Evidence map›Paper›PMID 40021616›Full record

ArticleMicrosystems & nanoengineering2025

Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics.

Muhammad Umair Khan, Bilal Hassan, Anas Alazzam, Shimaa Eissa, Baker Mohammad

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Muhammad Umair KhanCenter for Cyber-Physical Systems - System on Chip Lab, Khalifa University, Abu Dhabi, 127788, UAE. muhammad.khan@ku.ac.ae.ORCID http://orcid.org/0000-0003-2702-0717
Bilal HassanDepartment of Electrical Engineering, Khalifa University, Abu Dhabi, 127788, UAE.
Anas AlazzamCenter for Cyber-Physical Systems - System on Chip Lab, Khalifa University, Abu Dhabi, 127788, UAE.ORCID http://orcid.org/0000-0002-8518-8027
Shimaa EissaDepartment of Chemistry, Khalifa University, Abu Dhabi, 127788, UAE.ORCID http://orcid.org/0000-0002-5558-0060
Baker MohammadCenter for Cyber-Physical Systems - System on Chip Lab, Khalifa University, Abu Dhabi, 127788, UAE. baker.mohammad@ku.ac.ae.ORCID http://orcid.org/0000-0002-6063-473X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG's performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na

Identifiers

PMID40021616
PMCPMC11871289

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.