Solid-Solutions as Supports and Robust Photocatalysts and Electrocatalysts: A Review
Bibliographic record
Abstract
Abstract Some solid solutions have been strongly utilized over the years as good materials for the synthesis of electrocatalysts and photoctalysts. Sometimes, they are used as supports in order to improve electrocatalytic and photocatalytic properties. We show various achievements of solid solutions as good electrocatalysts, and also, good electrocatalysts support materials in oxygen reduction reaction (ORR), hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Also, we demonstrate various works utilizing solid solutions as good photocatalysts, and good photocatalysts support materials in overall water splitting and carbon dioxide reduction. In all these reports, solid solutions proved to posses the necessary properties needed of any material as electrocatalysts and photocatalysts. In many cases, their use as catalysts supports recorded great improvements. X-ray photoelectron spectroscopy (XPS) was largely used to confirm the chemical environment of the results obtained, together with X-ray diffraction (XRD). In the electrochemical methods, cyclic voltammograms (CVA), chronoamperometry and rotating disk electrode (RDE), were also carried out. Linear sweep voltametry (LSV) curve was carried out in some cases to measure the current at a working electrode, and tables were shown for clear explanation. In addition, a photoluminescence spectrum (PL) was used to probe the electronic structure of the various solid solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".