No abstract

Polyelectrolyte (PE)/surfactant (S) mixtures play a distinguished role in the efficacy of shampoos and toiletries primarily due to the deposition of PE/S precipitates on the hair surface upon dilution of the formulations. The classical interpretation of this phenomenon is a simple composition change during which the system enters the two-phase region. Recent studies, however, indicated that the phase properties of PE/S mixtures could be strongly affected by the applied solution preparation protocols. In the present work, we aimed at studying the impact of dilution on the nonequilibrium aggregate formation in the sodium poly­(styrenesulfonate) (NaPSS)/dodecyltrimethylammonium bromide (DTAB)/NaCl system. Mixtures prepared with hundredfold dilution of concentrated NaPSS/DTAB/NaCl solutions in water were compared with those ones made by rapid mixing of dilute NaPSS/NaCl and DTAB/NaCl solutions. The study revealed that the phase-separation concentration range as well as the composition, morphology, and visual appearance of the precipitates were remarkably different in the two cases. These observations clearly demonstrate that the dilution/deposition process is also related to the nonequilibrium phase properties of PE/S systems, which can be used to modulate the efficiency of various commercial applications.

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied “out of the lab”. During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.