Surfactants, as a class of soft materials, exhibit a wide variety of self-assembled structures and physical properties due to their flexible molecular design. However, exploring their full potential remains difficult due to the complexity of their behavior in multicomponent systems. This study introduces an integrated approach that combines molecular simulations and machine learning to predict the structure and performance of aqueous surfactant formulations. Using dissipative particle dynamics, we reproduced and analyzed the self-assembly behavior of over 200 formulations of cleansing agents. While the overall morphology of aggregates showed limited correlation with cleansing performance, detailed analysis revealed that the spatial distribution of weakly hydrophobic components significantly influenced the result. Based on this insight, we constructed machine learning models using molecular descriptors to predict cleansing efficiency, achieving high accuracy with an Extra Tree Regressor. Furthermore, we performed in silico formulation screening to identify promising ingredient combinations. Additionally, a separate model was developed to predict the critical packing parameter from molecular structure using deep learning, achieving high accuracy. These results demonstrate that combining simulation and machine learning offers a powerful method for understanding and designing functional surfactant systems.

Washing technology using fine-bubbles was one of the technologies that were spreading to many fields. In this paper, as a model experiment of washing face, a washing experiment of oily stains on artificial skin was performed by using a washing solution containing microbubbles in flowing water, and applied shear to the object. The experimental results showed that the effect of suspended microbubble was enhanced with increasing washing time when nonionic surfactant solution was used, that the effect of microbubble suspension was enhanced at three surfactant concentrations above and below the critical micelle concentration, and that the effect of suspended microbubble was not enhanced under relatively high temperature conditions.

Historically, humans have feared microorganisms due to their association with diseases, leading to the overuse of disinfectants and antibiotics, which disrupts microbial balance and fosters antibiotic resistance. This paper explores the potential of microbial cleaning, where beneficial microorganisms offer a natural yet effective long-term cleaning performance by producing a variety of enzymes that break down organic soils. This sustained cleaning effect can reduce the frequency and effort of cleaning, while maintaining a natural microbiome in the home. We will cover the mode of action and benefits of microbial cleaning through several experimental studies, and discuss its safety and market acceptance.