Raman spectroscopy along with Principal Component Analysis for the confirmation of Silver(I)-N-heterocyclic carbene complex formation

Characterization of three different benzimidazolium ligands and their organo-selenium complexes by using density functional theory and Raman spectroscopy

In the present study, Raman spectroscopy (RS) along with density functional theory (DFT) calculations have been performed for the successful characterization and confirmation of the formation of three different selenium-based N-heterocyclic carbene (NHC) complexes from their respective salts. For this purpose, mean RS features and DFT calculations of different ligands and their respective selenium NHC complexes are compared. The identified characteristic RS and DFT features, of each of these ligands and their selenium complexes, show that the polarizability of benzimidazolium rings increases after complex formation with selenium. This has been shown by the enhanced intensity of the associated Raman peaks, therefore, confirming the formation of newly formed bonds. The complex formation is also confirmed by the identification of several new peaks in the spectra of complexes and these Raman bands were absent in the spectra of the ligands. Moreover, Raman spectral data sets are analyzed using a multivariate data analysis technique of Principal Component Analysis (PCA) to observe the efficiency of the RS analysis. The results presented in this study have proved the RS technique, along with DFT, an undoubtedly fast approach for the confirmation of synthesis of selenium based NHC-complexes.

Synthesis, Characterization, and Evaluation of Antifungal Activity of 1-Butyl-3-hexyl-1H-imidazol-2(3H)-selenone by Surface-Enhanced Raman Spectroscopy

In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Raman spectroscopic characterization of selenium N-heterocyclic carbene compounds

In this study, Raman spectroscopy is employed to analyze and characterize two salts (N-heterocyclic carbene) and their respective selenium N-heterocyclic carbene compounds. The features observed as differences among Raman spectral data of two different N-heterocyclic carbene salts are called Salt-I and Salt-II and their respective Se compounds, called Compound-I & Compound-II, are used to confirm the formation covalent bond between Se atom carbon atom of carbene. Enhancement in peak intensities and shifting of peak positions is directly related with compound formation. Raman spectral data provide a detail information about bond formation, chemical and structural differences between salts and compounds. The observed Raman spectral features of both salts and compounds are in consistent with computationally calculated Raman spectral features. Raman spectral features of each salt and its respective compound was further analyzed with principal component analysis, which was found helpful for differentiating each salt from its respective compound.

Identification and visualisation of microplastics via PCA to decode Raman spectrum matrix towards imaging

To visualise microplastics and nanoplastics via Raman imaging, we need to scan the sample surface over a pixel array to collect Raman spectra as a matrix. The challenge is how to decode this spectrum matrix to map accurate and meaningful Raman images. This study compares two decoding approaches. The first approach is used when the sample contains several known types of microplastics whose standard spectra are available. We can map the Raman intensity at selected characteristic peaks as images. In order to increase the image certainty, we employ a logic-based algorithm to merge several images that are simultaneously mapped at several characteristic peaks to one image. However, the rest of the signals other than the selected peaks are ignored, meaning a low signal-noise ratio. The second approach for decoding is used when samples are complicated and standard spectra are not available. We employ principal component analysis (PCA) to decode the spectrum matrix. By selecting principal components (PC) and generating PC score curves to mimic the Raman spectrum, we can justify and assign the suspected items to microplastics and other materials. By mapping the PC loadings as images, microplastics and other materials can be simultaneously visualised. We analyse a sample containing two known microplastics to validate the effectiveness of the PCA-based algorithm. We then apply this method to analyse "unknown" microplastics printed on paper to extract Raman spectra from the complicated background and individually assign the images to paper fabric/additive, black carbon and microplastics, etc. Overall, the PCA-based algorithm shows some advantages and suggests a further step to decode Raman spectrum matrices towards machine learning.

Synthesis of sandwich type acyclic tetra-nuclear silver(I)-N-heterocyclic carbene complexes for wound healing applications

Two meta-xylyl linked tetrakis-benzimidazolium salts (L1-L2) as multidentate ligands and two respective silver complexes (C1 and C2) were synthesized. A multistep reaction was done at room temperature, starting with simple benzimidazole and alkyl halides, going through precursors and salt formation by reflux and finally in situ deprotonation of tetrabenzimidazolium salts with Ag2O to yield respective tetra-nuclear Ag(I)-N-heterocyclic Carbene (NHC) complexes. Propyl and butyl groups were bonded at the terminal positions of tetra-azolium open chain salts. Characterization of compounds was done by analytical and spectroscopic techniques. On the basis of spectroscopic data, a chemical structure with open chains having four Ag(I) ions sandwiched between NHC layers was established. Potential of synthesized complexes (C1 & C2) for wound contraction was evaluated and compared with standard wound contraction gel. Percentage wound contraction of both complexes was found very close to that of standard drug used in parallel.

Organotellurium compounds: an overview of synthetic methodologies

In wake of emerging applications of organotellurium compounds in biological and material science avenues, the current review describes their key synthetic methodologies while focusing the synthesis of organotellurium compounds through five ligand-to-metal linkages including carbon; carbon-oxygen; carbon-nitrogen; carbon-metal; carbon-sulfur to tellurium. In all of these linkages whether tellurium links with ligands through a complicated or simple pathways, it is often governed through electrophilic substitution reactions. The present study encompasses these major synthetic routes so as to acquire comprehensive understanding of synthetic organotellurium compounds.