In-situ fabrication of metal oxide nanocaps based on biphasic reactions with surface nanodroplets
Bibliographic record
Abstract
Surface-bound nanomaterials are widely used in clean energy techniques from lithium batteries, solar-driven evaporation in desalination to hydrogen production by photocatalytic electrolysis. Reactive surface nanodroplets may potentially streamline the process of fabrication of a range of surface-bound nanomaterials invoking biphasic reactions at interfaces. In this work, we demonstrate the feasibility of reactive surface nanodroplets for in-situ synthesis and anchoring of nanocaps of metal oxides with tailored porous structures. Spatial arrangement and surface coverage of nanocaps are predetermined during the formation of reactive nanodroplets, while the crystalline structures of metal oxides can be controlled by thermal treatment of organometallic nanodroplets produced from the biphasic reactions. Notably, tuning the ratio of reactive and non-reactive components in surface nanodroplets enables the formation of porous nanocaps that can double photocatalytic efficiency in the degradation of organic contaminants in water, compared to smooth nanocaps. In total, we demonstrate in-situ fabrication of four types of metal oxides in the shape of nanocaps. Our work shows that reactive surface nanodroplets may open a door to a general, fast and tuneable route for preparing surface-bound metal oxides. This fabrication approach may help develop new nanomaterials needed for photocatalytic reactions, wastewater treatment, optical focusing, solar energy conversion and other clean energy techniques.
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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".