Faraday Discussions最新文献

Nanoparticles (NPs) of iron oxide are dispersed in mixtures of water and ionic liquid, here ethylammonium nitrate (EAN), and the NP/NP and NP/solvent interactions are studied. They are analysed via small-angle X-ray scattering and dynamic light scattering coupled to forced Rayleigh scattering, from 22 °C to 80 °C. The NPs are well-dispersed as individual objects in the whole range of compositions and temperatures thanks to sufficient repulsion due to the organization of the solvents at the interface. The surface changes from hydrophilic to hydrophobic around a proportion of 50 vol% water : 50 vol% EAN, following the evolution of the bulk mixtures, which remain heterogeneous in the whole range of compositions.

Collisions of optically centrifuged CO₂ molecules with J = 244–282 (E_rot = 22 800–30 300 cm⁻¹) are investigated with high-resolution transient IR absorption spectroscopy to reveal collisional and orientational phenomena of molecules with hyper-thermal rotational energies. The optical centrifuge is a non-resonant optical excitation technique that uses ultrafast, 800 nm chirped pulses to drive molecules to extreme rotational states through sequential Raman transitions. The extent of rotational excitation is controlled by tuning the optical bandwidth of the excitation pulses. Frequencies of 30 R-branch ν₃ fundamental IR probe transitions are measured for the J = 186–282 states of CO₂, expanding beyond previously reported IR transitions up to J = 128. The optically centrifuged molecules have oriented angular momentum and unidirectional rotation. Polarization-sensitive transient IR absorption of individual rotational states of optically centrifuged molecules and their collision products reveals information about collisional energy transfer, relaxation kinetics, and dynamics of rotation-to-translation energy transfer. The transient IR probe also measures the extent of polarization anisotropy. Rotational energy transfer for lower energy molecules is discussed in terms of statistical models and a comparison highlights the role of increasing energy gap with J and angular momentum of the optically centrifuged molecules.

As part of an ongoing study of the structure and properties of mixtures of ionic liquids in which one component has a hydrocarbon chain and the other a semiperfluorocarbon chain, we now report a study of the mixtures [C₈MIM]_1−x[C₁₀MIM-F₁₇]_x[Tf₂N], [C₁₀MIM]_1−x[C₈MIM-F₁₃]_x[Tf₂N] and [C₁₀MIM]_1−x[C₁₀MIM-F₁₇]_x[Tf₂N], where [C₈MIM][Tf₂N] is 1-methyl-3-octylimidazolium bis(trifluoromethylsulfonyl)imide, [C₁₀MIM][Tf₂N] is 1-decyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [C₈MIM-F₁₃][Tf₂N] is 1-(1H,1H,2H,2H-perfluorooctyl)-3-methylimidizolium bis(trifluoromethylsulfonyl)imide and [C₁₀MIM-F₁₇][Tf₂N] is 1-(1H,1H,2H,2H-perfluorodecyl)-3-methylimidizolium bis(trifluoromethylsulfonyl)imide. The mixtures were investigated using small-angle X-ray (SAXS) and neutron (SANS) scattering complemented by molecular dynamics simulations (with viscosity and surface tension measurements also possible for the mixtures [C₁₀MIM]_1−x[C₈MIM-F₁₃]_x[Tf₂N]). Unlike previous studies of [C₈MIM]_1−x[C₈MIM-F₁₃]_x[Tf₂N], where no strong evidence of alkyl/fluoroalkyl chain segregation or triphilic behaviour was seen (Elstone et al., J. Phys. Chem. B, 2023, 127, 7394–7407), these new mixtures show the formation of small aggregates of varying sizes of each component, even though all were co-miscible across the full range of compositions. Thus, while a clear polar non-polar peak (PNPP) was observed at large or small values of x, at intermediate compositions the small-angle neutron scattering at low q was dominated by scattering from these small aggregates, while at other compositions, there was little or no evidence of the PNPP. The origins of this behaviour are discussed in terms of inter-chain interactions.

The field of computational chemistry has made an impressive impact on contemporary chemical research. In order to carry out computational studies on actual systems, sophisticated software is required in form of large-scale quantum chemical program packages. Given the enormous diversity and complexity of the methods that need to be implementation in such packages, it is evident that these software pieces are very large (millions of code lines) and extremely complex. Most of the packages in widespread use by the computational chemistry community have had a development history of decades. Given the rapid progress in the hardware and a lack of resources (time, workforce, money), it is not possible to keep redesigning these program packages from scratch in order to keep up with the ever more quickly shifting hardware landscape. In this perspective, some aspects of the multitude of challenges that the developer community faces are discussed. While the task at hand – to ensure that quantum chemical program packages can keep evolving and make best use of the available hardware – is daunting, there are also new evolving opportunities. The problems and potential cures are discussed with the example of the ORCA package that has been developed in our research group.

The cumulant expansion of the Green's function is a computationally efficient beyond-GW approach renowned for its significant enhancement of satellite features in materials. In contrast to the ubiquitous GW approximation of many-body perturbation theory, ab initio cumulant expansions performed on top of GW (GW + C) have demonstrated the capability to handle multi-particle processes by incorporating higher-order correlation effects or vertex corrections, yielding better agreements between experiment and theory for satellite structures. While widely employed in condensed matter physics, very few applications of GW + C have been published on molecular systems. Here, we assess the performance of this scheme on a series of 10-electron molecular systems (Ne, HF, H₂O, NH₃, and CH₄) where full configuration interaction estimates of the outer-valence quasiparticle and satellite energies are available.

