569u Ultrasonic Sound Propagation In Porous Materials

Omar L. Sprockel, Drug Product Process & Package Development, Bristol-Myers Squibb, One Squibb Drive, New Brunswick, NJ 08903, Erik Yang, School of Pharmacy and Pharmaceutical Sciences, Purdue University, 1102 nooning tree dr, Chesterfield, MO 63017, Dimuthu A. Jayawickrama, Analytical R&D, Bristol-Myers Squibb, One Squibb Drive, New Brunswick, NJ 08903, Shih-Ying Chang, Process R&D Engineering Technologies, Bristol-Myers Squibb, One Squibb Drive, New Brunswick, NJ 08903, and Lei Li, Baby Care, Proctor & Gamble, 6280 Center Hill, Cincinatti.

Objective

The objective of this study was to develop the theoretical basis for the dependency of sound velocity through porous materials on density so that active ultrasonics can be used to determine the density of ribbons that are intermediates in the roller compaction manufacturing process.

Theoretical

Equation 1 was developed and predicts that sound velocity depends on density for a porous material above the percolation threshold, where V is the sound velocity, E*Max is the modified Young's elastic modulus, S is a scaling factor, ρ is the ribbon density, and ρc is the percolation threshold density. According to percolation theory, the relationship between sound velocity and density of porous materials holds above the percolation threshold density, where the properties of the system would not change abruptly.

                                               

Equation 1

           

Experimental

Sound velocity through a sample was determined by measuring the time of travel of an ultrasound pulse though a sample of a given thickness. The sample was placed between two sensors. Aqualene™, an elastomer with a density close to water, was used as an ultrasonic buffer conduit.  Aqualene™ was placed between the ribbon sample and the sensors. A constant force is applied between the sensors. Ultrasonic waves were passed through the sample between the two sensors.  The waveform data was acquired and the time of travel between the two sensors was determined, based on a threshold value set at three times the background ultrasound noise level.  The sound velocity was calculated from the distance of travel and the time of travel.  The ribbon densities were determined using the weight and volume of the sample.

Results

Five sets of ribbons (n=3) were used to estimate the value of E*Max•S from the ribbon density and sound velocity data set. The sound velocity through twelve additional ribbon samples was determined and their density predicted. The average difference between the predicted and determined ribbon density values was ~ 1.76% (0.00 – 5.25%) and the slope of the predicted versus experimental density was ~1.07.

Conclusion

The theoretical basis for dependency of sound velocity on the density of porous ribbons was developed and the application of active ultrasonics as a method to predict ribbon density with a reasonable degree of accuracy was demonstrated.