Colloidal High Entropy Alloy Nanoparticles: Synthetic Strategies and Electrocatalytic Properties
Bibliographic record
Abstract
High entropy alloy (HEA) nanoparticles (NPs) have attracted much attention recently due to their unprecedented chemical properties. As such, HEA NPs have been used as materials with superior activity toward electrocatalytic applications. Specifically, solid solutions that form randomly mixed single-phased structures have received the most focus in the early stages of HEA NP development for their entropic-driven design and multifunctionality. Advances to non-colloidal and colloidal synthetic methods have allowed for the fabrication of solid solution HEA NPs with varying compositions and complexity to be applied to many practical applications such as fuel cells, energy storage and agriculture. In this review, the current colloidal methods and catalytic mechanisms for solid solution HEA NP synthesis are investigated from the physical chemistry perspective. A comprehensive discussion on the theory, techniques, and electrocatalytic applications of colloidal syntheses for successful solid solution HEA NP formation is presented. Finally, promising perspectives for the continued development of physical insights into structure-property relationships towards improved HEA NP synthesis and application are discussed.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".