Metallic Glass Nanoparticles Synthesized via Flash Joule Heating
Bibliographic record
Abstract
Metallic glass (MG) nanoparticles have attracted intensive research interest for their promising mechanical and catalytic applications. However, current production methods lack the ability to precisely control phase, composition, and morphology, making it challenging to explicitly study their structure-property relationship. Here, we report a direct one-step synthesis of MG nanoparticles using flash Joule heating (FJH) that allows us to produce nanoparticles with desired phase, composition, and morphology. With the fast and controllable cooling attainable through FJH, we can produce fully amorphous Pd-P, Pd-Ni-P, and Pd-Cu-P nanoparticles with precise control in alloy composition and particle size (2.33 nm ± 0.83 nm). As a demonstration of potential application, we show the improved oxygen evolution activity (∼300 mV lower onset potential) of the MG nanoparticles over their crystalline counterparts and long-term stability in 60-h testing.
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 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".