Galvanic replacement reaction to prepare catalytic materials
Bibliographic record
Abstract
Abstract Galvanic replacement reaction (GRR) has gained considerable interest as a facile and versatile synthetic method for modulating compositions, morphologies, and corresponding physicochemical properties of metallic nanoparticles. Thus far, extensive knowledge of GRR on monometallic templates has been accumulated, backed with ample experimental data and computational modeling and validation. The GRR templates have recently been extended to other materials such as alloys, oxides, sulfides, and liquid metals. These new materials have demonstrated potential applications in electrochemical energy conversion systems, which have been relatively unexplored for GRR‐originated materials. In this review, the recent findings in GRR on these new template materials are introduced, pointing to the incredible versatility of the GRR methodology in diversifying the catalytic materials classes. We further discuss the remaining critical issues and future research directions of GRRs to fully exploit the potential of GRR in spearheading future advances in electrocatalytic energy conversion and other important applications.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".