Journal of Chemical Sciences最新文献

The transport of water at the nanoscale is fundamental to advancements in nanofluidics, membrane technology, and biological systems. While numerous studies have investigated the one-dimensional diffusion of water in carbon nanotubes (CNTs), its viscosity remains underreported. Experimental measurements of viscosity at this scale are challenging, but molecular dynamics simulations offer a viable alternative for predicting the viscosity of cylindrically confined water. Various methods have been employed for this purpose; however, their limitations raise questions about the accuracy of the predicted values. Our group has developed a novel approach, the Jump-corrected confined Stokes–Einstein (JCSE) method, based on the confined Stokes–Einstein equation, to estimate the viscosity of water within cylindrical nanopores. This technique is particularly promising because it accounts for the breakdown of the Stokes–Einstein relation in both confined and supercooled water, enhancing the reliability of viscosity predictions. In this short perspective, we introduce the JC-CSE method and its application to supercooled water confined in hydrophobic and superhydrophobic CNTs. Additionally, we rationalize viscosity trends using hydrogen-bond analysis. Finally, we provide a brief outlook on the broader applicability of this method to other confined liquids.

Graphical abstract

This figure illustrates approaches to determining the viscosity under confinement. Traditional Green–Kubo relations are often inadequate, while the confined Stokes–Einstein relation offers alternatives. The Jump-corrected Confined Stokes–Einstein (JCSE) equation introduces corrections for molecular jumps and confinement effects, relating viscosity to diffusion, temperature, and confinement length scale, improving accuracy in nanoscale systems.

In this work, an extensive theoretical investigation on the stability and reactivity of different sizes of molybdenum carbide clusters was performed. For this investigation, the linear combination of Gaussian-type orbitals auxiliary density functional theory (LCGTO-ADFT) approach has been employed. The reactivity study was carried out considering the first benzene hydrogenation reaction on these clusters. The catalytic behavior in the studied reactions was analyzed with respect to the cluster sizes. For this study, molybdenum carbide clusters with the same stoichiometry (Mo:C=2:1), varying from systems containing a few atoms, up to systems characterized by a nanometric size have been considered. The lowest energy structures of the considered nanometric sized clusters are presented for the first time. Results of the obtained minima and transition state structures, harmonic frequencies, HOMO-LUMO gap and activation energy are presented. The relationship between the calculated activation energy and the atomic arrangements on the surface of the clusters, as well as the trend of the cluster size with respect to their metallic behavior are analyzed. This work shows that the catalytic behavior of these systems is strongly related to their size and HOMO-LUMO gap.

Graphical abstract

We use the elongated hydrogen chain as an illustration to affirmatively answer the question, “Can the FCI energies/properties be predicted with Hartree–Fock (HF) or density function theory (DFT) densities?” as proposed by the late Bob Parr. We employ some simple physics-inspired density-based quantities from the information-theoretic approach (ITA) to linearly correlate with the full configuration interaction (FCI) energies with the approximate DMRG (density matrix renormalization group) method as a solver. We have showcased that the deviations between the calculated and predicted ground-state energies are only about a few milliHartree. Moreover, with the “gold standard” CCSD(T) (coupled cluster with singles and doubles, and perturbative triples) polarizabilities as a reference, we have found a way to systematically reduce the severe overestimation of the molecular polarizabilities as calculated by the approximate DFT functionals. Accordingly, we have applied this strategy to simultaneously predict the energies and molecular properties of strongly correlated systems only with the ground-state electron densities as an input, which should be a good starting point for more complex systems.

Graphical abstract

In a remarkable leap forward for synthetic chemistry, MacMillan and co-workers have unveiled a clever way to directly forge C(sp²)–H alkyl bonds in arenes using light and copper catalysis. At the heart of their discovery lies the inventive idea of dynamic orbital selection, a concept that allows the catalyst to distinguish between aryl and alkyl radicals generated in situ. This precise control enables the selective union of these two distinct radical species, something long considered a major challenge. What makes the method especially appealing is its practicality: simple alcohols and carboxylic acids serve as alkyl radical sources, making the reaction broadly applicable and ideal for the late-stage functionalization of complex molecules. By elegantly sidestepping the harsh conditions and poor selectivity of classical Friedel–Crafts alkylations, this strategy opens new horizons for sustainable C–H functionalization, paving the way for innovations in drug discovery and molecular design.

Graphical abstract

Protein–protein interactions (PPIs) play a compelling role in biological systems. Additionally, they are promising therapeutic targets for various diseases. However, the design of small molecule inhibitors for PPIs is challenging due to the dynamic and featureless nature of the PPI interfaces, which often lack well-defined binding pockets. To address this challenge, the PPIscout approach employs mixed solvent molecular dynamics (MD) simulations to identify binding hotspots at PPI interfaces. This technique utilizes mixed amino acid–water MD simulations with capped amino acids as probe molecules. This method has been applied to three target proteins: MDM2, Bcl-XL and PDZ6. The mixed solvent simulations revealed that the probe molecules predominantly accumulated at the PPI interfaces, highlighting potential hotspots. These findings are corroborated with results from FTMap, a computational solvent mapping tool. Results demonstrate that mixed amino acid–water MD simulations effectively identify PPI hotspots and favorable interactions that are crucial for drug design. Moreover, the method also unveils potential allosteric sites that could be targeted for the development of peptides or small molecule drugs.

