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Morphological Signatures of Salt Crystals under Controlled Humidity Using Advanced Image Analysis.

Sanam Pudasaini¹, Amrutha S V², Oliver Steinbock²

¹Department of Chemistry, Florida Agricultural and Mechanical University, Tallahassee, Florida 32307, United States.

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|September 18, 2025

Summary

This summary is machine-generated.

Controlled humidity significantly impacts salt crystallization patterns. Advanced image analysis accurately identifies salt types based on crystal morphology, with deep learning models achieving over 97% accuracy.

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Area of Science:

Materials Science
Chemical Crystallization
Data Analysis

Background:

Crystallization processes are influenced by environmental factors.
Understanding salt crystallization morphology is crucial for various applications.
Relative humidity (RH) is a key environmental parameter affecting crystal formation.

Purpose of the Study:

To investigate the effect of controlled relative humidity (RH) on the crystallization patterns of sodium chloride (NaCl) and ammonium chloride (NH4Cl).
To analyze the morphological and textural features of salt deposits using advanced imaging and statistical methods.
To evaluate the potential of image analysis and machine learning for salt identification based on crystallization patterns.

Main Methods:

Design and fabrication of a humidity control chamber.
Controlled crystallization experiments of NaCl and NH4Cl on glass slides at varying RH levels.
High-resolution imaging and MATLAB-based analysis for feature extraction.
Principal Component Analysis (PCA) for pattern recognition.
Development and testing of deep learning neural network models for salt classification.

Main Results:

Relative humidity significantly affected salt drying times and crystal morphologies.
Ammonium chloride (NH4Cl) formed complex dendritic structures that increased in complexity with higher humidity.
Sodium chloride (NaCl) formed cubic/hopper crystals, with size and aggregation varying based on humidity.
PCA revealed distinct, humidity-specific crystallization patterns for each salt.
Deep learning models accurately predicted salt types from crystal morphologies (>97% accuracy).

Conclusions:

Controlled relative humidity systematically alters salt crystallization dynamics and resulting morphologies.
Advanced image analysis techniques can precisely quantify these morphological signatures.
Machine learning, particularly deep learning, shows high efficacy in identifying salts based on their crystallization patterns, demonstrating the potential for automated analysis and quality control.