A Deep Learning Perspective on Dropwise Condensation (Adv. Sci. 22/2021)

Y Suh, J Lee, P Simadiris, X Yan, S Sett, L Li… - Advanced …, 2021 - Wiley Online Library
The relationship between droplet statistics and heat and mass transfer has long remained
unclear due to the challenge in quantifying the overwhelming number of rigorous physical …

Time-Lapse Quantitative Analysis of Drying Patterns and Machine Learning for Classifying Abnormalities in Sessile Blood Droplets

A Pal, M Yanagisawa, A Gope - medRxiv, 2024 - medrxiv.org
When a colloidal droplet dries on a substrate, a unique pattern results from multi-facet
phenomena such as Marangoni convection, capillary flow, mass transport, mechanical …

Self-assembled patterns formed in evaporating droplets to analyze bi-component homeopathic preparations in the low dilution range

MO Kokornaczyk, S Würtenberger… - Homeopathy, 2023 - thieme-connect.com
Background Homeopathic complex remedies, composed of several homeopathic medicines
in the low potency range, are frequently used in the treatment of a number of common …

Texture identification in liquid crystal-protein droplets using evaporative drying, generalized additive modeling, and K-means Clustering

A Pal, A Gope - The European Physical Journal E, 2024 - Springer
Sessile drying droplets manifest distinct morphological patterns, encompassing diverse
systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study …

Droplet breakage and coalescence in liquid–liquid dispersions: Comparison of different kernels with EQMOM and QMOM

D Li, Z Gao, A Buffo, W Podgorska… - AIChE Journal, 2017 - Wiley Online Library
Droplet coalescence and breakage in turbulent liquid–liquid dispersions is simulated by
using computational fluid dynamics (CFD) and population balance modeling. The …

Prediction of particle agglomeration during nanocolloid drying using machine learning and reduced-order modeling

K Kameya, H Ogata, K Sakoda, M Takeda… - Chemical Engineering …, 2024 - Elsevier
Our previous studies, which employed Kinetic Monte Carlo (KMC) simulation to model
nanoparticle agglomeration during nanocolloid drying, revealed a discernible relationship …

Image‐based characterization of flocculation processes through PLS inspired representation learning in convolutional neural networks

A Baum, R Moiseyenko, S Glanville… - Journal of …, 2024 - Wiley Online Library
Monitoring of flocculation processes such as those used in downstream processing of a
fermentation broth is essential for process control. One approach is to apply microscopic …

Patterns from dried drops as a characterisation and healthcare diagnosis technique, potential and challenges: A review

K Sefiane, G Duursma, A Arif - Advances in Colloid and Interface Science, 2021 - Elsevier
When particulate-laden droplets evaporate, they leave behind complex patterns on the
substrate depending on their composition and the dynamics of their evaporation. Over the …

Image-based analysis of patterns formed in drying drops

A Pal, A Gope, GS Iannacchione - … 2019, Tezpur, India, December 17-20 …, 2019 - Springer
Image processing and pattern recognition offer a useful and versatile method for optically
characterizing drops of a colloidal solution during the drying process and in its final state …

Multidimensional Outcome Parameters in a Cress Seedling—CuCl2 Crystallization Assay to Corroborate Specific Effects of Stannum metallicum 30x Compared to …

P Doesburg, JO Andersen, C Scherr… - …, 2024 - thieme-connect.com
Background Previously we developed a test system which yielded highly significant
evidence for specific effects of a Stannum metallicum 30x preparation in a multi-center …