One-Pot Procedure to Synthesize High Surface Area Alumina Nanofibers Using Supercritical Carbon Dioxide
Bibliographic record
Abstract
For the first time, high surface area nanofibers were synthesized using aluminum isopropoxide monomer with acetic acid as the polycondensation agent in the green solvent, supercritical carbon dioxide (scCO(2)). It was found that the synthesis temperature, pressure, concentration, and acid/alkoxide ratio had a large effect on fiber formation. By optimizing the experimental conditions at 80 degrees C and 6000 psi of scCO(2) using aluminum isopropoxide at a concentration of 0.3 mmol/mL and acid/alkoxide ratio of 10, alumina nanofibers were formed ranging from 11 to 22 nm in diameter and 500 to 1000 nm in length, and with surface areas up to 580 m(2)/g. Lower temperatures gave irregular shaped nanoparticles, while a lower acid/alkoxide ratio (5:1) resulted in the formation of low surface area alumina bars. Increasing pressure led to better separation of the nanofibers and higher surface areas. In addition to the synthesis conditions, the influence of calcination temperature on the structural, textural, and morphological properties of the materials was examined using various physicochemical techniques including electron microscopy, TGA/DTA, powder XRD, FTIR, XPS, and nitrogen adsorption/desorption analysis. The long fibers with high aspect ratios were found to be thermally stable even after calcining at up to 1050 degrees C. The mechanism of fiber formation in scCO(2) is proposed based on a [Al(OH)(CH(3)CO(2))(2)](n) polycondensate backbone.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".