General Synthesis of Transition‐Metal‐Based Carbon‐Group Intermetallic Catalysts for Efficient Electrocatalytic Hydrogen Evolution in Wide pH Range
Bibliographic record
Abstract
Abstract Alloying is a well‐accepted strategy for modulating the electronic structures of catalyst materials. Compared to disordered solid‐solution alloys, intermetallic compounds feature ordered atomic arrangements and provide a unique platform with a rich and diverse resource to study the relationships among chemical composition, atomic structure, electronic structure, and properties. Unfortunately, it is still challenging to synthesize the nanostructures of intermetallic compounds for catalysis research. In this study, a series of intermetallic silicides (PtSi, RhSi, Ru2Si3, IrSi), germanide (Ru2Ge3), and stannides (Ru3Sn7, IrSn2, PdSn3, PdSn2) are rationally designed and constructed through a facile molten‐salt‐assisted route. As an example, the PtSi not only shows highly desirable electrocatalytic properties for the hydrogen evolution reaction (HER) with low overpotentials of 22, 38, and 66 mV at a current density of 10 mA cm‐2 in acidic, alkaline, and neutral media, respectively, but exhibits superior durability, as well as >97% faradic efficiency. The theoretical calculations suggest that the introduction of Si to Pt could weaken the binding energy between Pt and H atoms, which further facilitates the hydrogen generation during the HER process. Further, the findings inspired the authors to develop other kinds of metal‐based carbon‐group intermetallic phases with excellent activity in the HER and beyond.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".