Online 3D Characterization of Micrometer-Sized Cuboidal Particles in Suspension

Area of Science:

• Particle characterization
• Materials science
• Fluid dynamics

Background:

• Accurate particle size and shape characterization is crucial for understanding particulate matter.
• Existing 1D and 2D methods are insufficient for non-spherical particles, necessitating 3D approaches.
• Reliable sizing techniques for micrometer-sized cuboidal particles are currently lacking.

Purpose of the Study:

• To develop and experimentally assess an online, in-flow multiprojection imaging tool coupled with machine learning for characterizing cuboidal particles.
• To establish a methodology for fabricating micrometer-sized, non-spherical analytical standards for validation.
• To investigate the simultaneous measurement of particle size, shape, and orientation in dilute particle-laden flows.

Main Methods:

• Fabrication of monodisperse micro-cuboidal analytical standards with user-defined size and shape using photolithography.
• Experimental validation of an online multiprojection imaging tool using the fabricated analytical standards.
• Application of machine learning algorithms to analyze multiprojection image data for particle characterization.

Main Results:

• Demonstrated the utility of the multiprojection imaging tool and machine learning for characterizing micrometer-sized cuboidal particles.
• Successfully produced custom-designed micro-cuboidal analytical standards for validating the imaging system.
• Showcased that particle sizing data can be leveraged to estimate particle orientation in flow.

Conclusions:

• The developed multiprojection imaging and machine learning approach provides a rapid and robust protocol for 3D particle characterization.
• This method enables simultaneous measurement of size and orientation, advancing the study of particle behavior in flows.
• The fabricated analytical standards serve as a critical resource for validating advanced particle characterization techniques.