Mycelium as a self-growing biobased material for the fabrication of single-layer masks
Bibliographic record
Abstract
Disposable face masks are an essential piece of personal protective equipment for workers in medical facilities, laboratories, and the general public to prevent the spread of illnesses and/or contamination. Covid-19 resulted in an uptick in the usage and production of face masks, exacerbating issues related to the waste and recycling of these materials. Traditionally, face masks are derived from petrochemicals, such as melt-blown or spunbound polypropylene. As such, there is a need to find sustainable mask materials that can maintain or improve the performance of petrochemical masks. This paper explores an alternative mask material that utilizes fungal mycelium as self-growing filaments to enhance the efficiency of individual polypropylene mask layers. By engineering the growth pattern and time, breathability and filtration efficiency was optimized such that one layer of the mycelium-modified mask could replace all three layers of the traditional three-layer mask. Additionally, it was found that the mycelium-modified mask exhibits asymmetric hydrophobicity, with super-hydrophobicity at the composite-air interface and lower hydrophobicity at the composite-medium interface. This property can improve the performance of the modified mask by protecting the mask from external liquids without trapping water vapor from the user's breath. The findings from this study can provide a basis for further development of mycelium to create sustainable filtration materials with enhanced functionality.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".