Solvent-Free Mechanochemical Synthesis of Ultrasmall Nickel Phosphide Nanoparticles and Their Application as a Catalyst for the Hydrogen Evolution Reaction (HER)
Bibliographic record
Abstract
The photocatalytic (PC) splitting of water into oxygen and hydrogen has attracted considerable attention in the past decade. Despite the promise of hydrogen gas as a new energy resource, the environmentally friendly design of viable catalysts with good morphological and size control remains a continual challenge. Of the many classes of available catalysts, metal phosphides are a low-cost and potentially accessible option as catalysts for this reaction, in comparison to the traditionally used precious-metal catalysts. However, the synthesis of metal phosphide nanomaterials currently involves the use of highly reactive phosphorus sources at high temperatures in organic solvents. Herein, we demonstrate the application of sodium phosphide as an excellent solid-state phosphorus source for the synthesis of nickel phosphide nanoparticles below 3 nm in diameter, a size range previously unheard of for the mechanochemical synthesis of metal phosphides. These nanoparticles in turn showed success for the hydrogen evolution reaction, using graphitic carbon nitride as a photocatalytic support, generating 233.9 μmol g–1 h–1 of hydrogen, using broad spectrum light at room temperature after only 3 h, and being readily recyclable and reusable, without any decrease in reactivity. This mechanochemical method shows a mass intensity (MI) value over 2.5 times lower than those of traditionally used solution-based methods, even after workup and washing of the product. Upon scaling up the reaction to a 2.5 g scale, we were able to further improve the MI to 3 times lower than those of traditional solvent-based methods, while the energy demand was reduced over 18-fold.
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 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 | 0.001 |
| 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) | 0.000 | 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".