Synthesis of Nanoporous Network Materials with High Surface Areas from the Cooperative Assemblage of Alkyl-Chain-Capped Metal/Metal Oxide Nanoparticles
Bibliographic record
Abstract
A simple route for the synthesis of a new class of porous metal/metal oxide nanoparticle (NP) materials such as Cu−TiO 2, Au−ZrO 2, Cu−ZrO 2, and Au−TiO 2 NPs through the cooperative assembly of presynthesis hydrophobic oleic acid (OA)-capped metal and metal oxide NPs is reported. In such a way, the synergistic interaction between metal and metal oxide NPs has gained significant interest owing to new properties that arise at the metal−metal oxide interface. Various technique including XRD, N 2 adsorption/desorption isotherms, FTIR, TEM, XPS, and catalytic test were used to monitor the physicochemical and catalytic properties of these materials. The results revealed that these porous materials exhibit homogeneous dispersion in between metal and metal oxide NPs, high surface area, and narrow interparticle pore size distribution. The catalytic properties of these metal/metal oxide NP solids (even Cu oxide NP catalysts, e.g., no noble metal catalyst) in the CO oxidation reaction are better than those of commercial noble metal catalyst (Pt/Al 2 O 3 ) and the conventional metal oxide-supported Cu catalysts. Based on this synthesis approach, a variety of nanoporous multicomponent solids of both metal and metal oxide with desired proportions can be synthesized.
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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".