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The isomers of C4H5N consisting of eleven members (2-vinyl-2H-azirene, Isocyanocyclopropane, Ally isocyanide, N-vinylethyleneimine, Cyanocyclopropane, 2H-pyrrole, 3H-pyrrole, Ally cyanide, 2-cyanopropene, 2-butenenitrile, Pyrrole) have been studied computationally using the Gaussian-4 (G4) compound model with the Gaussian 09 suite of programs. Quan...
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... optimized structural parameters such as bond distance and bond angles for pyrrole are presented in Table 4, while the those for the other isomers of the C4H5N isomeric group are presented in Table 5. The error difference between the calculated and experimental bond distance and bond angles data [23] for pyrrole reveal that there is consistency and therefore shows high accuracy in our findings. ...Context 2
... optimized structural parameters such as bond distance and bond angles for pyrrole are presented in Table 4, while the those for the other isomers of the C4H5N isomeric group are presented in Table 5. The error difference between the calculated and experimental bond distance and bond angles data [23] for pyrrole reveal that there is consistency and therefore shows high accuracy in our findings. ...Similar publications
The study unravels the dynamic interplay of energy, stability, and abundance, providing profound insights into the composition of the interstellar medium. This investigation promises to unveil the underlying principles shaping the celestial tapestry of astrochemistry. High-level quantum chemical calculations provide accurate enthalpies of formation...
Quantum chemical calculations were carried out to quantitatively understand the origin of the Felkin–Anh(–Eisenstein) model, widely used to rationalize the π-facial stereoselectivity in the nucleophilic addition reaction to carbonyl groups directly attached to a stereogenic center. To this end, the possible approaches of cyanide to both (S)-2-pheny...
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... Recent investigations challenge traditional notions, expanding the repertoire of potential proton acceptors (Ramphal, 2021;Ditler, & Luber 2022;Domingos et al., 2018). The evolving landscape prompts introspection into the essence of a hydrogen bond, with rare gases even considered as acceptors under specific conditions Etim et al., 2022). This research navigates through microwave spectroscopic and ab initio results, focusing on isoprene argon complexes formed in the interstellar medium (Chen et al., 2022;Kurdziel, 2004;Wickramasinghe, & Bauval 2017). ...
The study of various weakly bound complexes in the interstellar medium has posed significant challenges. Isoprene which is a fundamental organic molecule, and Argon which is a noble gas, form a complex that serves as an essential model system for examining molecular interactions in space. This research investigates the isoprene-Argon complex through the combination of Pulsed Nozzle Fourier Transform Microwave (PNFTMW) spectrometry and advanced computational methods. Utilizing a PNFTIR spectrometer, the study focuses on rotational transitions of both the Isoprene monomer and Isoprene-Argon complex, providing valuable insights into their vibrational and rotational characteristics. The discussion encompasses rotational constants (A, B, C) and dipole moment components (|μa|/D, |μb|/D, |μc|/D), shedding light on the complex's rotational behaviour and charge distribution along principal axes. The observed variations in rotational constants across diverse quantum chemical methods highlight the importance of method selection, while differences in dipole moments show the complex's sensitivity to computational nuances. The findings contribute to a better understanding of the Isoprene-Argon complex's molecular structure and its potential implications in interstellar chemistry. This multidisciplinary approach lays the groundwork for future investigations into molecular interactions and the refinement of computational models for improved predictive accuracy in describing complex molecular properties.
... In order to ascertain if other antifungal drugs had the same shortcomings, an examination of the interactions between the other antifungal drugs with Phosphodiesterase (PDE) proteins, known regulators of cyclic nucleotides involved in vasodilation had to be carried out [39]. By exploring how these drugs may affect PDE proteins, the study brought further insights into the understanding of their potential impact on the cyclic guanosine monophosphate (cGMP) signaling pathway, which plays a crucial role in erectile function. ...
