Jung Hyun Kim | Searcharxiv

arXiv2026

Evaporation-induced colloidal diffusiophoresis in electrolyte solutions

Non-volatile solutes accumulate near a receding drying front, generating concentration gradients that can drive directional colloidal transport by diffusiophoresis. This dynamic spatial arrangement of colloidal particles could affect the microstructure and macroscopic properties of the dried film. Here, we combine experiments and theory to investigate polystyrene particles suspended in electrolyte solutions drying within a capillary tube. By varying the electrolyte composition and ambient relative humidity, we show that evaporation-induced ion gradients drive particles toward the liquid-gas interface and enhance interfacial accumulation, whereas slower drying at higher humidity weakens the gradient and particle migration. We use the heat balance integral method to predict the transient solute field for a receding interface and couple it to particle transport to capture the observed particle trajectories and accumulation trends. These results identify background electrolytes and drying rate as coupled controls on particle distribution and provide a predictive framework to optimize formulation and processing parameters for manufacturing slurry-based materials, such as battery electrodes and turbine coatings.

cond-mat.soft↗

arXiv2021

Machine learning in the social and health sciences

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.

cs.LG↗