S Amrutha
Papers
2
Total Citations
12
H-Index
2
About
S. Amrutha is pioneering the intersection of robotics, computer vision, and analytical chemistry to transform how we analyze water quality. Her research centers on using high-throughput robotic systems and machine learning to interpret the complex patterns left by evaporating liquid droplets—a field with profound implications for environmental monitoring and industrial process control. In her landmark 2025 study, she developed a robotic drop imager that collected over 23,000 images of dried salt stains, training a multi-layer perceptron neural network to identify both salt type and concentration with greater than 90% accuracy (9 citations). Building on this foundation, she introduced a novel photo-based method for quantifying water hardness—a critical parameter for domestic and industrial systems—using deposit patterns formed during droplet evaporation (3 citations). By replacing traditional techniques like titration and atomic absorption spectroscopy with a simple, scalable imaging approach, Amrutha’s work promises to democratize water quality testing. Her innovative fusion of automation, machine learning, and physical chemistry marks her as a rising leader in low-cost, high-throughput analytical methods.
Research Focus
Key Achievements
Top Papers
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