• Meragawi, Sally El
• Mirshekarloo, Meysam Sharifzadeh
• Majumder, Mainak
• Abedin, Md. Joynul
• Chen, Wanqing

Abstract

<jats:title>Abstract</jats:title><jats:p>Continuous flow manufacturing is an innovative technology mainly applied in the chemical and pharmaceutical industries that is progressively being adapted to the manufacturing of nanomaterials to overcome the challenge of reproducing a product with consistent characteristics at a large scale. Here, a flow photochemical system is designed and prototyped for the synthesis of holey graphene oxides (hGOs). Compared to existing methods for the synthesis of hGO, the process is fast, highly scalable, and controllable. Through a combination of rigorous data analysis using machine learning algorithms on transmission electron microscope images and systematic studies of process parameters, it is demonstrated that characteristics of the produced hGO (i.e., porosity and pore size) are remarkably reproducible to the extent that it can be predicted by empirical models of processing‐property correlations. Depending on the tailored nanopore structures, the synthesized hGOs out‐performed GO in a range of applications that can benefit from the nanoporous two‐dimensional (2D) sheets such as in supercapacitors, gas adsorption, and nanofiltration membranes. These results are significant in offering new perspectives on the low‐cost industrialization of 2D nanomaterials.</jats:p>

Topics

• impedance spectroscopy
• pore
• porosity
• machine learning