In this paper we present a comprehensive account of our results on streamer propagation in dielectric fluids in point-plane geometries. Propagation velocities for both positive and negative streamers have been determined as a function of the following parameters: temperature, pressure, density, viscosity, composition, and conductivity. Effects of voltage and interelectrode spacing were examined. Current and light emission during streamer growth were measured. The relation between shock wave and streamer velocities was investigated. Small concentrations of low-ionization potential additives markedly accelerated the positive streamers, while electron scavengers accelerated the negative streamers. Mechanisms to account for these observations are discussed.

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Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.

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