Direct and Easily Prepared Nanocomposite Impurity-Free Hydroxyapatite from Locally Delivered CKD as a Potent Catalyst for trans-2-Butene Production
Bibliographic record
Abstract
Abstract This project is considered the first successful attempt to directly prepare highly pure nanocomposite hydroxyapatite (HAPT) from cement kiln dust. It is prepared by applying a cost-effective preparation method. This is confirmed by XRD, FT-IR and EDX analyses. The crystallite size of impurity-free hydroxyapatite sample prepared at 500°C was equal to 23.6 nm and showed a hierarchical mesoporous unit, in the nano-scale, with Ca-deficient structure according to SEM-EDX analyses. HAPT exhibits a high population of different acidic sites, i.e. weak, moderate and strong acidic sites, as determined by TG and DSC-TPD experiments using tetrahydrofuran (THF) as a probe molecule. This wide range of acidic sites over HAPT is clearly and positively enhanced its catalytic activity during the conversion of sec-butanol to trans-2-butene. The sec-butanol was converted by 40% into trans-2-butene at 200°C, and gradually increased up to 91.4% conversion at 300°C with % selectivity < 99%. Moreover, our prepared HAPT showed a higher catalytic activity using air as a carrier, during the conversion of sec-butanol, instead of N2-gas as a carrier. A comparison between the catalytic activity of HAPT prepared from the waste CKD and another sample prepared using pure Ca(NO3)2, both calcined at 500°C for 3 h in oxygen, was done during the conversion of sec-butanol in the temperature range of 200-300°C. HAPT from CKD gave unrivaled results compared to the other sample prepared from calcium nitrate. We ascribed this distinguish behavior to both the acidic sites distribution over each sample and their crystallite sizes.
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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".