Stabilization, Characterization, and Electrochemical Applications of High‐Entropy Oxides: Critical Assessment of Crystal Phase–Properties Relationship
Bibliographic record
Abstract
Abstract High‐entropy oxides (HEOs), a class of newly emerging energy conversion and storage technology materials, have gained significant interest due to their unique structure, complex stoichiometry, and corresponding synergetic effect. Despite the increasing number of reported studies related to HEOs in recent years, details of their structural properties and electrochemical activities are still lacking. Herein, the exciting developments of HEOs regarding their design, synthesis, characterization, theoretical calculations, and electrochemical performances are outlined. The fundamentals of HEOs, including their strict definition, main features, and four‐core aspect effects are presented. The different synthetic methods of HEOs are categorized to highlight the significance of parameter optimization to ensure the single‐phase stability of HEOs. The advances in characterization techniques on the local lattice and atomic distribution and the basic principles of combinatorial screening methods based on computational techniques are also elaborated. Recent HEO‐based electrode/electrocatalysts toward Li‐ion batteries and oxygen catalysis are reviewed to assess the potential applications of HEOs. This review draws attention to the critical challenges of HEOs that are worth more extensive explorations 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".