Water-soluble nonionic surfactant, pentaethylene glycol monododecyl ether, C12E5, spontaneously blooms to the surface of spin-cast hydrophobic polyiso-prenes, generating hydrophilic surfaces. This system represents a simple model for hydrophilic chemical modification of rubbery polymers yet demonstrates surprisingly rich, complex and unexpected behaviour. The ver-tical depth profiles were quantified using neutron reflectometry (NR) and deuterated surfactant, and the surface properties were characterized using con-tact angle analysis and atomic force microscopy (AFM). Despite the low surface tension of the toluene solvent used in film preparation and the low surface en-ergy of the PI matrix, NR depth profiles revealed clear evidence of surfactant segregation. This surface layer was typically thicker than a monolayer, but incomplete, yet was remarkably stable with respect to dissolution, even when exposed to hundreds of thousands of times the volume of water required to dissolve all the surfactant on the surface. Despite the apparent resistance to removal from the surface, water exposure does alter the subsequent wettabil-ity of the surface, with a hydrophilic-to-hydrophobic transition occurring after rinsing. Complementary AFM images of these C12E5 / cis-PI films showed unexpected strand-like features on the surface of the film, which we attribute to a non-uniform lateral distribution of some of the surfactant. This surface structure becomes more evident after rinsing, and it appears that there are two distinct populations of surfactant on the PI film surface. We conclude that some of the bloomed surfactant exists as layers, which are relatively inert with respect to rinsing or surface modification, and some is laterally inhomogene-ous. This latter population is primarily responsible for surface wetting be-havior, but is not detected by specular NR.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.