The Journal of Physical Chemistry Letters最新文献

Quadruple-switchable nanoscale assemblies are built by combining two types of water-soluble molecular photoswitches through dipole–dipole interaction. Uniting the wavelength-specific proton dissociation of a photoacid and ring-opening of an anionic spirooxazine results in an assembly that can be addressed by irradiation with two different wavelengths: pH and darkness.

Encapsulated atomic hydrogen in cube-shaped octasilsesquioxane (POSS) cages of the Si₈O₁₂R₈ type (where R is an organic group) is one of the simplest alternative stable systems to paramagnetic endofullerenes that have been regarded as key elements of spin-based quantum technologies. Apart from common sources of decoherence such as nuclear spin and spectral diffusion, all H@POSS species studied so far suffer from additional shortening of T₂ at low temperatures due to methyl group rotations. Here we eliminate this factor for the first time by studying the smallest methyl-free derivative with R = H, namely, H@T₈H₈. By applying dynamical decoupling methods, we measure electron spin coherence times T₂ up to 280 ± 76 μs at T = 90 K and observe a linear dependence of the decoherence rate 1/T₂ on trapped hydrogen concentrations, which we attribute to the spin dephasing mechanism of instantaneous diffusion and a nonuniform spatial distribution of encapsulated H atoms.

Doping of zinc oxide (ZnO) with manganese (Mn) tunes midbandgap states of ZnO to enhance its optical properties and makes it into an efficient photoactive material for photoelectrochemical water splitting, waste removal from water, and other applications. We demonstrate that ZnO modified with 1 at. % Mn exhibits the best performance, as rationalized by experimental, structural, and optical characterization and theoretical analysis. ZnO doped with the optimal Mn content possesses improved light absorption in the visible region and minimizes charge carrier recombination. The doping is substitutional and creates midgap states near the valence band. Mn atoms break localized charge traps at oxygen vacancy sites and eliminate photoluminescence peaks associated with oxygen vacancies. The optimal performance of Mn-modified ZnO is demonstrated with the photodegradation of Congo red and photoelectrochemical water splitting.

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrostatic or dispersion interactions. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body nonbonded interactions in the data-driven modeling of matter.

Recently, there have been several experimental demonstrations of how the rates of concerted proton electron transfer (CPET) are affected by stepwise thermodynamic parameters of only proton (ΔG°_PT) or electron (ΔG°_ET) transfer. Semiclassical structure–activity relationships have been invoked to rationalize these linear free energy relationships, but it is not clear how they would manifest in a nonadiabatic reaction. Using density functional theory calculations, we demonstrate how a decrease in ΔG°_PT can lead to transition state imbalance in a nonadiabatic framework. We then use these calculations to anchor a theoretical model that reproduces experimental trends with ΔG°_PT and ΔG°_ET. Our results reconcile predictions from semiclassical transition state theory with models that treat proton transfer quantum mechanically in CPET reactivity, make new predictions about the importance of basicity for uphill CPET reactions, and suggest similar treatments may be possible for other nonadiabatic reactions.

Stereoelectronic effects in single-molecule junctions have been widely utilized to achieve a molecular switch, but high-efficiency and reproducible switching remain challenging. Here, we demonstrate that there are three stable intramolecular conformations in the 9,10-diphenyl-9,10-methanoanthracen-11-one (DPMAO) systems due to steric effect. Interestingly, different electronic coupling approaches including weak coupling (through-space), decoupling, and strong coupling (through-bond) between two terminal benzene rings are accomplished in the three stable conformations, respectively. Theoretical calculations show that the molecular conductance of three stable conformations differs by more than 1 order of magnitude. Furthermore, the populations of the three stable conformations are highly dependent on the solvent effect and the external electric field. Therefore, an excellent molecular switch can be achieved using the DPMAO molecule junctions and external stimuli. Our findings reveal that modulating intramolecular electronic coupling approaches may be a useful manner to enable molecular switches with high switching ratios. This opens up a new route for building high-efficiency molecular switches in single-molecular junctions.

Simulating the nuclear–electronic quantum dynamics of large-scale molecular systems in the condensed phase is key for studying biologically and chemically important processes such as proton transfer and proton-coupled electron transfer reactions. Herein, the real-time nuclear–electronic orbital time-dependent density functional theory (RT-NEO-TDDFT) approach is combined with a hybrid quantum mechanical/molecular mechanical (QM/MM) strategy to enable the accurate description of coupled nuclear–electronic quantum dynamics in the presence of heterogeneous environments such as solvent or proteins. The densities of the electrons and quantum protons are propagated in real time, while the other nuclei are propagated classically on the instantaneous electron–proton vibronic surface. This approach is applied to phenol bound to lysozyme, intramolecular proton transfer in malonaldehyde, and nonequilibrium excited-state intramolecular proton transfer in o-hydroxybenzaldehyde. These examples illustrate that the RT-NEO-TDDFT framework, coupled with an atomistic representation of the environment, allows the simulation of condensed-phase systems that exhibit significant nuclear quantum effects.

The geometry and surface state of nanocrystals (NCs) strongly affect their physiochemical properties, self-assembly behaviors, and potential applications, but there is still a lack of a facile method to regulate the exposed facets of the NCs, especially metal@semiconductor core–shell NCs. Herein, we present a reproducible approach for tuning the morphology of PbS NCs from nanocubes to nano-octahedrons by introducing lead halides as precursors. Excitingly, the method can be easily extended to the synthesis of metal@PbS core–shell NCs with single-crystalline shells and specific exposed facets. In addition, the halide passivation layers on the NCs are found to greatly improve their antioxidant ability. Therefore, the Cl-passivated NCs can self-assemble into atomic-coupled monolayer films via oriented attachment under ambient conditions, which exhibit enhanced electrical conductivities compared with uncoupled counterparts. The precise synthesis of nanocrystals with tunable shapes and the construction of self-assembled films provide a way to expand their application in high-performance optoelectronic devices.

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (M^pro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and M^pro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.

Neural network potentials have recently emerged as an efficient and accurate tool for accelerating ab initio molecular dynamics (AIMD) in order to simulate complex condensed phases such as electrolyte solutions. Their principal limitation, however, is their requirement for sufficiently large and accurate training sets, which are often composed of Kohn–Sham density functional theory (DFT) calculations. Here we examine the feasibility of using existing density functional tight-binding (DFTB) molecular dynamics trajectory data available in the IonSolvR database in order to accelerate the training of E(3)-equivariant graph neural network potentials. We show that the solvation structure of Na⁺ and Cl^– in aqueous NaCl solutions can be accurately reproduced with remarkably small amounts of data (i.e., 100 MD frames). We further show that these predictions can be systematically improved further via an embarrassingly parallel resampling approach.