Understanding the structural intricacies in carbon nitride materials through multimodal characterization: a critical...

Polymeric carbon nitrides (CNs) have emerged as a chemically versatile and synthetically accessible class of semiconductors, capable of diverse functionalities ranging from solar fuel generation to environmental remediation. Yet, despite intense global research activity since the landmark report of visible-light photocatalysis by graphitic CN (g-C₃N₄) in 2009, their translation into application-ready, high-performance systems remains disproportionately slow. This stagnation stems less from intrinsic material limits than from an incomplete, and often superficial, understanding of their structural intricacies-an outcome of over-reliance on a narrow set of characterization tools applied in isolation. CNs defy the crystalline simplicity of conventional semiconductors, encompassing a continuum of morphologies, defect landscapes, and degrees of polymerization that evolve dynamically with synthesis and post-treatment. Resolving this structural heterogeneity requires an integrated, multimodal characterization philosophy that couples complementary methods to interrogate chemistry, morphology, and electronic structure across multiple length scales. This topical review critically examines both widely adopted and underutilized techniques for CN characterization, from XPS, FT-IR, and PXRD to advanced methods such as solid-state NMR, XANES/EXAFS, UPS, TAS, and real-space probes like pair distribution function analysis. Special emphasis is placed on their synergistic deployment to resolve ambiguities that single-technique approaches leave unanswered-validating surface-sensitive observations with bulk probes, correlating structural motifs with electronic shifts, and mapping dynamic changes under operational conditions. Case studies illustrate how such convergence has elucidated structural motifs, defect states, and active-site architectures in ways not possible otherwise. By reframing characterization from a confirmatory step to a generative driver of discovery, this review outlines a blueprint for structurally informed CN design. The aim is to empower predictive, reproducible material development, where performance optimization is grounded in a rigorous, multidimensional understanding of structure rather than in empirical iteration alone.