Researchers at the Massachusetts Institute of Technology (MIT) have developed a faster method for estimating particle size distributions in pharmaceutical manufacturing. The study, published on September 23, in the journal Light: Science & Application, describes a technique that accelerates analysis time by 60 times.
The new method, based on pupil engineering and machine learning, can estimate powder size distribution from a single snapshot speckle image. This reduces reconstruction time from 15 seconds to just 0.25 seconds, offering real-time monitoring capabilities for pharmaceutical manufacturing.
Dr. Qihang Zhang, a former MIT PhD student and now an associate researcher at Tsinghua University, explained, "Our learning-based model can estimate the powder size distribution from a single snapshot speckle image, consequently reducing the reconstruction time from 15 seconds to a mere 0.25 seconds."
The technique uses a low-cost, noninvasive particle size probe that collects back-scattered light from powder surfaces. Its compact and portable design makes it compatible with most drying systems in the market, requiring only an observation window.
This breakthrough addresses a longstanding challenge in pharmaceutical manufacturing: monitoring the characteristics of drying mixtures, a critical step in producing medication and chemical compounds. Traditional methods either involved time-intensive imaging and particle counting, or used scattered light to estimate particle size distribution (PSD).
The accelerated PSD estimation method offers several potential benefits, including improved manufacturing efficiency and accuracy, fewer failed batches of products, real-time monitoring of fast dynamical systems, and a platform for studying models of processes including drying, mixing, and blending.
Professor George Barbastathis of MIT's Department of Mechanical Engineering, the study's senior author, led a collaborative effort involving researchers from three MIT departments: Mechanical Engineering, Chemical Engineering, and Electrical Engineering and Computer Science.
This research, part of the MIT-Takeda program, demonstrates the power of interdisciplinary collaboration between physicists and engineers. As pharmaceutical companies adopt this technology, it could lead to more efficient drug manufacturing processes and potentially lower medication costs for consumers.