Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning

Abstract

Biological morphogenesis has inspired many efficient strategies to diversify material structure and functionality using a fixed set of components. However, implementation of morphogenesis concepts to design soft nanomaterials is underexplored. Here, we study nanomorphogenesis in the form of the three-dimensional (3D) crumpling of polyamide membranes used for commercial molecular separation, through an unprecedented integration of electron tomography, reaction-diffusion theory, machine learning (ML), and liquid-phase atomic force microscopy. 3D tomograms show that the spatial arrangement of crumples scales with monomer concentrations in a form quantitatively consistent with a Turing instability. Membrane microenvironments quantified from the nanomorphologies of crumples are combined with the Spiegler-Kedem model to accurately predict methanol permeance. ML classifies vastly heterogeneous crumples into just four morphology groups, exhibiting distinct mechanical properties. Our work forges quantitative links between synthesis and performance in polymer thin films, which can be applicable to diverse soft nanomaterials.

Cosolvent-Assisted Interfacial Polymerization toward Regulating the Morphology and Performance of Polyamide Reverse Osmosis Membranes: Increased m-Phenylenediamine Solubility or Enhanced Interfacial Vaporization?

“… a The correlation coefficient R 2 , a common statistical parameter used for analyzing the linear dependence between a pair of variables, was obtained using Microsoft Excel software. This parameter has been commonly used for studying the membrane structure–property relationship in the literature. ,− b TEM-void fraction was calculated as the ratio between the area of the voids in the PA layer and the total area of the PA layer, where the areas were measured by the software Image pro plus based on the TEM micrographs. c TEM-apparent thickness was measured as the overall thickness of the crumpled nanovoid-containing PA layer using Image pro plus based on the TEM micrographs. d SEM-surface coverage was calculated as the ratio between the projected area of the exterior PA layer and the projected area of the total PA layer, where the areas were measured by Image pro plus based on the SEM micrographs. e AFM-RMS (root-mean-square roughness), AFM-average height, and AFM-surface area ratio were obtained by the AFM tests. …”

“… a The correlation coefficient R 2 , a common statistical parameter used for analyzing the linear dependence between a pair of variables, was obtained using Microsoft Excel software. This parameter has been commonly used for studying the membrane structure–property relationship in the literature. ,− …”

Molecular insights into the structure-property relationships of 3D printed polyamide reverse-osmosis membrane for desalination

“…Still, IP faces certain inherent limitations. Namely, the reaction is self-terminated so uncontrolled film growth can result in membranes with undesired thickness and rough surface morphology [13][14][15][16]. The properties of the support-layer surface can also lead to inconsistent polymerization behavior, with undesirable consequences to membrane performance [17,18].…”

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

“…Previous studies have shown that the crumples exhibit different effective moduli depending on the morphology groups they belong to. 72 Despite the variety of crumples observed in the membrane (Fig. 3a), they consistently fall into these four groups; crumples within each group show high quantitative similarity in their fingerprints (Fig.…”