ArticleNature communications2022
Mesoscopic sliding ferroelectricity enabled photovoltaic random access memory for material-level artificial vision system.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
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Who cites it
19 citing papers in PubMed.
- Sliding Ferroelectric Metals: An Emerging Frontier.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Scalable Perovskite Platform With Multi-State Photoresponsivity for In-Sensor Saliency Detection.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Giant Switchable Remanent Polarization and Photocurrent in Ferroelectric Thin Film Photomemristor for In Situ Training.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Very-large-scale mimetic optogenetic synapses for physical reservoir computing.Nature communications · 2026Article
- In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages.Nature communications · 2025Article
- Tunable-Diameter Nanoscrolls from Janus WSSe/WSeACS nano · 2025Article
- Symmetry-broken MoSNature communications · 2025Article
- Emergence of sliding ferroelectricity in naturally parallel-stacked multilayer ReSeNature communications · 2025Article
- Nanotubes from Transition Metal Dichalcogenides: Recent Progress in the Synthesis, Characterization and Electrooptical Properties.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Versatile on-chip polarization-sensitive detection system for optical communication and artificial vision.Light, science & applications · 2025Article
- In situ training of an in-sensor artificial neural network based on ferroelectric photosensors.Nature communications · 2025Article
- Modulations of the work function and morphology of a single MoSNanoscale advances · 2024Article
- Mechanism of WSACS nano · 2024Article
- An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition.Micromachines · 2024Article
- The gold ticket to achiral WSNature materials · 2024Article
- Non-volatile electrical polarization switching via domain wall release in 3R-MoSNature communications · 2024Article
- Submillimeter-Long WSNano letters · 2023Article
- Insights into the Growth of Ternary WSSe Nanotubes in an Atmospheric CVD Reactor.Inorganic chemistry · 2023Article
- Mesoscopic sliding ferroelectricity enabled photovoltaic random access memory for material-level artificial vision system.Nature communications · 2022Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Intelligent materials with adaptive response to external stimulation lay foundation to integrate functional systems at the material level. Here, with experimental observation and numerical simulation, we report a delicate nano-electro-mechanical-opto-system naturally embedded in individual multiwall tungsten disulfide nanotubes, which generates a distinct form of in-plane van der Waals sliding ferroelectricity from the unique combination of superlubricity and piezoelectricity. The sliding ferroelectricity enables programmable photovoltaic effect using the multiwall tungsten disulfide nanotube as photovoltaic random-access memory. A complete "four-in-one" artificial vision system that synchronously achieves full functions of detecting, processing, memorizing, and powering is integrated into the nanotube devices. Both labeled supervised learning and unlabeled reinforcement learning algorithms are executable in the artificial vision system to achieve self-driven image recognition. This work provides a distinct strategy to create ferroelectricity in van der Waals materials, and demonstrates how intelligent materials can push electronic system integration at the material level.
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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.