INVESTIGATION OF INFRARED SPECTROSCOPY AND RAMAN SPECTROSCOPY FOR FUNCTIONAL GROUP IDENTIFICATION AND STRUCTURAL CONFIRMATION OF TRI-SUBSTITUTED BENZALDEHYDE
Dr. Karishma A. Nikose, Prof. Roshankumar G. Satsure, Prof. Kalyani Yashwant Kumbhare, Akshay Suresh Nimbhorkar, *Prof. Suraj P. Rajurkar, Prof. Suraj D. Thakare
ABSTRACT
The structural characterization of substituted aromatic aldehydes is of considerable importance in organic, pharmaceutical, and analytical chemistry because of their widespread application as synthetic intermediates and biologically relevant molecules. In the present study, a detailed vibrational spectroscopic analysis of 2-hydroxy-3,4-dimethoxybenzaldehyde was carried out using the complementary techniques of infrared (IR) and Raman spectroscopy. The selected compound contains multiple functional groups, including hydroxyl (–OH), aldehyde (–CHO), and methoxy (–OCH?) substituents, making it an ideal model for investigating substituent effects and vibrational interactions within an aromatic system. Infrared spectroscopy was utilized to identify characteristic absorption bands corresponding to carbonyl stretching, hydroxyl vibrations, methoxy C–O stretching, and fingerprint region features associated with aromatic substitution. Raman spectroscopy provided additional structural information through the detection of aromatic ring skeletal vibrations, ring-breathing modes, and other vibrational bands that were weak or inactive in the IR spectrum. The combined interpretation of IR and Raman spectra significantly improved the accuracy of vibrational band assignments and enabled a more comprehensive understanding of the molecular structure. The findings demonstrate that integrated IR–Raman spectroscopy is a reliable and efficient approach for the characterization of structurally complex tri-substituted benzaldehydes. The spectral assignments generated in this study may be valuable for organic synthesis verification, compound screening, and the development of spectral reference databases. Furthermore, the study emphasizes the future potential of combining experimental spectroscopy with computational modeling and machine learning tools for advanced structural analysis.
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