Preparation of a hydrophobic recycled jute-based nonwoven using a titanium dioxide/stearic acid coating
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work aims at developing a hydrophobic treatment for jute fiber-based nonwovens. Three solutions of titanium dioxide (TiO2) nanoparticles were prepared through a sol–gel method by varying the molar ratio of the various constituents. The nonwoven was pretreated with these solutions before being impregnated with different concentrations of stearic acid. The TiO2 nanoparticles synthesized are amorphous; their size varies with the concentration of ethanol used as a solvent in the sol–gel method. The nanoparticle coating produced on the jute fibers is uniform. The nonwoven wettability was evaluated by measuring its water contact angle and retention time; the nonwoven became hydrophobic at the lowest fatty acid concentration tested. An increase in the stability of the hydrophobicity was observed when the TiO2 nanoparticle pretreatment was used compared to the application of the stearic acid treatment only. No detrimental effect of the hydrophobic treatment on the nonwoven mechanical performance and thermal stability was observed. These results demonstrate the potential of the TiO2 nanoparticle/stearic acid treatment as a fast method to provide a stable hydrophobicity to recycled jute-based nonwovens.
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.
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 | 0.000 |
| 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 | 0.001 |
| 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 it