Electrocatalytic Hydrolysis‐Modulated Multistate Resistive Switching Behaviors in Memristors
Bibliographic record
Abstract
Current rapid development of big data, Internet of Things, and artificial intelligence require exponentially higher data storage capacity. The memristor technology, which stores data by controlling resistance states, demonstrates great prospects in resistive random‐access memory (RRAM), synapse construction, and neuromorphic computing. However, traditional memristor devices can only store 1‐bit of data by tuning two separate resistance states, which limits their storage density. Herein, a water‐coupled Ag/TiO2_few‐layer graphene_TiO2/Al memristor is developed as a multibit data storage system. The high and low resistance state ratio (HRS/LRS) increases from 5 to 44 when water is coupled in the device. An electrocatalytic hydrolysis‐modulated resistive switching mechanism is proposed for the physical phenomenon. Herein, not only a multilevel per cell (MLC) storage device is developed, but also a novel electrocatalysis coupling mechanism for memristor technology is provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".