Transparent PAN:TiO2 and PAN-co-PMA:TiO2 Nanofiber Composite Membranes with High Efficiency in Particulate Matter Pollutants Filtration
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
Abstract Particulate matter is one of the main pollutants, causing hazy days, and it has been serious concern for public health worldwide, particularly in China recently. Quality of outdoor atmosphere with a pollutant emission of PM2.5 is hard to be controlled; but the quality of indoor air could be achieved by using fibrous membrane-based air-filtering devices. Herein, we introduce nanofiber membranes for both indoor and outdoor air protection by electrospun synthesized polyacrylonitrile:TiO 2 and developed polyacrylonitrile-co-polyacrylate:TiO 2 composite nanofiber membranes. In this study, we design both polyacrylonitrile:TiO 2 and polyacrylonitrile-co-polyacrylate:TiO 2 nanofiber membranes with controlling the nanofiber diameter and membrane thickness and enable strong particulate matter adhesion to increase the absorptive performance and by synthesizing the specific microstructure of different layers of nanofiber membranes. Our study shows that the developed polyacrylonitrile-co-polyacrylate:TiO 2 nanofiber membrane achieves highly effective (99.95% removal of PM2.5) under extreme hazy air-quality conditions (PM2.5 mass concentration 1 mg/m 3 ). Moreover, the experimental simulation of the test in 1 cm 3 air storehouse shows that the polyacrylonitrile-co-polyacrylate:TiO 2 nanofiber membrane (1 g/m 2 ) has the excellent PM 2.5 removal efficiency of 99.99% in 30 min.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it