Department of Chemistry

Quaternary carbons are embedded in various natural products, pharmaceuticals, and organic materials. However, constructing this valuable motif is far from trivial. Conventional approaches mainly rely on classical polar disconnections and encounter bottlenecks concerning harsh conditions, functional group tolerance, regioselectivity, and step economy. In this context, Kawamata, Baran, Shenvi, and co-workers recently demonstrated that two feedstock chemicals, alkyl carboxylic acids and olefins, could be utilized to construct tetrasubstituted carbons in the presence of an inexpensive iron porphyrin catalyst and a suitable reductant combination through quaternization of the radical intermediates. The method enables access to various sterically encumbered quaternary carbons under mild and robust conditions. Taking a complete detour from conventional approaches, the present heteroselective radical–radical coupling simplifies the synthesis of quaternary carbon-containing molecules through an innovative and distinctive disconnection approach.

Ordered random vectors are frequently encountered in many problems. The generalized order statistics (GOSs) and sequential order statistics (SOSs) are two general models for ordered random vectors. However, these two models do not capture the dependency structures that may be present in the underlying random variables. In this paper, we study the developed sequential order statistics (D-SOSs) and developed generalized order statistics (D-GOSs) models that incorporate dependency structures among ordered random vectors. We then study various univariate and multivariate ordering properties of D-SOS and D-GOS models under Archimedean copula. We develop corresponding results for both one-sample and two-sample situations. We also present some simulational results and a real data analysis for illustrative purpose.

In recent times, diaryliodonium reagents (DAIRs) have witnessed a resurgence as arylating reagents, especially under photoinduced conditions. However, reactions proceeding through electron donor-acceptor (EDA) complex formation with DAIRs are restricted to electron-rich reacting partners serving as donors due to the well-known cage effect. We discovered a practical and high-yielding visible-light-induced EDA platform to generate aryl radicals from the corresponding DAIRs and use them to synthesize key chalcogenides. In this process, an array of DAIRs and dichalcogenides react in the presence of 1,4 diazabicyclo[2.2.2]octane (DABCO) as a cheap and readily available donor, furnishing a variety of di(hetero)aryl and aryl/alkyl chalcogenides in good yields. The method is scalable, features a broad scope with good yields, and operates under open-to-air conditions. The photoinduced chalcogenation technology is suitable for late-stage functionalizations and disulfide bioconjugations and facilitates access to biologically relevant thioesters, dithiocarbamates, sulfoximines, and sulfones. Moreover, the method applies to synthesizing diverse pharmaceuticals, such as vortioxetine, promazine, mequitazine, and dapsone, under amenable conditions.

New therapeutic leads are in global demand against multiple drug-resistant Staphylococcus aureus, as presently there is no drug of choice left to treat this pathogen. In the present work, we have designed, synthesized, and in vitro validated dimedone-coupled 2,3-dihydrofuran (DDHF)-based inhibitor scaffolds against Staphylococcal thioredoxin reductase (SaTR), a pivotal drug target enzyme of Gram-positive pathogens. Accordingly, a green multicomponent method that is both efficient and one pot has been optimized to synthesize DDHF derivatives. The synthesized DDHF derivatives were found to inhibit a purified SaTR enzyme. The best inhibitor derivative, DDHF20, inhibits SaTR as a competitive inhibitor for the NADPH binding site at low micromolar concentrations. DDHF20-capped silver nanoparticles are synthesized and characterized, and their bactericidal property has been checked in vitro. Furthermore, detailed in silico-based structure-guided functional studies have been carried out to uncover the plausible mode of action of DDHF20 as a potential anti-Staphylococcal therapeutic lead.

