A low-cost automated platform for fast and accurate pH control via physics-informed active learning

Area of Science:

• Chemical Engineering
• Biotechnology
• Machine Learning Applications

Background:

• Precise pH adjustment is crucial for chemical synthesis and biopharmaceutical development.
• Complex multi-buffer equilibria present significant challenges for conventional pH control methods, leading to modeling difficulties and inefficiency.

Purpose of the Study:

• To develop a low-cost automated titration platform and a hybrid physics-informed active learning framework.
• To efficiently achieve target pH values in complex buffered systems with minimal experimental iterations.

Main Methods:

• Development of a low-cost automated titration platform (hardware cost < 100 USD).
• Establishment of a hybrid physics-informed active learning framework for pH optimization.
• Validation across diverse buffer systems (phosphate, acetate, citrate, ammonium).

Main Results:

• The hybrid framework achieved target pH values in as few as 3-5 experimental iterations.
• Demonstrated substantial efficiency improvements over purely data-driven methods.
• Rapid convergence to target pH in diverse buffer systems.

Conclusions:

• The developed platform and framework offer a highly efficient solution for pH control in complex systems.
• Provides significant pedagogical value for students and researchers in automated experimentation and machine learning.
• Represents a cost-effective and accessible tool for advancing chemical and biopharmaceutical research.