Cellulose, nanocellulose, and antimicrobial materials for the manufacture of disposable face masks: A review
Bibliographic record
Abstract
Cellulose is among the most promising renewable and biodegradable materials that can help meet the challenge of replacing synthetic fibers currently used in disposable N95 respirators and medical face masks. Cellulose also offers key functionalities that can be valued in filtration applications using approaches such as nanofiltration, membrane technologies, and composite structures, either through the use of nanocellulose or the design of functional composite filters. This paper presents a review of the structures and compositions of N95 respirators and medical face masks, their properties, and regulatory standards. It also reviews the use of cellulose and nanocellulose materials for mask manufacturing, along with other (nano)materials and composites that can add antimicrobial functionality to the material. A discussion of the most recent technologies providing antimicrobial properties to protective masks (by the introduction of natural bioactive compounds, metal-containing materials, metal-organic frameworks, inorganic salts, synthetic polymers, and carbon-based 2D nanomaterials) is presented. This review demonstrates that cellulose can be a solution for producing biodegradable masks from local resources in response to the high demand due to the COVID-19 pandemic and for producing antimicrobial filters to provide greater protection to the wearer and the environment, reducing cross-contamination risks during use and handling, and environmental concerns regarding disposal after use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".