Local Molecular Structure Research Articles

Copper-induced lattice distortion in Na4Fe3(PO4)2(P2O7) cathode enabling high power density Na-ion batteries with good cycling stability

Na4Fe3(PO4)2P2O7 (NFPP) has attracted attention due to its high theoretical capacity, low cost, and good structure stability for Na-ion batteries. However, its practical application is limited by the low intrinsic electronic conductivity and sluggish ion diffusion kinetics. To tackle those problems, lattice strain engineering by heteroatom doping is applied to tune the local molecular structure and optimize the electrochemical properties of electrode materials. Copper cation (+2) with a smaller ionic radius was chosen to dope at Fe-site in NFPP, and the doping amount was also optimized, illustrating the optimized sample of Na4Fe2.7Cu0.3(PO4)2P2O7 (NFPP-0.3Cu) delivered a high capacity of 119.01 mAh g-1 at 1 C (1 C = 129 mA g-1) and a high capacity retention of 82.76 % after 3000 cycles at 20 C. Notably, full cells with NFPP-0.3 Cu as cathode and hard carbon as anode delivered a high energy density of 230 Wh kg-1 and a power density of 2280 W kg-1. The exceptional electrochemical performances are attributed to the modulated electronic structure and abundant lattice defects by Cu-doping. Furthermore, in-situ X-ray diffraction technique and theoretical calculation have jointly proved that the lattice distortions originated from Cu-doping have reduced the band gap of the NFPP-0.3Cu and altered the coordination environment of Fe, shortening the Fe-O and Cu-O bond lengths, significantly enhancing the intrinsic ionic conductivity and the diffusion kinetics of Na+. This work provides new point of views on lattice strain engineering and reaction kinetics of cathode materials in promoting high energy and power density sodium-ion batteries.

The Role of the Anion in Concentrated Electrolytes for Lithium-Sulfur Batteries

Highly concentrated electrolytes show promise in enhancing lithium-sulfur (Li-S) battery performance by mitigating polysulfide (PS) solubility. The role of the salt anion for the performance improvement(s) is however not well understood. Here a systematic characterization using (concentrated) electrolytes based on three different salts: LiTFSI, LiTf, and LiTDI, in a common DOL:DME solvent mixture is reported for a wide range of physicochemical and electrochemical properties: ionic conductivity, density, viscosity, speciation, and PS solubility. While increased salt concentration in general improves Li-S battery performance, the role of the salt anion introduces complexity. The 2 m LiTDI-based electrolyte, with a slightly higher viscosity and lower PS solubility, outperforms the LiTFSI-based counterpart in terms of accessible reversible capacity. Conversely, the 2 m LiTf-based electrolyte exhibits subpar performance due to the formation of ionic aggregates that renders more free solvent and, therefore higher PS solubility, which, however can be improved by using a 5 m concentrated electrolyte. Hence, using electrolyte salt concentration as a rational design route demands an understanding of the local molecular structure, largely determined/affected by the choice of anion, as well as how it connects to the global properties and in the end improved Li-S battery performance.

New measurement system based on small-angle neutron scattering for structural analysis of light-responsive materials

With the increasing importance of light-responsive materials, it is vital to analyze the relationship between function and structural changes induced by light irradiation. Small-angle scattering (SAS) is effective for such structural analysis. However, quantitatively capturing local molecular structure formation and molecular reactions at a scale of less than 1 nm via SAS is difficult. In this study, to analyze the structure of non-equilibrium phenomena in light-responsive materials, a new sample environment has been developed for a time-of-flight small- and wide-angle neutron scattering instrument (TAIKAN), comprising a UV–Vis irradiation system, UV–Vis absorption measurement equipment and photodetector. Simultaneous measurement of small-angle neutron scattering and UV–Vis absorption was achieved. This system was used to demonstrate the in situ observation of UV–Vis irradiation-induced structural change of micelles formed by 4-butylazobenzene-4′-(oxyethyl)trimethylammonium bromide, which is a light-responsive surfactant, in an aqueous solution. The results showed that the present measuring system provides direct information on the interplay between changes in micelle structure and changes in molecular configuration.

