SURFACTANT-FREE SILVER NANOFLUIDS As LIQUID SYSTEMS WITH NEUROMORPHIC POTENTIAL Article Swipe
Daniil Nikitin
,
Kateryna Biliak
,
Jonathan Lemke
,
Mariia Protsak
,
Pavel Pleskunov
,
Marco Tosca
,
Suren Ali‐Ogly
,
Veronika Červenková
,
Blessing Adejube
,
Lucia Bajtošová
,
Zulfiya Černochová
,
Ján Prokeš
,
Ivo Křivka
,
Hynek Biederman
,
Franz Faupel
,
Alexander Vahl
,
Andrei Choukourov
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.37904/nanocon.2023.4748
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.37904/nanocon.2023.4748
Neuromorphic engineering is a rapidly developing branch of science that aims to implement the unique attributes of biological neural networks in artificial devices.Most neuromorphic devices are based on the resistive switching effect, which involves changing the device's conductivity in response to an external electric field.For instance, percolating nanoparticle (NP) networks produced by gas aggregation cluster sources (GAS) show collective spiking behavior in conductivity reminiscent of brain-like dynamics.Nevertheless, the problem of dynamic spatial reconfiguration in solid-state neuromorphic systems remains unsolved.Herein, novel nanofluids with resistive switching properties are proposed as neuromorphic media.They are produced by depositing silver NPs from GAS into vacuum-compatible liquids (paraffin, silicon oil, and PEG) without the use of surfactants or other chemicals.When the electric field is applied between two electrodes, the migration of NPs toward biased electrode is detected in all liquids.The electrophoretic nature of the NP movement was proved by means of ζ-potential measurements.Such movement led to the self-assembly of NPs in conductive paths connecting the electrodes and, as a result, to resistive switching.The electrical response was strongly dependent on the dielectric constant of the base liquid.The Ag-PEG nanofluid demonstrated the best switching performance reproducible during several tens of current-voltage cycles.The growth of flexible and reconfigurable conductive filaments in nanofluids makes them suitable media for potential realization of 3D neural networks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.37904/nanocon.2023.4748
- https://www.confer.cz/nanocon/2023/download/4748-surfactant-free-silver-nanofluids-as-liquid-systems-with-neuromorphic-potential.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 10
- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4392804805Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.37904/nanocon.2023.4748Digital Object Identifier
- Title
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SURFACTANT-FREE SILVER NANOFLUIDS As LIQUID SYSTEMS WITH NEUROMORPHIC POTENTIALWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
Daniil Nikitin, Kateryna Biliak, Jonathan Lemke, Mariia Protsak, Pavel Pleskunov, Marco Tosca, Suren Ali‐Ogly, Veronika Červenková, Blessing Adejube, Lucia Bajtošová, Zulfiya Černochová, Ján Prokeš, Ivo Křivka, Hynek Biederman, Franz Faupel, Alexander Vahl, Andrei ChoukourovList of authors in order
- Landing page
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https://doi.org/10.37904/nanocon.2023.4748Publisher landing page
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https://www.confer.cz/nanocon/2023/download/4748-surfactant-free-silver-nanofluids-as-liquid-systems-with-neuromorphic-potential.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.confer.cz/nanocon/2023/download/4748-surfactant-free-silver-nanofluids-as-liquid-systems-with-neuromorphic-potential.pdfDirect OA link when available
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Neuromorphic engineering, Nanofluid, Pulmonary surfactant, Computer science, Materials science, Nanotechnology, Physics, Artificial intelligence, Artificial neural network, Thermodynamics, NanoparticleTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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