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

• Published in: Small methods 2023-01, Vol.7 (1), p.e2201018-n/a
• Main Authors: Binel, Pietro, Jain, Ankit, Jaeggi, Anna, Biri, Daniel, Rajagopalan, Ashwin Kumar, deMello, Andrew J., Mazzotti, Marco
• Format: Article
• Language: English
• Subjects: 3D characterization; 3D imaging; analytical standards; Imaging, Three-Dimensional; particle orientation; Particle Size; Particulate Matter; photolithography
• ISSN: 2366-9608; 2366-9608
• DOI: 10.1002/smtd.202201018

Characterization of particle size and shape is central to the study of particulate matter in its broadest sense. Whilst 1D characterization defines the state of the art, the development of 2D and 3D characterization methods has attracted increasing attention, due to a common need to measure particle shape alongside size. Herein, ensembles of micrometer‐sized cuboidal particles are studied, for which reliable sizing techniques are currently missing. Such particles must be characterized using three orthogonal dimensions to completely describe their size and shape. To this end, the utility of an online and in‐flow multiprojection imaging tool coupled with machine learning is experimentally assessed. Central to this activity, a methodology is outlined to produce micrometer‐sized, non‐spherical analytical standards. Such analytical standards are fabricated using photolithography, and consist of monodisperse micro‐cuboidal particles of user‐defined size and shape. The aforementioned activities are addressed through an experimental framework that fabricates analytical standards and subsequently uses them to validate the performance of our multiprojection imaging tool. Significantly, it is shown that the same set of data collected for particle sizing can also be used to estimate particle orientation in flow, thus defining a rapid and robust protocol to investigate the behavior of dilute particle‐laden flows. Cuboidal particles in the micrometer size range are often observed during and after the synthesis of organic, inorganic, and metal‐organic substances. To accurately characterize particle size and shape, three characteristic lengths (length, width, and thickness) must be quantified. Herein, the utility and accuracy of an online, in flow, non‐invasive measurement technique based on multiprojection imaging and machine learning is illustrated.