Elucidating Structural Disorder in a Polymeric Layered Material: The Case of Sodium Poly(heptazine imide) Photocatalyst

TL;DR

This paper explores the complex structure of a photocatalyst material using advanced imaging and machine learning to better understand its disorder.

Contribution

A new structural model of NaPHI incorporating out-of-plane undulations is proposed using machine learning and multi-technique analysis.

Findings

NaPHI flakes exhibit bent morphologies and stacking disorder at the mesoscale.

A structural model with undulations of ∼0.5 Å amplitude and 2–3 nm wavelength matches experimental data.

The approach combines machine learning with imaging to analyze semi-crystalline materials.

Abstract

Structurally heterogeneous
materials present major challenges for
characterization due to their complex nanoscale order. Sodium poly­(heptazine
imide) (NaPHI), a layered carbon nitride photocatalyst, exemplifies
this complexity, with its precise structure remaining unresolved.
Here, we uncover new structural insights into NaPHI using energy-filtered
four-dimensional scanning transmission electron microscopy combined
with machine-learning-based diffraction image segmentation, supported
by transmission electron microscopy, atomic force microscopy, X-ray
diffraction, and Raman spectroscopy. At the mesoscale, NaPHI flakes
display bent morphologies, while nanodiffraction patterns reveal features
characteristic of stacking disorder. Based on these insights, we modeled
a NaPHI-layered structure incorporating out-of-plane undulations (waves)
with amplitudes of ∼0.5 Å and wavelengths of 2–3
nm. This model reproduces the observed line features in nanodiffraction
patterns and agrees with powder X-ray diffraction data, thereby bridging
local and bulk structural information. The introduced approach uses
data-driven machine learning to identify statistically significant
features, offering a robust framework for structural analysis of semi-crystalline
materials.

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