Erectile dysfunction (ED) is a prevalent condition affecting a significant portion of the male population. This research delves into the potential link between Griseofulvin, a known antifungal medication, and its impact on erectile function. A comprehensive computational approach was employed. Optimization of griseofulvin was carried out using the highly reputable density functional theory (DFT) with the B3LYP functional and 6–31*G(d,p) using water and ethanol as the solvents of interest. We explored the interactions of Griseofulvin with Human Phosphodiesterase 5 proteins (PDE5), specifically targeting the crystal structures 1UDT and 1UDU. Molecular docking studies provided valuable insights into the binding mechanisms of Griseofulvin with PDE5, shedding light on potential allosteric modulation and conformational changes. Further molecular docking studies were carried out on other popular antifungal drugs like amphotericin, terbinafine and ketoconazole in order to compare their interactions with 1UDT and 1UDU with that of griseofulvin. Through an array of computational analyses, including molecular dynamics simulations and binding free energy calculations, we aimed to elucidate the propagating effects of Griseofulvin on the catalytic activity and structural stability of PDE5. The findings from this research could contribute to a deeper understanding of the molecular mechanisms underlying Griseofulvin's impact on erectile function, potentially opening avenues for the development of novel therapeutic interventions for ED.
... Examples include femtosecond transient absorption spectroscopy and 2D-IR spectroscopy. These methods shed light on the elapsed periods and routes for the production and rupture of hydrogen bonds as well as the interactions between hydrogen bonding and other molecular movements [65]. A thorough knowledge of temporal characteristics of intramolecular hydrogen bonding is possible because to ultrafast spectroscopy, which enables the investigation of hydrogen bond dynamics in real-time [66]. ...
Hydrogen bonds between and within molecules are essential interactions that control how molecules behave in various chemical and biological systems. To better understand the complex nature of hydrogen bonding events, spectroscopic and computational methodologies were integrated in this abstract. Direct probing of the vibrational and electronic fingerprints linked to hydrogen bonds has been made possible by spectroscopic techniques, such as infrared and nuclear magnetic resonance spectroscopy, providing crucial details regarding bond strength, length, and dynamics. Molecular dynamics simulations and advanced computational techniques like density functional theory (DFT) have simultaneously produced a theoretical foundation for comprehending the energetics and geometry of hydrogen bonds. A thorough understanding of hydrogen bonding interactions in a variety of settings, including biomolecular systems, liquids, and solids, has been made possible by the synergistic interaction between experimental data and theoretical discoveries. The combined efforts of spectroscopic and computational research have revealed the relevance of hydrogen bonds in molecular recognition, reaction processes, and material properties, even though difficulties still exist in adequately simulating solvent effects and long-range interactions. This multidisciplinary approach continues to lead to discoveries as technology develops, providing a deeper understanding of hydrogen bonding and its ramifications across other scientific disciplines.
Heavy metal pollution is a significant environmental concern due to its detrimental effects on ecosystems and human health. The conventional techniques for the eliminating heavy metals from water-based solutions, like chemical precipitation and ion exchange, have certain drawbacks in terms of effectiveness, expenses, and ecological impact. In recent years, researchers have explored the use of natural materials, including tea leaves and tea fibre, as alternative adsorbents for heavy metal removal. From research, tea leaves and tea fibre, derived from the camellia sinensis plant, have emerged as promising adsorbent due to their abundance, low cost, and potential for heavy metal adsorption from aqueous phase. This comprehensive review aims to summarize and evaluate the current state of knowledge regarding the use of tea leaves and tea fibre in the removal of heavy metals from aqueous solutions. It will discuss the adsorption mechanisms, influencing factors, and potential applications of these natural materials. The research will also covers a comparative analysis of this adsorbent with other adsorbents, isotherms, kinetics and equilibrium studies, as well as regeneration and reusability of tea-based adsorbents.
Cancer remains a significant challenge in healthcare, spurring ongoing exploration for effective therapies. Computational methods, emerging as invaluable tools in drug discovery, have garnered attention for their cost-effectiveness and efficiency. In this study, we investigate the anticancer potential of 1-Guanidinosuccinimide and Benzene-ethanamine, 2,5-difluoro-β, 3,4-trihydroxy-n-methyl, targeting Mouse double minute 2, a critical protein in cancer pathways. Quantum chemical calculations with GAUSSIAN 09 (B3LYP; 6-311(d,p)) explored molecular structures across various solvation environments (Dimethyl Sulfoxide (DMSO) , ethanol, and methanol). Docking analysis using AutoDock Vina revealed binding to 4ZFI, with affinities of -5.9 and -6.6 kcal/mol, indicating diverse interactions. In-silico pharmacokinetics and ADMET profiling underscored favorable drug-like properties. Compound 2 emerged as a promising therapeutic candidate, showing superior binding versatility and strength. Both compounds adhere to Lipinski's rule, suggesting their potential as viable drug candidates. Further research and experimental validation are advocated to realize their therapeutic potential and expedite drug development efforts.
