Synthesis of Chiral Nematic Mesoporous Metal and Metal Oxide Nanocomposites and their Use as Heterogeneous Catalysts
Bibliographic record
Abstract
We report the synthesis of chiral nematic mesoporous metal and metal oxide nanocomposites containing zinc or copper prepared through a vacuum‐assisted loading of the metal precursor into a chiral nematic mesoporous silica template followed by high‐temperature treatment. Significant metal loading into the template was achieved after a single loading cycle (up to 55 % of the pore volume) as confirmed by nitrogen sorption measurements. Growth of the metal nanoparticles was contained within the porous silica template, and subsequent removal of the template afforded free‐standing chiral nematic metal oxide films with surface areas up to 200 m2/g. To demonstrate the utility of these high surface area materials, we used the prepared CuO material as a heterogeneous catalyst for click reactions. Our prepared CuO showed excellent activity (95 % conversion after seven hours) when compared to commercially available CuO nanoparticles (20 % conversion after 24 hours) owing to its considerably higher surface area. Moreover, the prepared CuO material maintained high activity (>70 % conversion) after four cycles.
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) | 0.000 | 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".