Field‐assisted electrocatalysts spark sulfur redox kinetics: From fundamentals to applications
Bibliographic record
Abstract
Abstract The chief culprit impeding the commercialization of lithium–sulfur (Li–S) batteries is the parasitic shuttle effect and restricted redox kinetics of lithium polysulfides (LiPSs). To circumvent these key stumbling blocks, incorporating electrocatalysts with rational electronic structure modulation into sulfur cathode plays a decisive role in vitalizing the higher electrocatalytic activity to promote sulfur utilization efficiency. Breaking the stereotype of contemporary electrocatalyst design kept on pretreatment, field‐assisted electrocatalysts offer strategic advantages in dynamically controllable electrochemical reactions that might be thorny to regulate in conventional electrochemical processes. However, the highly interdisciplinary field‐assisted electrochemistry puzzles researchers for a fundamental understanding of the ambiguous correlations among electronic structure, surface adsorption properties, and catalytic performance. In this review, the mechanisms, functionality explorations, and advantages of field‐assisted electrocatalysts including electric, magnetic, light, thermal, and strain fields in Li–S batteries have been summarized. By demonstrating pioneering work for customized geometric configuration, energy band engineering, and optimal microenvironment arrangement in response to decreased activation energy and enriched reactant concentration for accelerated sulfur redox kinetics, cutting‐edge insights into the holistic periscope of charge‐spin‐orbital‐lattice interplay between LiPSs and electrocatalysts are scrutinized, which aspires to advance the comprehensive understanding of the complex electrochemistry of Li–S batteries. Finally, future perspectives are provided to inspire innovations capable of defeating existing restrictions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".