Superhydrophobic lignin reinforced rubber film as oil water separator
Bibliographic record
Abstract
Natural rubbers (NR) are used widely in industry, yet they exhibit diminished tensile strength and deteriorate under high temperature and moisture conditions. Thus, additional substances are needed to improve their mechanical properties and thermal stability. In this study, we examined a hypothesis that the incorporation of lignin derivative, which was fabricated via aqueous-based copolymerization of kraft lignin (K) and aminopropyl/flurosilsesquioxane (WAPFSS), into the NR matrix would improve the hydrophobic, thermal and mechanical characteristics of NR. The findings of this study revealed that 20 wt% incorporation of lignin-silsesquioxane copolymer (W) into the NR formulation (W20) improved the mechanical properties of NR films by 81 %, which was 58 % higher than its counterpart, i.e., kraft lignin (KL) included NR matrix (K20 sample). Interestingly, the W20 sample was more mechanically durable after heat treatment and water exposure than K20 and pure NR. W20's 150° water contact angle and 10° slide angle confirmed its superhydrophobicity, which survived after high-temperature exposure and sandpaper wear tests. The W20 adsorbed oil at a higher rate than water and showed a 96 % oil separation efficiency. The present study provides foundations for fabricating superhydrophobic and sustainable lignin-reinforced natural rubber films with potential applications as oil and water separators.
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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 | 0.001 |
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".