Superhydrophobic modification of cellulose and cotton textiles: Methodologies and applications
Bibliographic record
Abstract
Superhydrophobic cellulose-based products have immense potential in many industries where plastics and other polymers with hydrophobic properties are used. Superhydrophobic cellulose-based plastic is inherently biodegradable, renewable and non-toxic. Finding a suitable replacement of plastics is highly desired since plastics has become an environmental concern. Despite its inherent hydrophilicity, cellulose has unparalleled advantages as a substrate for the production of superhydrophobic materials which has been widely used in self-cleaning, self-healing, oil and water separation, electromagnetic interference shielding, etc. This review includes a comprehensive survey of the progress achieved so far in the production of super-hydrophobic materials based on cellulose and fiber networks. The methodologies and applications of superhydrophobic-modified cellulose and fiber networks are emphasized. Overall, presented herein is targeting on summarizing some of the aspects that are critical to advance this evolving field of science which may provide new ideas for the developing and exploring of superhydrophobic and green-based materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".