ArticleNature communications2020
Proton-enabled activation of peptide materials for biological bimodal memory.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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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.
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Who cites it
17 citing papers in PubMed, 65 citations in OpenAlex.
- Advances in Gel-Based Electrolyte-Gated Flexible Visual Synapses for Neuromorphic Vision Systems.Gels (Basel, Switzerland) · 2026Review
- Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems.Nano-micro letters · 2026Review
- Ultralow-Power Peptide-Based Memristor Enabled by Emulation of Proton-Mediated Synaptic Signaling.Small methods · 2025Article
- Mixed Ionic and Electronic Charge Transport in Conductive Protein Fibers Revealed with DC Electrical Measurements.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Assessing the efficacy of the Pomodoro technique in enhancing anatomy lesson retention during study sessions: a scoping review.BMC medical education · 2025Article
- A density functional theory study of tyrosine-proton mediated transport in Ag-filamentary nanodevices.Smart molecules : open access · 2025Article
- Role of Hydrogen Transfer in Functional Molecular Materials and Devices.Precision chemistry · 2025Review
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Molecular switching by proton-coupled electron transport drives giant negative differential resistance.Nature communications · 2024Article
- A nonconjugated radical polymer enables bimodal memory and in-sensor computing operation.Science advances · 2024Article
- Memfractance of Proteinoids.ACS omega · 2024Article
- Amyloid-Gold Nanoparticle Hybrids for Biocompatible Memristive Devices.Materials (Basel, Switzerland) · 2023Article
- Memristors with Biomaterials for Biorealistic Neuromorphic Applications.Small science · 2022Article
- Circularly polarized light-sensitive, hot electron transistor with chiral plasmonic nanoparticles.Nature communications · 2022Article
- A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing.Nanoscale advances · 2022Article
- Switched Proton Conduction in Metal-Organic Frameworks.JACS Au · 2022Review
- Synaptic transistors based on a tyrosine-rich peptide for neuromorphic computing.RSC advances · 2021Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage. This study describes a peptide-based platform of materials and devices that can control the coupled conduction of protons and electrons and thus create distinct regions of synapse-like performance depending on the proton activity. We utilized tyrosine-rich peptide-based films and generalized our principles by demonstrating both memristor and synaptic devices. Interestingly, even memristive behavior can be controlled by both voltage and humidity inputs, learning and forgetting process in the device can be initiated and terminated by protons alone in peptide films. We believe that this work can help to understand the mechanism of biological memory and lay a foundation to realize a brain-like device based on ions and electrons.
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Registered trials
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.