Recent Advances in Functionalized Mesoporous Silica Frameworks for Efficient Desulfurization of Fuels
Bibliographic record
Abstract
Considerable health and climate benefits arising from the use of low-sulfur fuels has propelled the research on desulfurization of fossil fuels. Ideal fuels are urgently needed and are expected to be ultra-low in sulfur (10-15 ppm), with no greater than 50 ppm sulfur content. Although several sulfur removal techniques are available in refineries and petrochemical units, their high operational costs, complex operational needs, low efficiencies, and higher environmental risks render them unviable and challenging to implement. In recent years, mesoporous silica-based materials have emerged as promising desulfurizing agents, owing to their high porosity, high surface area, and easier functionalization compared to conventional materials. In this review, we report on recent progress in the synthesis and chemistry of new functionalized mesoporous silica materials aiming to lower the sulfur content of fuels. Additionally, we discuss the role of special active sites in these sorbent materials and investigate the formulations capable of encapsulating and trapping the sulfur-based molecules, which are challenging to remove due to their complexity, for example the species present in JP-8 jet fuels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".