RETRACTED: Modification of SBA-15 mesoporous silica as an active heterogeneous catalyst for the hydroisomerization and hydrocracking of n-heptane
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
In this study, a mesoporous SBA-15 silica catalyst was prepared and modified with encased 1% platinum (Pt) metal nanoparticles for the hydrocracking and hydroisomerization of n-heptane in a heterogeneous reaction. The textural and structural characteristics of the nanostructured silica, including both encased and non-encased nanoparticles, were measured using small-angle X-ray diffraction (XRD), nitrogen adsorption-desorption porosimetry, Brunauer-Emmett-Teller (BET) surface area analysis, Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Catalytic testing was carried out in a plug-flow reactor under highly controlled operating conditions involving the reactant flow rate, pressure, and temperature. Gas chromatography was used to analyze the species as they left the reactor. The results demonstrated that 1% Pt/SBA-15 has a high n-heptane conversion activity (approximately 85%). Based on the results of this experimental work, there is no selectivity in the SBA-15 catalysts for isomerization products because they are inactive at the relatively low temperature that is essential for hydroisomerization. On the other hand, the SBA-15 catalysts have a considerable selectivity for products that have cracks, owing to their ability to withstand extremely high temperatures (300-400 °C) as well as the availability of Lewis acid sites within the catalyst structure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".