Authors : Dr. Surabhi Singhal

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/mvh844wh

DOI : https://doi.org/10.38124/ijisrt/26sep1300

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study organizes the recorded Fourier transform infrared and laser Raman bands of 4-amino-2chlorobenzonitrile into an experimental benchmark for computational vibrational analysis and interpretable machine learning. The available measurements comprise peak assignments obtained in potassium bromide, Nujol and Raman conditions rather than complete digitized intensity arrays. Accordingly, the work does not report invented densityfunctional-theory frequencies or classification accuracy. It instead quantifies agreement among the recorded channels, identifies chemically diagnostic regions and defines a reproducible pathway for later simulation and model validation. The nitrile stretching mode is observed at 2235 cm⁻¹ in KBr, 2237 cm⁻¹ in Nujol and 2232 cm⁻¹ in Raman spectra. Amino-related evidence includes associated N–H stretching at 3222–3280 cm⁻¹, C–NH₂ stretching at 1346 cm⁻¹ in KBr and 1336 cm⁻¹ in Raman data, and lower-frequency amino deformations. Chlorine-sensitive modes occur at 738 and 365 cm⁻¹ in the Raman spectrum. Nineteen matched KBr–Nujol modes yield a mean absolute difference of 10.79 cm⁻¹ and a root-mean-square difference of 17.04 cm⁻¹. Nine matched KBr–Raman modes yield corresponding values of 17.67 and 26.67 cm⁻¹. A proposed workflow combines scaled quantum-chemical frequencies, mode-displacement verification, peak-level baselines and dualbranch one-dimensional convolutional learning. The framework emphasizes grouped validation, external testing and chemical inspection of model attention. The resulting paper establishes what is experimentally supported, what can be calculated and what requires new spectral acquisition.

Keywords : 4-Amino-2-Chlorobenzonitrile; FTIR Spectroscopy; Laser Raman Spectroscopy; Vibrational Assignment; Density Functional Theory; Machine Learning; Multimodal Spectral Fusion.

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