Ligand‐Exchange‐Induced Amorphization of Pd Nanomaterials for Highly Efficient Electrocatalytic Hydrogen Evolution Reaction
Bibliographic record
Abstract
Various kinds of amorphous materials, such as transition metal dichalcogenides, metal oxides, and metal phosphates, have demonstrated superior electrocatalytic performance compared with their crystalline counterparts. Compared to other materials for electrocatalysis, noble metals exhibit intrinsically high activity and excellent durability. However, it is still very challenging to prepare amorphous noble-metal nanomaterials due to the strong interatomic metallic bonding. Herein, the discovery of a unique thiol molecule is reported, namely bismuthiol I, which can induce the transformation of Pd nanomaterials from face-centered-cubic (fcc) phase into amorphous phase without destroying their integrity. This ligand-induced amorphization is realized by post-synthetic ligand exchange under ambient conditions, and is applicable to fcc Pd nanomaterials with different capping ligands. Importantly, the obtained amorphous Pd nanoparticles exhibit remarkably enhanced activity and excellent stability toward electrocatalytic hydrogen evolution in acidic solution. This work provides a facile and effective method for preparing amorphous Pd nanomaterials, and demonstrates their promising electrocatalytic application.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".