Adsorption of Oil by 3-(Triethoxysilyl) Propyl Isocyanate-Modified Cellulose Nanocrystals
Bibliographic record
Abstract
Oil leaks into water bodies and increased organic pollutants harm the environment and ecosystem in several ways, and cleaning up oil spills from water bodies is a global challenge. This research aimed to construct modified cellulose nanocrystal (CNC) based aerogels with 3-triethoxysilyl propyl isocyanate (TEPIC) to evaluate their potential application in oil adsorption. Here, a freeze-drying method was employed to make CNC aerogels. The aerogels were characterized using scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, porosity and density measurements, Fourier transform infrared spectroscopy (FTIR), water contact angle (WCA) measurement, compressive strength, and oil adsorption capacity. SEM results confirmed that the aerogels have a largely porous structure, including a community of uniformly interconnected cellulose fibers. Moreover, the studied aerogels had a low density due to the high porosity. Also, the small pore diameter and high specific surface area were confirmed by the BET evaluation. FTIR confirmed the existence of functional groups and strong hydrogen bonding between CNC/TEPCI/Urea molecules. All TEPIC-modified CNC aerogels had water contact angle values greater than 130° indicating their hydrophobicity. The highest oil and glycerol adsorption was obtained with the use of modified CNC aerogels. Thus, the sample modified with 3 wt% TEPIC showed the highest adsorption capacities of 130 ± 7.22, 120 ± 4.75, and 95.28 ± 4.82 gg−1 for motor oil, vegetable oil and glycerol, respectively. The results of this study showed that ultra-light, hydrophobic and oil adsorbent materials based on chemically modified CNC aerogels can successfully be fabricated.
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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) | 0.000 | 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 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".