This dissertation aims to further the field of cloth face mask research through material and design studies. Improvised, handmade, and manufactured cloth face masks rose in prevalence in response to the Covid-19 pandemic but using and reusing common fabrics to capture exhaled aerosols is still not well understood. National and international organizations have simplified their recommendations to multiple layers of tightly woven fabrics, yet the range of cloth face masks in circulation is much more variable. Therefore, finding fabric layering strategies that are effective yet still comfortable and will withstand repeated cycles of decontamination is the main goal of the material studies. These studies include the effect of decontamination on air permeability and filtration efficiency, electrospinning nanofibers onto nonwoven substrate to be used as core layer in cloth face mask, and modeling air permeability and filtration efficiency of heterogeneous layering of conventional fabrics. Additionally, cloth face mask design has not been addressed formally in research; sizing studies have been conducted on half- and full-facepiece respirators, but none to date have looked at the dynamic fit of face masks. Proper initial (static) and persistent (dynamic) fit of face masks is crucial for both minimizing leakage and maximizing comfort. Therefore, the goal of the design study is to use 3D head scans of dynamic face postures to evaluate specific design features of cloth face masks. The design results can be generalized beyond cloth face masks to medical and non-medical face masks and other headgear. The future of this dissertation is to combine the material studies and design study results into an iterative design process towards new prototype(s) that optimize comfort, fit, and filtration performance with possibilities as smart wearable devices.