The adiabatic connection (AC) approximation, along with its linearized variant AC0, was introduced as a method of obtaining dynamic correlation energy. When using a complete active space self-consistent field (CASSCF) wave function as a reference, the AC0 approximation is considered one of the most efficient multi-reference perturbation theories. It only involves the use of 1st- and 2nd-order reduced density matrices. However, some numerical results have indicated that the excitation energies predicted by AC0 are not as reliable as those from the second-order N-electron valence state perturbation theory (NEVPT2). In this study, we develop a spinless formulation of AC0 based on the Dyall Hamiltonian and provide a detailed comparison between AC0 and NEVPT2 approaches. We demonstrate the components within the correlation energy expressions that are common to both methods and those unique to either AC0 or NEVPT2. We investigate the role of the terms exclusive to NEVPT2 and explore the possibility of enhancing AC0’s performance in this regard.

The Cu₂OCl₂ compound has been shown to be a high-temperature spin-driven multiferroic system, with a linear magneto-electric coupling. In this paper we propose a complete study of its magnetic structure. We derive the low energy magnetic Hamiltonian using ab initio multi-reference configuration interaction and the spin structure using Monte-Carlo simulations. Among the three magnetic structures proposed in the literature from different experimental results, our calculations support the incommensurate cycloid magnetic structure with a = (q_a,0,0) propagation vector. Using symmetry analysis, we show that all experimental results (polarization, magnetic order, magneto-electric coupling) can be accounted for in the Fd′d′2 magnetic space group (2-fold axis along ).

The task of computing wavefunctions that are accurate, yet simple enough mathematical objects to use for reasoning, has long been a challenge in quantum chemistry. The difficulty in drawing physical conclusions from a wavefunction is often related to the generally large number of configurations with similar weights. In Tensor Product Selected Configuration Interaction (TPSCI), we use a locally correlated tensor product state basis, which has the effect of concentrating the weight of a state onto a smaller number of physically interpretable degrees of freedom. In this paper, we apply TPSCI to a series of three molecular systems ranging in separability, one of which is the first application of TPSCI to an open-shell bimetallic system. For each of these systems, we obtain accurate solutions to large active spaces, and analyze the resulting wavefunctions through a series of different approaches including (i) direct inspection of the TPS basis coefficients, (ii) construction of Bloch effective Hamiltonians, and (iii) computation of cluster correlation functions.

Li–S batteries have attracted attention as next-generation rechargeable batteries owing to their high theoretical capacity and cost-effectiveness. Sparingly solvating electrolytes hold promise because they suppress the dissolution and shuttling of polysulfide intermediates to increase the coulombic efficiency and extend the cycle life. This study investigated the solubility of polysulfide (Li₂S₈) in a range of liquid electrolytes, including organic electrolytes, highly concentrated electrolytes, and ionic liquids. The Li₂S₈ solubility was well correlated with the donor number (DN_NMR), estimated via²³Na-NMR, and was lower than 100 mM_(elemental sulfur) in electrolytes with DN_NMR < 14, regardless of the type of electrolyte. Highly concentrated electrolytes comprising lithium bis(trifluoromethanesulfonyl)amide (LiTFSA) and linear chain dialkyl ethers such as methyl propyl ether (MPE), n-butyl methyl ether (BME), and ethyl propyl ether (EPE) were studied as sparingly solvating electrolytes for Li–S batteries. Monomethyl ethers, such as BME, showed more pronounced Li-ion coordination and higher ionic conductivity, whereas the steric hindrance of the longer alkyl chains in EPE lowered the solvation number, enhanced ion association, and lowered the ionic conductivity despite the solvents having similar dielectric constants. The charge–discharge rate capabilities of Li–S cells with dialkyl ether-based electrolytes were more impressive than those of cells with a localized high-concentration electrolyte using sulfolane (SL) and hydrofluoroether (HFE), [Li(SL)₂][TFSA]-2HFE. The higher rate performance was attributed to the superior Li-ion transport properties of the dialkyl ether-based electrolytes. A pouch-type cell using lightweight [Li(BME)₃][TFSA] demonstrated an energy density exceeding 300 W h kg⁻¹ under lean electrolyte conditions.

Key to being able to accurately model the properties of realistic materials is being able to predict their properties in the thermodynamic limit. Nevertheless, because most many-body electronic structure methods scale as a high-order polynomial, or even exponentially, with system size, directly simulating large systems in their thermodynamic limit rapidly becomes computationally intractable. As a result, researchers typically estimate the properties of large systems that approach the thermodynamic limit by extrapolating the properties of smaller, computationally-accessible systems based on relatively simple scaling expressions. In this work, we employ Gaussian processes to more accurately and efficiently extrapolate many-body simulations to their thermodynamic limit. We train our Gaussian processes on Smooth Overlap of Atomic Positions (SOAP) descriptors to extrapolate the energies of one-dimensional hydrogen chains obtained using two high-accuracy many-body methods: coupled cluster theory and Auxiliary Field Quantum Monte Carlo (AFQMC). In so doing, we show that Gaussian processes trained on relatively short 10–30-atom chains can predict the energies of both homogeneous and inhomogeneous hydrogen chains in their thermodynamic limit with sub-milliHartree accuracy. Unlike standard scaling expressions, our GPR-based approach is highly generalizable given representative training data and is not dependent on systems’ geometries or dimensionality. This work highlights the potential for machine learning to correct for the finite size effects that routinely complicate the interpretation of finite size many-body simulations.