Polymeric and Hybrid Membranes for Achieving Ultra-Low Sulfur Fuel Requirements
Bibliographic record
Abstract
This review aims to explore membrane-based alternative methods for removing harmful sulfur compounds in fuels to replace the inefficient conventional techniques.Membranebased methods provide an efficient route to achieve very low sulfur content in fuels (< 10 ppm), as required by international environmental policies.A systematic review of 42 experimental studies reported over the period from 2005 to 2025, being the timeframe that would most likely encompass an actual paradigm change in membrane-based technologies.The data were retrieved from scientific databases, including Scopus, Web of Science, ScienceDirect, and ACS, to evaluate the performance of polymer and composite membranes for desulfurization.Innovative membrane systems like polydimethylsiloxane (PDMS), polyethylene glycol (PEG), polyimide, and mixed-layer membranes (MMMs) were found to be highly effective in both evaporation-and permeability-based desulfurization processes.PEG-PI MMMs reinforced with metal organic frameworks (MOFs) demonstrated removal efficiencies as high as 80% and a permeability of > 200 g/mh, which were significantly higher compared to that for neat polymeric membranes.The key separation principles include diffusion-dissolution, facilitated transport with complexes (Ag, Cu, MOFs), and molecular sieving.Finally, in spite of challenges such as polymer swelling and stability remaining, polyethylene glycol (PEG) and polyimide (PI)-MMMs, especially those enhanced with metal-organic frameworks (MOFs), stand out as strategic industrial candidates due to the best balance of flow, selectivity, and stability.The future direction must urgently focus on long-term stability testing using real fuel streams rather than model fuels to confirm the practical viability of these integrated membranes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".