Can Medical-Grade Gloves Provide Protection after Repeated Disinfection?
Bibliographic record
Abstract
The advent of the COVID-19 pandemic has generated an increased consumption of personal protective equipment (PPE), including gloves and masks, by healthcare workers and by the general public at a global scale. This has generated substantial shortage of these single-use and disposable PPEs that will end up as a landfill waste. Extending the life cycle of PPEs, such as gloves, by disinfecting treatments could help mitigate these concerns. However, the effect of various disinfection treatments on the functionality of gloves is unknown. In this study, six commonly used viral disinfection treatment methods (i.e., ultraviolet (UV) radiation, dry heat, steam, alcohol, chlorine compounds, and quaternary ammonium compounds) were evaluated for their effect on the performance attributes of two commonly used medical-grade gloves and nitrile and vinyl (latex) gloves. The barrier properties of both gloves against water and ethanol vapor flux were not affected up to 10 cycles of disinfection cycles. However, the increase in the disinfection cycle from 10 to 20 slightly reduced their barrier properties with minor variation in the type of disinfection and glove type. Infrared spectroscopy and microscopy investigations confirmed that both types of gloves could withstand up to 20 cycles of disinfection treatments with no observable change in the chemical structure and surface morphology of the disinfected surfaces, respectively. Lastly, tear property testing of the gloves indicated little to no change from the baseline after 20 cycles of treatment in both the nitrile and vinyl-based gloves. Overall, this study indicated that alcohol, UV, and heat treatment could be acceptable disinfection methods that allow the reuse of gloves up to 20 cycles. Such repeated disinfection of gloves not only reduces the strain on the supply of gloves but also decreases the postconsumer landfilled waste and environmental footprint of gloves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".