Study’s Excerpt/Novelty This study provides a comprehensive computational analysis of the thermodynamic properties of isoelectronic diatomic interstellar molecular species containing oxygen and sulfur atoms, spanning a wide range of interstellar temperatures. By investigating entropy, free energy, heat capacity, and internal energy, the research reveals unique trends, such as the earlier responsiveness of sulfur-containing molecules to temperature changes and the significant impact of molecular size on thermodynamic properties. These insights into the isoelectronic effects and thermodynamic behaviors of specific molecules across extreme temperature ranges enhance our understanding of the role of oxygen and sulfur in complex interstellar molecular systems, offering a foundation for future astrophysical research. Full Abstract Interstellar molecular species, particularly isoelectronic diatomic molecules, exhibit distinct thermodynamic traits, setting them apart from other molecular species. This study investigates the thermodynamic properties of isoelectronic diatomic interstellar molecular species containing oxygen and sulfur atoms, employing computational methods to analyze entropy, free energy, heat capacity, and internal energy across a spectrum of interstellar temperatures. Graphical representations highlight intriguing trends, revealing Oxygen and sulfur-containing molecules' earlier responsiveness to temperature changes compared to oxygen counterparts. Notably, molecule size emerges as a key determinant, with larger mass molecules exhibiting higher entropy, free energy, and heat capacity. We showed the isoelectronic effect of these Sulfur and Oxygen containing molecular species (OH, SN, CO, CS, SiO, SiS, FeO, FeS, PO, PS O2, OS, ZnO, ZnS, TiO, TiS) on several interstellar molecules at temperatures ranging from to (i.e., from the coldest place in the universe to the mean temperature of the interstellar medium). These findings offer valuable insights into the thermodynamic behavior of interstellar molecular species, paving the way for future research on the role of oxygen and sulfur atoms in complex molecular systems.
Dipole-dipole interactions, the basic forces controlling molecular behaviour, have wide-ranging effects on chemistry, biology, and materials research. Understanding these interactions plays a key role in molecular recognition, self-assembly, and solvation, and is essential for deciphering complex molecular processes. While useful, conventional approaches of characterising dipole-dipole interactions have trouble capturing the subtleties of complicated intermolecular forces and complex systems. The article explores the creative use of machine learning approaches to characterise dipole-dipole interactions in response to these difficulties. Understanding the nuances of these relationships requires a revolutionary approach, which is provided by machine learning, a field built on data-driven insights and pattern identification. Data preparation, feature extraction, model training, and model validation are all covered as part of the exploration of machine learning's fundamental principles. These methods allow for the creation of prediction models that can calculate the intensities and energy of interactions, giving rise to a quantitative knowledge of the forces at work. Complex connections between chemical characteristics and dipole-dipole interactions are revealed by utilising the power of machine learning methods, such as regression and classification. Unsupervised learning, a defining feature of machine learning, reveals complicated molecular datasets subtle patterns. Additionally, the combination of quantum mechanics and machine learning offers a synergistic strategy. A link between data-driven insights and fundamental physics is created by incorporating quantum mechanical concepts into machine learning models, enhancing the accuracy and depth of characterization of dipole-dipole interactions. This study demonstrates the revolutionary potential of machine learning in the field of characterisation of dipole-dipole interaction.
In many sectors, corrosion is a major issue with serious economic and safety ramifications. To reduce material deterioration and maintain the durability of structures and equipment, efficient corrosion inhibitors must be developed. Computational electrochemistry techniques have become effective resources for comprehending corrosion mechanisms and creating new inhibitors in recent years. This article gives a general overview of the computational methods used in corrosion inhibition research, including Quantum Mechanics/Molecular Mechanics (QM/MM), Molecular Dynamics (MD), and Density Functional Theory (DFT) simulations. Despite the various benefits, computational electrochemistry methods have several drawbacks, such as the necessity for precise modeling of complicated systems and the high computer resource requirements. To close the gap between atomistic simulations and macroscopic behavior, future efforts should concentrate on increasing precision, broadening the scope of simulations, and using multi-scale modeling techniques. By shedding light on the mechanisms underlying corrosion and assisting in the creation of effective inhibitors, computational electrochemistry approaches have transformed the study of corrosion inhibition. With these methods, it is possible to find innovative corrosion inhibitors with improved performance and hasten the development of corrosion protection systems.
