Solution–Liquid–Solid Growth and Catalytic Applications of Silica Nanorod Arrays
Bibliographic record
Abstract
Abstract As an analogue to the vapor–liquid–solid process, the solution–liquid–solid (SLS) method offers a mild solution‐phase route to colloidal 1D nanostructures with controlled sizes, compositions, and properties. However, direct growth of 1D nanostructure arrays through SLS processes remains in its infancy. Herein, this study shows that SLS processes are also suitable for the growth of nanorod arrays on the substrate. As a proof of concept, seedless growth of silica nanorod arrays on a variety of hydrophilic substrates such as pristine and oxide‐modified glass, metal sheets, Si wafers, and biaxially oriented polypropylene film are demonstrated. Also, the silica nanorod arrays can be used as a new platform for the fabrication of catalysts for photothermal CO2 hydrogenation and the reduction of 4‐nitrophenol reactions. This work offers some fundamental insight into the SLS growth process and opens a new avenue for the mild preparation of functional 1D nanostructure arrays for various applications.
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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".