Dataset Information

Leveraging Wettability Engineering to Develop Three-Layer DIY Face Masks From Low-Cost Materials

ABSTRACT: With the rapid spread of COVID-19 worldwide, the demand for appropriate face masks in the market has also skyrocketed. To ease strain on the supply of masks to the essential healthcare sector, it has become imperative that ordinary people rely more on home-made masks that can be easily put together using commonly available materials, while at the same time performing reasonably at arresting the ingress or egress of airborne droplets. Here, we propose a simple do-it-yourself (DIY) method for preparing a three-layered face mask that deploys two hydrophobic polypropylene nonwoven layers interspaced with a hydrophilic cellulosic cloth. The first hydrophobic layer, facing the user, allows high-momentum droplets (e.g., expelled by a sneeze or cough) to pass through and get absorbed in the next hydrophilic layer, thereby keeping the skin in contact with the mask dry and comfortable. The third (outermost) hydrophobic layer prevents penetration of the liquids from the middle layer to the outside, and also arrests any airborne droplets on its exterior. Simple tests show that our masks perform better in arresting the droplet transmission as compared to surgical masks available in the market.

Electronic supplementary material

The online version of this article (10.1007/s41403-020-00115-9) contains supplementary material, which is available to authorized users.

SUBMITTER: Sarkar S

PROVIDER: S-EPMC7271140 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundNon-pharmaceutical interventions (NPIs) used to limit SARS-CoV-2 transmission vary in their feasibility, appropriateness and effectiveness in different contexts. In Bangladesh a national lockdown implemented in March 2020 exacerbated poverty and was untenable long-term. A resurgence in 2021 warranted renewed NPIs. We sought to identify NPIs that were feasible in this context and explore potential synergies between interventions.MethodsWe developed an SEIR model for Dhaka District, parameterised from literature values and calibrated to data from Bangladesh. We discussed scenarios and parameterisations with policymakers with the aid of an interactive app. These discussions guided modelling of lockdown and two post-lockdown measures considered feasible to deliver; symptoms-based household quarantining and compulsory mask-wearing. We compared NPI scenarios on deaths, hospitalisations relative to capacity, working days lost, and cost-effectiveness.ResultsLockdowns alone were predicted to delay the first epidemic peak but could not prevent overwhelming of the health service and were costly in lost working days. Impacts of post-lockdown interventions depended heavily on compliance. Assuming 80% compliance, symptoms-based household quarantining alone could not prevent hospitalisations exceeding capacity, whilst mask-wearing prevented overwhelming health services and was cost-effective given masks of high filtration efficiency. Combining masks with quarantine increased their impact. Recalibration to surging cases in 2021 suggested potential for a further wave in 2021, dependent on uncertainties in case reporting and immunity.ConclusionsMasks and symptoms-based household quarantining synergistically prevent transmission, and are cost-effective in Bangladesh. Our interactive app was valuable in supporting decision-making, with mask-wearing being mandated early, and community teams being deployed to support quarantining across Dhaka. These measures likely contributed to averting the worst public health impacts, but delivering an effective response with consistent compliance across the population has been challenging. In the event of a further resurgence, concurrent messaging to increase compliance with both mask-wearing and quarantine is recommended.

Dataset Information

Leveraging Wettability Engineering to Develop Three-Layer DIY Face Masks From Low-Cost Materials

Electronic supplementary material

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets