A General Chelate-Assisted Co-Assembly to Metallic Nanoparticles-Incorporated Ordered Mesoporous Carbon Catalysts for Fischer–Tropsch Synthesis
Bibliographic record
Abstract
The organization of different nano objects with tunable sizes, morphologies, and functions into integrated nanostructures is critical to the development of novel nanosystems that display high performances in sensing, catalysis, and so on. Herein, using acetylacetone as a chelating agent, phenolic resol as a carbon source, metal nitrates as metal sources, and amphiphilic copolymers as a template, we demonstrate a chelate-assisted multicomponent coassembly method to synthesize ordered mesoporous carbon with uniform metal-containing nanoparticles. The obtained nanocomposites have a 2-D hexagonally arranged pore structure, uniform pore size (~4.0 nm), high surface area (~500 m(2)/g), moderate pore volume (~0.30 cm(3)/g), uniform and highly dispersed Fe(2)O(3) nanoparticles, and constant Fe(2)O(3) contents around 10 wt %. By adjusting acetylacetone amount, the size of Fe(2)O(3) nanoparticles is readily tunable from 8.3 to 22.1 nm. More importantly, it is found that the metal-containing nanoparticles are partially embedded in the carbon framework with the remaining part exposed in the mesopore channels. This unique semiexposure structure not only provides an excellent confinement effect and exposed surface for catalysis but also helps to tightly trap the nanoparticles and prevent aggregating during catalysis. Fischer-Tropsch synthesis results show that as the size of iron nanoparticles decreases, the mesoporous Fe-carbon nanocomposites exhibit significantly improved catalytic performances with C(5+) selectivity up to 68%, much better than any reported promoter-free Fe-based catalysts due to the unique semiexposure morphology of metal-containing nanoparticles confined in the mesoporous carbon matrix.
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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) | 0.001 | 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 | 0.001 |
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".