Pseudocapacitance‐Induced Synaptic Plasticity of Tribo‐Phototronic Effect Between Ionic Liquid and 2D MoS<sub>2</sub>
Bibliographic record
Abstract
Abstract Contact‐induced electrification, commonly referred to as triboelectrification, is the subject of extensive investigation at liquid–solid interfaces due to its wide range of applications in electrochemistry, energy harvesting, and sensors. This study examines the triboelectric between an ionic liquid and 2D MoS2 under light illumination. Notably, when a liquid droplet slides across the MoS2 surface, an increase in the generated current and voltage is observed in the forward direction, while a decrease is observed in the reverse direction. This suggests a memory‐like tribo‐phototronic effect between ionic liquid and 2D MoS2. The underlying mechanism behind this tribo‐phototronic synaptic plasticity is proposed to be ion adsorption/desorption processes resulting from pseudocapacitive deionization/ionization at the liquid–MoS2 interface. This explanation is supported by the equivalent electrical circuit modeling, contact angle measurements, and electronic band diagrams. Furthermore, the influence of various factors such as velocity, step size, light wavelength and intensity, ion concentration, and bias voltage is thoroughly investigated. The artificial synaptic plasticity arising from this phenomenon exhibits significant synaptic features, including potentiation/inhibition, paired‐pulse facilitation/depression, and short‐term memory (STM) to long‐term memory (LTM) transition. This research opens up promising avenues for the development of synaptic memory systems and intelligent sensing applications based on liquid–solid interfaces.
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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) | 0.002 | 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".