Evaluation of the degradation of the graphene-polypropylene composites of masks in harsh working conditions
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
The recent COVID-19 outbreak has led health authorities to recommend at least the use of surgical masks, most preferably respirators (FFP2 or KN95), to prevent the spread of the virus. Non-woven fabrics have been chosen as the best option to manufacture the face masks, due to their filtration efficiency, low cost, and versatility. Modifying the mask filters with graphene has been of great interest due to its potential use as antibacterial and virucidal properties. Indeed, some companies have commercialized face masks in which graphene is coated and/or embedded. However, the Canadian sanitary authorities advised against using the Shandong Shengquan New Materials Co. graphene masks because of the possibility of pulmonary damage produced by graphene inhalation. Thus, we have analyzed the stability of the graphene filter of these masks and compared it with two other commercially available graphene mask filters, evaluating the morphological and spectroscopical change of the fibers, as well as the particles released during the endurance tests. Our work introduces the necessary tools and methodology to evaluate the potential degradation of face masks under extreme working conditions. These methods complement the present standard tests ensuring the security of the new filters based on composites or nanomaterials.
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 | 0.000 |
| 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 it