Controlled Morphology Synthesis of Nanostructured β-AlF<sub>3–<i>x</i></sub>(OH)<i><sub>x</sub></i> with Tunable Specific Surface Area
Bibliographic record
Abstract
In this work, the synthesis of β-AlF 3– x (OH) x nanoparticles with very high specific surface area (SSA) using a microwave-assisted solvothermal process is reported. The influence of synthesis parameters on the morphology and SSA was investigated, and the nature of the solvent is shown to have the greatest impact. Five samples prepared using different solvent mixtures were deeply characterized by thermogravimetric analysis (TGA), N 2 sorption, powder X-ray diffraction, transmission electron microscopy (TEM), and 19 F and 27 Al high-field solid-state NMR. Their SSAs range from 25 to 345 m 2 ·g –1 with an associated OH content slightly increasing from ≈16% (AlF 2.52 (OH) 0.48 ) to ≈19% (AlF 2.42 (OH) 0.58 ), as estimated by TGA and 27 Al high-field solid-state NMR. Compared to previous reference work [ Dambournet, D., Chem. Mater. 2008, 20 4 1459−1469], β-AlF 3– x (OH) x nanoparticles with SSAs up to 4 times larger were obtained. TEM revealed the formation of hollow nanostructures except when the surface exceeds 300 m 2 ·g –1, in which case isolated nanoparticles are observed. The sample with the highest SSA also displaying an appealing cumulative pore volume of 0.060 cm 3 ·g –1, its hydrogen adsorption capability was evaluated to show that β-AlF 3– x (OH) x nanoparticles have a potential interest for hydrogen storage 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) | 0.000 | 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".