Physical Chemistry Chemical Physics最新文献

An orientation-dependent regulatory mechanism whereby gold (Au) clusters influence the excited-state intramolecular proton transfer (ESIPT) process in biomolecules is revealed for the first time in this study. Using alanine (ALA) as a model system with multiscale theoretical calculations, we reveal that the orientation of Au clusters can precisely modulate both the reaction pathway and thermodynamic feasibility of proton transfer. Theoretical screening of clusters with different numbers of Au atoms and excited-state dynamics studies indicate that pristine ALA hardly undergoes spontaneous proton transfer in the excited state; however, upon sulfurization and bonding with clusters, ESIPT is activated. IR and Raman vibrational analyses demonstrate that the alanine-Au₅ cluster (ALA-Au₅) exhibits the most significant intramolecular hydrogen bond regulatory effect and achieves an optimal balance between capturing orientation-specific vibrational features and maintaining computational efficiency. A machine learning-assisted conformational search identified stable configurations, which lay a foundation for subsequent calculations. Electronic structure and energy analyses confirm that Au cluster binding to the acceptor blocks ESIPT while binding to the donor boosts it, elucidating how the spatial orientation of metal clusters modulates electronic structure and excited-state dynamics and providing a theoretical foundation for the rational design of metal-bio hybrid photofunctional systems.

Bio-based surfactants are a great alternative to conventional surfactants owing to their low toxicity and minimal environmental impact. Herein, four new 3-quinuclidinol-based surfactants, namely, [C₁₆Qui][Br], [C₁₂Qui][Br], [C₁₂AQui][Br] and [C₈Qui][Br], have been synthesized and characterized for the first time. The thermal stability of all the surfactants at higher than 250 °C makes them suitable for high-temperature applications. The self-assembly behavior of these surfactants is thoroughly characterized using various techniques. The interactions between relatively hydrophobic bulky head groups, along with other hydrophobic interactions, drive micelle formation. Small-angle neutron scattering (SANS) measurements provide insights into the micellar structures of all surfactants: [C₁₆Qui][Br] assembles into spherical micelles, [C₁₂Qui][Br] and [C₁₂AQui][Br] form prolate ellipsoidal structures, and [C₈Qui][Br] arranges into loosely packed, random polymer-like micelles. The micellar structure of short-chain surfactants is solely driven by interactions between head groups, while more compact micellar structures are observed for long-chain surfactants. The presence of an amide functionality near the quinuclidinolium head group further governs the characteristics and properties of the micelles. The interlocking of head groups in the self-assembled structure and distinct structural arrangement of surfactant ions are studied through ¹H-NMR spectroscopy and 2D ¹H-¹H correlation and nuclear Overhauser effect spectroscopies (COSY and NOESY). The aqueous solutions of these surfactants are employed as a catalytic reactor to enhance the enzymatic activity of cytochrome c. The synergistic effect of hydrophobic and electrostatic interactions enhances the enzymatic activity in both monomeric and micellar forms. Interestingly, these surfactants exhibit good microbial inhibition depending upon their structural features and net charge. The present study provides additional understanding of the synthesis of bio-based surfactants and their utilization in diverse biocatalytic and medical applications.

In this work, we present a multiscale computational investigation of the fragmentation chemistry of 3,4-dihydro-2H-pyran (DHP) by combining complementary quantum chemical calculations, ab initio molecular dynamics, potential energy surface analysis, QCxMS simulations, and machine-learning methods. First, the intrinsic fragmentation pathways of neutral DHP are investigated. Electronic structure calculations characterize the molecular properties, while molecular dynamics simulations reveal the dominant fragmentation channels and their dependence on internal excitation energy. The underlying reaction mechanisms are further elucidated by automated exploration of the potential energy surface identifying key intermediates, transition states, and activation barriers. In the second part, electron-ionization (EI) mass spectra are predicted using QCxMS and three machine-learning models (NEIMS, RASSP, and a developed by us HYBRID GNN-ResNet architecture) and compared with available experimental data. The results highlight the complementary roles of physics-based and data-driven approaches in describing different aspects of DHP fragmentation. The neutral-state molecular dynamics and potential-energy-surface calculations provide mechanistic insight into intrinsic dissociation pathways, whereas QCxMS and machine-learning models enable direct comparison with the experimental EI mass spectrum. Together, these approaches provide a multiscale description of DHP fragmentation while also defining the limits of correspondence between neutral-state dynamics and electron-impact fragmentation.

