High-throughput robotic collection, imaging, and machine learning analysis of salt patterns: Composition and concentration from dried droplet photos

Abstract

Macroscopic deposit patterns resulting from dried solutions and dispersions are often perceived as random and without meaningful information. Their formation is governed by a bewildering interplay of evaporation, crystal nucleation and growth, capillary flows, Marangoni convection, diffusion, and heat exchange that severely hinders mechanistic studies. It is therefore remarkable that the patterns contain subtle clues about the chemical nature of the original solution. To utilize this information, extensive reference image libraries and advanced analysis methods are essential. For this purpose, we developed a robotic drop imager (RODI) that, under non-stop operation, produces up to 2,500 high-resolution images of sample deposits daily. Utilizing RODI, we have assembled an initial library of 23,417 images for seven inorganic salts and five concentration levels. Each image is analyzed and distilled into 47 metric values that capture distinct characteristics of the deposit patterns. This compact dataset is utilized for machine learning and artificial intelligence training, specifically with Random Forest, XGBoost, and a deep learning Multi-Layer Perceptron. We achieved prediction accuracies of 98.6% for the salt type and 92.5% for the combined salt type and initial concentration. Expanded databases will likely enable the rapid identification of broad compositional features from mere photographic images, with possible applications ranging from phone-based apps to field-based analytical and lab safety tools.