Electrochemical interfacial catalysis in Co-based battery electrodes involving spin-polarized electron transfer
Bibliographic record
Abstract
Interfacial catalysis occurs ubiquitously in electrochemical systems, such as batteries, fuel cells, and photocatalytic devices. Frequently, in such a system, the electrode material evolves dynamically at different operating voltages, and this electrochemically driven transformation usually dictates the catalytic reactivity of the material and ultimately the electrochemical performance of the device. Despite the importance of the process, comprehension of the underlying structural and compositional evolutions of the electrode material with direct visualization and quantification is still a significant challenge. In this work, we demonstrate a protocol for studying the dynamic evolution of the electrode material under electrochemical processes by integrating microscopic and spectroscopic analyses, operando magnetometry techniques, and density functional theory calculations. The presented methodology provides a real-time picture of the chemical, physical, and electronic structures of the material and its link to the electrochemical performance. Using Co(OH) 2 as a prototype battery electrode and by monitoring the Co metal center under different applied voltages, we show that before a well-known catalytic reaction proceeds, an interfacial storage process occurs at the metallic Co nanoparticles/LiOH interface due to injection of spin-polarized electrons. Subsequently, the metallic Co nanoparticles act as catalytic activation centers and promote LiOH decomposition by transferring these interfacially residing electrons. Most intriguingly, at the LiOH decomposition potential, electronic structure of the metallic Co nanoparticles involving spin-polarized electrons transfer has been shown to exhibit a dynamic variation. This work illustrates a viable approach to access key information inside interfacial catalytic processes and provides useful insights in controlling complex interfaces for wide-ranging electrochemical systems.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".