Effects of hydrothermal treatment on performance of flexible ZnO/PAN nanofibers for toluene vapor removal
Bibliographic record
Abstract
Polyacrylonitrile nanofibers decorated by zinc oxide nanorods were fabricated by combination of electrospinning technique and hydrothermal treatment. Suspension of 20 nm ZnO nanopartilces in PAN solution was subjected to electrospinning technique. The composite nanofibers of ZnO and PAN in the average diameter of 317 nm were obtained. The ZnO nanopartibles on the surface of PAN nanofibers act as nucleation sites for growth of ZnO nanorods. The obtained ZnO/PAN nanofibers were subjected to hydrothermal treatment process using the autoclave containing solution of zinc acetate and hexamethylenetetramine in water. The ZnO nanorods formed on the surface of PAN nanofibers. In addition, influence of zinc acetate concentration, treatment temperature, treatment time, and zinc acetate : hexamethylenetetramine ratio were also studied. It was also found that the obtained ZnO/PAN nanofibers still possess flexibility. The ZnO/PAN nanofibers were subjected to investigate performance of VOCs removal using photocatalytic reaction. Toluene was used as delegate for VOCs. It was found that the gaseous toluene could be degraded for more than 95% within 2 hours.
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 | 0.000 | 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".