Experimental Optimization of Antifoulant Impregnated Polymer Structures for Marine Applications

Trent Kelly, Tan GZ and Hunt EM

Published on: 2022-12-30

Abstract

Fabricated marine structures often suffer adverse effects from marine biofouling including structural failure due to the accumulated weight and surface profile of hard-shell fouling. While current techniques such as surface coatings can be somewhat effective, they are costly and have a relatively short lifespan. Additionally, there are updated regulations from the environmental agencies that prescribe allowable amounts of biocidal materials in the ocean in certain regions of the world. Antifoulant impregnation of polymers has been recently studied in marine fouling of offshore structures with promising results for microbial and cost efficacy. In this study, experiments were conducted to optimize a blend of antifoulant additives incorporated into a high-density polyethylene matrix during the manufacturing process. Samples were submerged in the Indian Ocean and data was collected over a period of seven months with the primary indicator of fouling being the growth of individual barnacles on the surface. Factors included for optimization include microbial efficacy, cost, and environmental regulations based on geographic location. Results show that for regulated waters, a blend of zinc oxide and ANA, a commercially available antifoulant, is the optimum formula for incorporation into high-density polyethylene. For unregulated waters, a blend of cuprous oxide and ANA provides the optimum formula for microbial and cost efficacy. Experimental data formed the basis for the optimization process used in this study. The methods used were time and cost intensive and dependent upon a variety of environmental factors such as temperature, salinity, and growth rate of individual barnacles. As the need for antifouling structures continues to grow in a variety of industries this experimental method becomes less feasible. Future work by the authors will examine a predictive machine learning approach to surface visualization and optimization of impregnated antifouling structures for use across industries.