Ab-Initio Spectroscopic Characterization of Melem-Based Graphitic Carbon Nitride Polymorphs

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• Polymeric graphitic carbon nitride (gCN) exhibits unique structures with reactive sites for enhanced photoactivated electrocatalysis.
• Diverse gCN polymorphs exist, each with distinct structural and electronic properties influencing catalytic performance.
• Experimental identification of specific gCN polymorphs is challenging due to a lack of reference spectroscopic data.

Purpose of the Study:

• To computationally characterize various melem-based gCN models with different polymerization degrees and arrangements.
• To identify unique spectroscopic fingerprints (XRD, XPS, NEXAFS) for each gCN model.
• To correlate these fingerprints with specific structural and electronic properties for unambiguous material identification.

Main Methods:

• Optimization of several melem-based gCN models using density functional theory (DFT).
• Computational simulation of X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectra.
• Comparison of theoretical predictions with available experimental data.

Main Results:

• Detailed computational characterization of simulated spectroscopic properties for different gCN polymorphs.
• Identification of unique spectral fingerprints distinguishing each gCN model.
• Correlation established between spectral features and the underlying structural/electronic properties of gCN.

Conclusions:

• The study provides a computational framework for identifying gCN polymorphs via their unique spectroscopic signatures.
• This work facilitates accurate material characterization, crucial for rationalizing and optimizing gCN-based catalysts.
• The findings aid in selecting and synthesizing specific gCN structures for targeted electrocatalytic applications.