ReviewMicromachines2020
Memristive Non-Volatile Memory Based on Graphene Materials.
Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing.Nano-micro letters · 2026Review
- Subthreshold Current Suppression in ReSACS applied nano materials · 2023Article
- Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications.Nanomaterials (Basel, Switzerland) · 2023Review
- Multiple Resistive Switching Mechanisms in Graphene Oxide-Based Resistive Memory Devices.Nanomaterials (Basel, Switzerland) · 2022Article
- Tunable Switching Behavior of GO-Based Memristors Using Thermal Reduction.Nanomaterials (Basel, Switzerland) · 2022Article
- Ternary Arithmetic Logic Unit Design Utilizing Carbon Nanotube Field Effect Transistor (CNTFET) and Resistive Random Access Memory (RRAM).Micromachines · 2021Article
- Analysis of Leakage Current of HfOMicromachines · 2021Article
- Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application.Nanomaterials (Basel, Switzerland) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young's modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 10
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What OpenQuestion holds
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.