Observational analysis showed humidity significantly alters crystallization patterns in salt crystals, indicating deep learning models can accurately categorize different salts.
We designed, fabricated, and utilized a humidity control chamber to investigate how controlled relative humidity (RH) affects crystallization patterns of two different salts; sodium chloride (NaCl) and ammonium chloride (NH4Cl) on glass slides. We also performed high-resolution imaging and MATLAB-based analysis to extract morphological and textural features of dried salt deposits, which were then assessed using Principal Component Analysis (PCA) technique. Results showed that humidity significantly impacted drying times and crystal morphologies. NH4Cl formed dendritic structures that grew more complex with higher humidity, while NaCl produced cubic/hopper crystals, whose size and aggregation varied with humidity. PCA analysis showed humidity-specific patterns, with NH4Cl displaying greater sensitivity. Our results demonstrate that the controlled humidity systematically alters salt crystallization dynamics, and advanced image analysis can precisely quantify these morphological signatures. Deep learning neural network models predict the correct salts from their image morphologies with over 97% accuracy.
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Pudasaini et al. (2025) studied this question.
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