Pharmaceutical manufacturers have long confronted challenges in monitoring the attributes of drying blends, a crucial step in processing medicinal products and chemicals, due to their complex composition and dynamic behavior. Currently, two non-invasive characterisation methods are typically employed: researchers either image patterns and count individual particles, or they utilise scattered light to estimate the particle size distribution. While the previous process is indeed time-consuming and generates significant waste, it’s unclear why this would make the alternative more appealing.
Recently, Massachusetts Institute of Technology (MIT) engineers and researchers have created an innovative technology that has been demonstrated to revolutionize the manufacturing processes for pharmaceutical capsules and powders, significantly boosting efficiency and accuracy while minimizing the risk of defective product batches. A newly published open-access paper, “_______”, in the prestigious journal _______________, builds upon previous research by presenting an innovative, accelerated approach to _______.
While understanding the habits of scattered light is crucial in optics, says Dr. Qihang Zhang, a researcher at Tsinghua University. “Through advancements in understanding scattered mild, we have also developed a valuable tool for the pharmaceutical industry.” Discovering the root cause of an issue and rectifying it through meticulous examination of fundamental principles is undoubtedly the most exhilarating aspect for the analytics team.
A novel pupil-signal-driven approach to estimating pupillary sway dynamics minimizes the required number of frames for accurate analysis. The researchers assert that their data-driven model can accurately infer the powder measurement distribution from a solitary frame of speckle image, thereby dramatically reducing the reconstruction time from 15 seconds to just 0.25 seconds.
“According to Zhang, the team’s primary achievement is a remarkable acceleration of a particle measurement detection method, boasting a 60-fold improvement, as well as a comprehensive optimisation of both algorithms and hardware.” This high-speed probe successfully detects the dimensional evolution in rapid dynamic processes, providing a platform for analyzing patterns of operations in the pharmaceutical industry, including drying, mixing, and blending.
The proposed method yields a cost-effective, non-invasive particle measurement device that collects faint reflected light from powder surfaces. The compact and moveable prototype is compatible with most drying programs available, provided there is a remark window. This online measurement strategy has the potential to significantly improve manufacturing process efficiency and product quality by enhancing management’s ability to monitor and optimize operations in real-time. Lack of online monitoring previously hindered a systematic examination of dynamic patterns in manufacturing processes. This probe has the potential to establish a novel foundation for conducting sequence analysis and modeling, facilitating advancements in particle measurement evolution.
This collaborative project, a successful fusion of physics and engineering expertise, stems from the esteemed MIT-Takeda initiative. Collaborators are affiliated with the Massachusetts Institute of Technology (MIT), specifically with three departments: Mechanical Engineering, Chemical Engineering, Electrical Engineering, and Computer Science. Professor George Barbastathis, a renowned expert in mechanical engineering from Massachusetts Institute of Technology (MIT), serves as the lead author for this piece.
