace Engineering, Sapienza University of Rome, Rome, 00184, Italy. alberto.giacomello@uniroma1.it.ORCID 0000-0003-2735-6982

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 803213
6 · The paper itself

Abstract

Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical behaviour of memristors, resistors with memory. State-of-the-art technologies currently employ semiconductor-based neuromorphic approaches, which have already demonstrated their efficacy in machine learning systems. However, these approaches still cannot match performance achieved by biological neurons in terms of energy efficiency and size. In this study, we utilise molecular dynamics simulations, continuum models, and electrophysiological experiments to propose and realise a bioinspired hydrophobically gated memristive nanopore. Our findings indicate that hydrophobic gating enables memory through an electrowetting mechanism, and we establish simple design rules accordingly. Through the engineering of a biological nanopore, we successfully replicate the characteristic hysteresis cycles of a memristor and construct a synaptic device capable of learning and forgetting. This advancement offers a promising pathway for the realization of nanoscale, cost- and energy-effective, and adaptable bioinspired memristors.

Indexed as

NanoporesBrainElectricityElectrophysiological PhenomenaSemiconductors

Identifiers

PMID38110352
PMCPMC10728163

What Socratic 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 Socratic graph.