Synthesis of a Novel Cellulose Nanofiber-Based Composite Hydrogel with Poly(methyl methacrylate-<i>co</i>-methacrylic Acid) for Effective Water Removal from Liquid Fuels
Bibliographic record
Abstract
Water is present in liquid fuels in three different forms: dissolved, free, or emulsified, and its presence can considerably impair fuel quality by encouraging microorganism growth. This growth contributes to the generation of sludge, an increase in turbidity, and the corrosion of tanks and mechanical components of motor vehicles. In this context, this research work proposes the synthesis of a nanocomposite hydrogel made of poly(methyl methacrylate- co -methacrylic acid) and cellulose nanofibers (CNFs) by free radical polymerization for removal of water from diesel. An extensive physicochemical characterization of the hydrogels was performed, and a full experimental design (2 2 with 3 central points) evaluated the influence of the different CNF percentages and temperatures on the maximum swelling degree of the hydrogel nanocomposites. According to this experimental design, the only statistically significant independent variable was the CNF percentage. Finally, batch tests were performed to build the kinetic curves based on five adsorbents: CNF, poly(MMA- co -MAA), and poly[(MMA- co -MAA) with CNF at 1, 2.5, and 5%]. All samples were highly effective at removing water from commercial diesel in a short time. In this analysis, CNF reached equilibrium in 3 h, while all other samples required 8 h. All composite hydrogels exceeded 80% water removal at the equilibrium time. The high efficiency of the nanocomposites was demonstrated, suggesting the potential for application on an industrial scale, over a wide range of water concentrations.
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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".