Electric Potential Controlled Ionic Lubrication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric potential controlled lubrication, also known as triboelectrochemistry or electrotunable tribology, is an emerging field to regulate the friction, wear, and lubrication performance under charge distribution on the solid–liquid interfaces through an applied electric potential, allowing to achieve superlubrication. Electric potential controlled lubrication is of great significance for smart tunable lubrication, micro-electro-mechanical systems (MEMS), and key components in high-end mechanical equipment such as gears and bearings, etc. However, there needs to be a more theoretical understanding of the electric potential controlled lubrication between micro- and macro-scale conditions. For example, the synergistic contribution of the adsorption/desorption process and the electrochemical reaction process has not been well understood, and there exists a significant gap between the theoretical research and applications of electric potential controlled lubrication. Here, we provide an overview of this emerging field, from introducing its theoretical background to the advantages and characteristics of different experimental configurations (including universal mechanical tribometers, atomic force microscopes, and surface force apparatus/balances) for electric potential controlled lubrication. Next, we review the main experimental achievements in the performance and mechanisms of electrotunable lubrication, especially using ionic lubricants, including electrolyte solutions, ionic liquids, and surfactants. This review aims to survey the literature on electric potential controlled lubrication and provide insights into the design of superlubricants and intelligent lubrication systems for various applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 0.001 |
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 it