Development of alginate beads for precise environmental release applications: A design of experiment based approach and analysis

Controlled release of active ingredients are important for drug delivery and more recently environmental applications including modulated dosing of chemical and biological controls. This study demonstrates the importance of investigating various material science factors that can influence the diffusion rates of alginate beads to improve and tune their performance for marine environmental applications. This investigation aimed to design a rational workflow to aid in leveraging alginate bead use as a carrier matrix for releasing a specific active agent into water. Experiments were conducted to focus on the narrow a large list of relevant material formulation parameters, which included chitosan molecular weight, chitosan concentration, calcium concentration, drop height, and bead size. Once the most relevant material preparation methods were screened, a more robust statistic Design of Experiments approach was performed and results determined the important (and unimportant) factors for increasing dye release kinetics in marine water. The process was further streamlined by narrowing the critical experimental factors to a three-level based on the prior analysis: chitosan MW, chitosan concentration, and bead size. Analysis of the collected data indicated that while chitosan MW had a negligible impact (Fstatistic = 0.22), bead size (Fstatistic = 60.33) significantly influenced the diffusion rates based on surface area. However, chitosan MW had minor effects where lower chitosan MW enabled higher product release rates. This case investigation was a novel application of the design of experiment approach towards environmental applications to understand differences in release rates to marine waters for the first time and the workflow provided also serve as the basis for researchers to optimize other environmental applications requiring optimization when it is unknown how a large number of formulation variables will impact performance in different environmental scenarios.