Spontaneous Emulsification of Organometallic Complexes Applied to the Synthesis of Nanocapsules Active for H<sub>2</sub> Release from Ammonia-Borane
Bibliographic record
Abstract
Herein, we achieved spontaneous emulsification of organometallic precursors to elaborate subμm metal nanocapsules after interfacial reduction. Depending on the proportion of the three components, water, solvent, and the metal precursor, either thermodynamically stable “surfactant-free microemulsions” (SFME) or metastable Ouzo emulsions are formed. We investigated the catalytic transition metals Au, Pd, and Pt, individually or combined, and stabilized by various ligands. Upon reduction of the precursors, either shells of discrete nanoparticles (NPs) or continuous shells were obtained, for the SFME and Ouzo emulsions, respectively. The Au/Pd mixed emulsions lead to a unique structural morphology, in which the Au–Pd nanoparticles are embedded in a continuous submicronic metal shell. The AuNPs are available to grow larger particles within the NP shell using a seeded growth approach. The water-stable and surfactant-free nanocapsules are appealing as catalysts, and, as such, were evaluated for the hydrolysis of ammonia-borane as a promising catalytic strategy for H 2 release from an H-high-content storage material. This work establishes for the first time a genuine activity of water-compatible gold colloids for this reaction.
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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) | 0.000 | 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 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".