Density-based atomic partitioning methods provide atomic electron densities that enable the consistent derivation of atomic properties used to model non-bonded interactions, but their atomic charges often fail to accurately reproduce molecular electrostatics. We develop dipole-constrained additive variational Hirshfeld (d-AVH), a partitioning method that exactly reproduces the reference molecular dipole using atomic charges alone. The method extends the AVH framework by introducing dipole constraints into the variational optimization of promolecular densities, which are represented as linear combinations of neutral and charged proatom densities. Two variants are studied: the computationally efficient minimal-basis d-AVH-M and the more flexible bound-proatom d-AVH-B. Across a diverse dataset of more than 760 compounds, d-AVH systematically improves the accuracy of point-charge approximations to the electrostatic potential (ESP) relative to AVH while preserving the conformational stability and interpretability of Hirshfeld-based atomic densities. For several chemically distinct subsets, d-AVH matches the accuracy of ESP-fitted charges and surpasses Charge Model 5. These results establish d-AVH as a broadly applicable density-based framework for constructing electrostatically faithful atomic charges.

As oxygenated biofuels gain prominence in sustainable energy strategies, understanding their reaction kinetics with hydroxyl (˙OH) radicals is essential for predicting combustion behavior. In this work, all six hydrogen-atom abstraction (HAA) pathways in the solketal (2,2-dimethyl-1,3-dioxolane-4-methanol) + ˙OH reaction were characterized using high-level ab initio calculations and variational transition state theory (VTST). Molecular geometries and vibrational frequencies were obtained at the M06-2X/6-311++G(d,p) level, with single-point energies refined at the QCISD(T)/CBS level. Low-frequency torsional modes were treated using one-dimensional hindered rotor scans. All abstraction channels proceed through low or partially submerged transition states (-3.1 to 3.2 kcal mol^-1), necessitating a variational treatment. VTST calculations with asymmetric Eckart tunneling corrections were performed over 300-2000 K. At 298 K, the α (hydroxymethyl C-H) and r₁ (ring methine C-H) channels dominate the reactivity, yielding a total rate coefficient of k_total (298 K) ≈ 1.4 × 10¹³ cm³ mol^-1 s^-1. With increasing temperature, the ring-centered channels (r₁ and r₂) become dominant, while O-H abstraction becomes increasingly competitive, reaching ∼24% of the total flux at 2000 K. A systematic sensitivity analysis shows that the β₁, β₂, and O-H channels are strongly influenced by the treatment of low-frequency vibrational modes, leading to variations in the predicted rate coefficients of up to several orders of magnitude. In contrast, the α, r₁, and r₂ channels are comparatively robust. Temperature-dependent thermochemical properties (Δ_fH°, S°, ) were computed and fitted to NASA polynomials, and modified Arrhenius parameters were derived for all channels. The resulting dataset provides a consistent foundation for incorporation into detailed combustion models and highlights the importance of entropic and variational effects in hydrogen abstraction from multifunctional oxygenates.

The development of active and stable CO oxidation catalysts is critical for PEMFCs, as they purify hydrogen fuel and mitigate cathode poisoning, determining cell efficiency and operational lifetime. In this work, we present a rational design strategy for such catalysts via first-principles calculations. We constructed well-defined Fe₁(OH)₃/M₁Pt interfaces by anchoring Fe₁(OH)₃ clusters onto transition-metal-doped Pt surfaces. A multi-step screening strategy was implemented to screen sixteen transition metals, identifying Cu, Zn, Ag, and Au as optimal dopants that fine-tune *CO adsorption strength into a favorable range. These catalysts exhibit moderate *CO adsorption energies and significantly reduced Gibbs free-energy barriers for the rate-determining step compared to the Fe₁(OH)₃/Pt reference. The activity originates from a balance between moderate *CO adsorption and hydrogen-bond stabilization of *COOH by the Fe₁(OH)₃ site. Alloying the Pt support with Cu, Zn, Ag, or Au leads to more exothermic *COOH formation and enhances the reaction kinetics by lowering the kinetic barriers. Electronic analysis reveals that the cohesive energy of the dopant metal and the number of unpaired d-electrons serve as key descriptors for *CO adsorption strength. This work provides a comprehensive design strategy and fundamental insights for developing highly active and stable single-site catalysts for CO oxidation in PEMFCs.