Graphical abstract

PPIscout employs mixed-solvent molecular dynamics (MD) simulations, using amino acid probes, to identify binding hotspots and potential allosteric sites on dynamic protein-protein interaction (PPI) interfaces. This computational method reveals crucial interaction sites, aiding in the design of peptide and small molecule inhibitors.

Microwave-assisted efficient synthesis of nanosized manganese oxide (Mn₂O₃) is reported using glycerol and manganese acetate. Glycerol in this reported methodology acts as an efficient solvent for this nanomaterial synthesis under microwave radiation. This reported method enables the synthesis of nanosized Mn₂O₃ without further use of additives, stabilizers, and bases other than glycerol and manganese precursor. Glycerol is a renewable biomaterial derived nonvolatile and nontoxic, safe solvent. It has a high boiling point and dielectric constant, which makes it an ideal solvent for microwave synthesis. The synthesized nanosized Mn₂O₃ was analyzed by X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. We examined the catalytic properties of as-synthesized Mn₂O₃ for cinnamyl alcohol oxidation to cinnamaldehyde. In this oxidation activity experiment, nanosized Mn₂O₃ exhibits good conversion and selectivity towards the desired product. We also made an attempt to understand the probable mechanism of nanomaterial formation and found some conclusive evidence to support it. This research methodology is facile, energy efficient, and involves minimum chemicals for synthesis. It makes this reported methodology not only economically attractive but also environmentally friendly, which aligns well with green chemistry principles.

Graphical abstract

The graphical abstract illustrates the synthesis of Mn₂O₃ using glycerol and manganese acetate. The process involves microwave heating, centrifugation, ethanol–DW washing, drying, and calcination. Each step—from reagent mixing to final product isolation—is visually mapped, highlighting the equipment and conditions used to obtain Mn O for catalytic applications.

First-principle-based quantum chemical methods are employed to characterise the transition metals chalcogen–chalcogen bonds of the types M(HCh)₂²⁺ and M(ChCH₃)₂²⁺ (Ch = S, Se) in the gas phase. Use of such model systems allow us to calculate the binding energy values considering a very large basis set along with density functional theory (DFT) based methods and compare these values with corresponding values at MP2, CCSD, and CCSD(T) methods. In this work, DFT and post-Hartree–Fock wave function methods including MP2, CCSD, and CCSD(T) are used to calculate the binding energy using the 6-311++G(2d,2p) basis set. Error analysis performed for BLYP, SVWN, TPSS, M05, MPW1PW91, B3LYP, B3LYP-D, B3LYP-D3, M06-HF, MO8-HX, MN-15, LC-ωHPBE, ωB97XD, MP2, and CCSD methods using the CCSD(T) results as the reference. M06-HF, MN-15, LC-ωHPBE, MO8-HX, MN-15, and MPW1PW91 outperformed other DFT functionals. MP2, in case of small molecules like M(HCh)₂²⁺ and M(ChCH₃)₂², reflects poor performance because of its inability to capture significant electron correlation effects inherent to transition–metal complexes compared to CCSD and CCSD(T) methods. The results offer valuable insights into metal–ligand interactions, guiding future studies in catalyst optimization and electronic structure modelling.

Graphical abstract

This study employs DFT and post-Hartree–Fock wave functional methods (MP2, CCSD, CCSD(T)) to characterize M(HCh)₂²⁺ and M(ChCH₃)₂²⁺ (M = Mn²⁺, Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, and Zn²⁺; Ch = S, & Se) complexes in the gas phase. Error analysis identifies M06-HF, MN-15, LC-ωHPBE, and MO8-HX as the best-performing functionals. The findings provide valuable insights into metal-ligand interactions that may help in catalyst design and electronic structure modelling for transition-metal systems.

Biomass-based aerogel materials are attracting considerable attention in the catalytic industry because of their properties of high porosity, Light weight, and large surface area formed by the interconnected 3D networks. However, the functionalities of most aerogel catalysts are unsatisfactory because of their complicated fabrication process, low catalytic activities, especially low selectivity. Herein, the functionalized alginate-based aerogels were prepared by introducing tetrasodium iminodisuccinate on sodium alginate to increase the number of carboxyl groups on polymer mainchains, aiming to provide more positions for crosslinking Cu²⁺. Furthermore, MOF-5 grew in situ at the Cu-crosslinked positions to synthesize SI-MOF aerogels. Compared with the Cu-SA aerogel, the crosslinking amount of Cu²⁺ in SI-MOF was increased by 8.21% and the compressive strength was improved by 233%. In the phenol hydroxylation catalyzed by SI-MOF, the conversion of phenol reached 70.29% and the selectivity of catechol was notably improved by 91.77% with a ratio of catechol to hydroquinone being 11:1. The fabricated aerogel catalyst provides a new strategy for the catalytic industry.

Graphical abstract

In this work, sodium alginate (SA) was amidated by introducing tetrasodium iminodisuccinate (IDS) and cross-linked with copper ions to form SI aerogel, and then SI-MOF aerogel was prepared by in-situ growth of metal organic framework (MOF), which was used to catalyze phenol hydroxylation to obtain about 92% high pyrocatechol selectivity.