Recent Progress of Solid–Liquid Interface-Mediated Contact-Electro-Catalysis
Bibliographic record
Abstract
Contact electrification (CE) is a common physical process by which triboelectric charges are generated through the mutual contact between two objects. Despite the ongoing debates on CE’s mechanism, recent advancements in technology have elucidated the primary role of electron transfer in most CE processes. This discovery leads to the spawning of an emerging field, known as contact-electro-catalysis (CEC), which utilizes the electron transfer phenomenon during CE to initiate CEC. In this work, we provide the first comprehensive review of the recent progress of the solid–liquid interface-mediated CEC process, including its working principles, relationship with surface science, recent breakthroughs in applications, and future challenges. We aim to provide fundamental guidance for researchers to understand the reaction mechanism of the CEC process and to propose potential pathways to enhance CEC efficiency from a surface and interfacial science perspective. Later, recent application scenarios using the novel CEC techniques are summarized, including wastewater treatment, efficient generation of hydrogen peroxide (H 2 O 2 ), lithium-ion battery recycling, and CO 2 reduction. In general, CEC technology has opened a new avenue for catalysis, effectively expanding the range of catalyst options and holding promise as a solution to a variety of complex catalytic challenges in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".