Detection and discrimination of volatile organic compounds (VOCs) is important to provide a more realistic assessment of their potential implication in complex environments and medical diagnostics based on volatile biomarkers. Herein, chemiresistive sensors are fabricated using stacked MoS2 nanoflakes with defects and exposed-edge sites. The sensor is found to be extremely selective to triethylamine (TEA) over polar, non-polar VOCs and atmospheric gases. The sensor exhibits a sensitivity of 1.72% ppm−1, fast response/recovery (19 s/39 s) to 100 ppm TEA at room temperature, low limit of detection (64 ppb), device reproducibility, humidity tolerance (RH 90%) and stability tested up to 60 days. The kinetic analysis of sensing curves reveals two discrete adsorption sites corresponding to edge and basal sites of interaction, with a higher rate constant of association and dissociation for TEA. The Density Functional Theory (DFT) studies support higher adsorption energy of TEA on MoS2 surface with respect to other volatile amines. The sensor demonstrates TEA recognition and composition estimation capability in a binary mixture of a similar class of VOCs using Machine Learning driven analysis with 95% accuracy. The ability to discriminate amines in binary mixture of other volatile amines paves the way for the advancement of next-generation devices in the field of disease diagnosis.

Reduction of aldehydes to alcohols is a fundamental organic transformation, typically achieved through metal-catalyzed reductions or by the use of hydride-based reagents. However, these conventional methods often go through harsh conditions with expensive catalysts and additional reductants, limiting their broader applications. In this study, for the first time we introduce an efficient, metal-free reduction strategy using triphenylphosphine (PPh3) and KOtBu in MeOH. This method exhibits broad functional group tolerance, mild environment and selectivity in reducing aldehydes even in the presence of other reactive functionalities (NO2, CN, ketone, etc.). Key features highlight this novel approach with practicality, scalability to gram scales and excellent yields for the reduction of varied aldehydes to alcohols (30 examples; 65–95 % Yields).

This work investigates a mathematical model of the propagation of lightwaves in bent optical waveguides. This modeling leads to a non-self-adjoint eigenvalue problem for differential operator defined on the unbounded domain. Through detailed analysis, a relationship between the real and imaginary parts of the complex-valued propagation constants was constructed. Using this relation, it is found that the real and imaginary parts of the propagation constants are bounded, meaning they are limited within certain region in the complex plane. The orthogonality of these bent modes is also proved. By the asymptotic analysis of these modes, it is proved that as r → ∞ the behavior of the eigenfunctions is proportional to 1 / r .

Random weighted (Formula presented.) -out-of- (Formula presented.) systems are very useful in modeling the lifetimes of systems, wherein the success or failure of a system depends not only on its current operational status, but also on the contributions made by its components. In this paper, we consider random weighted (Formula presented.) -out-of- (Formula presented.) systems with redundant components drawn randomly from a mixed population consisting of (Formula presented.) different subpopulations/substocks. We study different optimal allocation policies of active redundancies and minimal repair components in a random weighted (Formula presented.) -out-of- (Formula presented.) system. Moreover, we investigate how the heterogeneity of subpopulations of items impacts the lifetime of a random weighted (Formula presented.) -out-of- (Formula presented.) system. We also present some simulational results and a real data analysis for illustrative purpose.

The domain of correlated non-Markovian channels are investigated, exploring the potential memory arising from the correlated action of channels and the inherent memory due to non-Markovian dynamics. The impact of channel correlations is studied using different non-Markovianity indicators and measures. In addition, the dynamical aspects of correlated non-Markovian channels, including entanglement dynamics as well as changes in the volume of accessible states, are explored. The analysis is presented for both unital and non-unital correlated channels. A new correlated channel constructed with modified Ornstein–Uhlenbeck noise (OUN) is also presented and explored. Further, the geometrical effects of the non-Markovianity of the correlated non-Markovian channels are discussed with a study of change in the volume of the accessible states. The link between the correlation factor and error correction success probability is highlighted.

The Markovian arrival process (MAP) is a versatile counting process with dependent and non-identically distributed inter-arrival times following the phase-type distribution. In this article, we study the classical (Formula presented.) -shock model and a mixed (Formula presented.) -shock model by assuming the MAP of shocks. We derive explicit expressions for the reliability and the mean lifetime of the system. Further, we study an optimal replacement policy based on the MAP. We illustrate the developed results through several numerical examples.