Antiviral and Antibacterial 3D-Printed Products Functionalised with Poly(hexamethylene biguanide).

Infection prevention and public health are a vital concern worldwide, especially during pandemics such as COVID-19 and seasonal influenza. Frequent manual disinfection and use of chemical spray coatings at public facilities are the typical measures taken to protect people from coronaviruses and other pathogens. However, limitations of human resources and coating durability, as well as the safety of disinfectants used are the major concerns in society during a pandemic. Non-leachable antimicrobial agent poly(hexamethylene biguanide) (PHMB) was mixed into photocurable liquid resins to produce novel and tailor-made covers for public facilities via digital light processing, which is a popular 3D printing technique for satisfactory printing resolution. Potent efficacies of the 3D-printed plastics were achieved in standard antibacterial assessments against S. aureus, E. coli and K. pneumoniae. A total of 99.9% of Human coronavirus 229E was killed after being in contact with the 3D-printed samples (containing the promising PHMB formulation) for two hours. In an eight-week field test in Hong Kong Wetland Park, antibacterial performances of the specially designed 3D-printed covers analysed by environmental swabbing were also found to be satisfactory. With these remarkable outcomes, antimicrobial products prepared by digital light processing 3D printing can be regarded as a reliable solution to long-term infection prevention and control.

A novel approach in predicting virtual garment fitting sizes with psychographic characteristics and 3D body measurements using artificial neural network and visualizing fitted bodies using generative adversarial network.

Advances in technology have brought accessibility to garment product fitting procedures with a virtual fitting environment and, in due course, improved the supply chain socially, economically, and environmentally. 3D body measurements, garment sizes, and ease allowance are the necessary factors to ensure end-user satisfaction in the apparel industry. However, designers find it challenging to recognize customers' motivations and emotions towards their preferred fit and define ease allowances in the virtual environment. This study investigates the variations of ease preferences for apparel sizes with body dimensions and psychological orientations by developing a virtual garment fitting prediction model. An artificial neural network (ANN) was employed to develop the model. The ANN model was proved to be effective in predicting ease preferences from two major components. A non-linear relationship was modeled among pattern parameters, body dimensions, and psychographic characteristics. Also, to visualize the fitted bodies, a generative adversarial network (GAN) was applied to generate 3D samples with the predicted pattern parameters from the ANN model. This project promotes mass customization using psychographic orientations and provides the perfect fit to the end users. New size-fitting data is generated for improved ease preference charts, and it enhances end-user satisfaction with garment fit.