Surface Investigation of Tungstophosphoric Acid Supported on Ordered Mesoporous Aluminosilicates for Biodiesel Synthesis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Aluminosilicates with ordered hexagonal mesopores denoted as MAS-7 and MAS-9 with uniform size were synthesized from zeolite β assembly and ZSM-5 precursors, respectively, with P123 as a surfactant, via the cooperative self-assembly pathway. A series of 12 tungstophosphoric acid (TPA) supported on MAS-7 and MAS-9 (H 3 PW 12 O 40 /MAS-7/MAS-9) catalysts for biodiesel production were synthesized via the wet impregnation technique. The characterization of the supports and catalysts was done using N 2 adsorption–desorption analysis, X-ray diffraction (XRD), and pyridine adsorption Fourier transform infrared and Raman spectroscopies. Also, the 29 Si magic-angle spinning and cross-polarization/MAS nuclear magnetic resonance (NMR) techniques were employed to study MAS-7/MAS-9 and the supported solid acid catalyst surfaces. The nitrogen sorption analysis and XRD patterns indicated the formation of well-defined mesoporous materials, whereas IR spectroscopy confirmed the presence of four distinct types of OH groups with varying degrees of acidity. 29 Si MAS NMR signified a stronger interaction between the framework of mesoporous aluminosilicates and H 3 PW 12 O 40 . The X-ray absorption near-edge spectra of L 1 - and L 3 -edge tungsten showed that W in the H 3 PW 12 O 40 /MAS-9 sample exists as W 6+, indicating the tungsten environment similar to that of H 2 WO 4 with O h symmetry. The catalytic activity of the synthesized catalysts was investigated for biodiesel synthesis using unrefined green seed canola oil as a feedstock, giving a methyl ester yield of 76.5–88.7 wt % under optimized reaction conditions. The catalyst activities were strongly correlated with the surface chemistry of the TPA-supported MAS-7 and MAS-9 catalysts.
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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".