Molecular cluster analysis using local order parameters selected by machine learning.

Accurately extracting local molecular structures is essential for understanding the mechanisms of phase and structural transitions. A promising method to characterize the local molecular structure is defining the value of the local order parameter (LOP) for each particle. This work develops the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO), a machine learning package that can propose an optimal (set of) LOP(s) quickly and automatically for a huge number of LOP species and various methods of selecting neighboring particles for the calculation. We applied this package to distinguish between the nematic and smectic phases of uniaxial liquid crystal molecules, and selected candidate LOPs that could be used to precisely observe the nematic-smectic phase transition. The LOP candidates were used to observe the nucleation and subsequent percolation transition, and the effect of the choice of LOP species and neighboring particles on the statistics of local molecular structures (clusters) was examined. The procedure revealed the time evolution of the number of clusters and the dependence of the percolation curve on the number of neighboring particles for each LOP species. The LOP species with the lowest dependence on the number of neighboring particles was the best-performing LOP species in the MALIO screening strategy. These results not only show that machine learning can powerfully screen a huge number of LOP species and suggest only a few promising candidates, but also indicate that MALIO can select the best LOP species.

Molecular Simulation-Guided Spectroscopy of Imidazolium-Based Ionic Liquids and Effects of Methylation on Ion-Cage and -Pair Dynamics.

Classical molecular dynamics simulations were performed to assess an atomistic interpretation of the ion-probe structural interactions in two typical ionic liquids (ILs), 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [BMIm][NTf2] and 1-butyl-2,3-dimethylimidazolium bis(trifluoromethylsulfonyl)imide [BDimIm][NTf2] through computational ultrafast spectroscopy. The nitrile stretching vibrations of the thiocyanate anion, [SCN]-, serve as the local mode of the ultrafast system dynamics within the imidazolium-based ionic liquid environment. The wavelet transform of classical trajectories determines the time-varying fluctuating frequencies and the stretch spectral signatures of SCN- in the normalized distribution. However, computational modeling of the two-dimensional (2D) spectra from the wavelet-derived vibrational frequencies yields time evolution of the local molecular structure along with the varied time-dependent dynamics of the spectral diffusion process. We calculated the frequency-frequency correlation functions (FFCFs), time correlations associated with the ion-pair and -cage dynamics, and mean square displacements as a function of time, depicting diffusive dynamics. The calculated results based on the pair correlation functions and the distribution of atomic density suggest that the hydrogen and methylated carbon at the two-position of the imidazolium ring of [BMIm] and [BDimIm] cations, respectively, strongly interact with the probe through the N of the thiocyanate anion rather than the S atom. The center-of-mass center-of-mass (COM-COM) cation-probe radial distribution functions (RDFs) in conjunction with the site-specific structural analysis further reveal well-structured interactions of the thiocyanate ion and [BMIm]+ cation rather than the [BDimIm] cation. In contrast, the anion-probe COM-COM RDFs depict weak interactive associations within the vibrational probe [SCN]- and [NTf2]- ions. Methylation at the two-position of the imidazolium ring predicts slower structural reorganization and breaking and reformation dynamics of the ion pairs and cages within the ionic liquid framework.

Multiple Ensembles of the Hydrogen-bonded Network in Ethylammonium Nitrate versus Water from Vibrational Spectral Dynamics of SCN- Probe.

We performed classical molecular dynamics simulations to monitor the structural interactions and ultrafast dynamical and spectral response in the protic ionic liquid, ethylammonium nitrate (EAN) and water using the nitrile stretching mode of thiocyanate ion (SCN- ) as the vibrational probe. The normalized frequency distribution of the nitrile stretch in SCN- attains an asymmetric shape in EAN, indicating the existence of more than one hydrogen-bonding environment in EAN. Further, we computed the 2D IR spectrum from classical trajectories, applying the response function formalism. Spectral diffusion dynamics in EAN undergo an initial rattling of the SCN- inside the local ion-cage occurring at a timescale of 0.10 ps, followed by the breakup of the ion-cage activating molecular diffusion at 7.86 ps timescale. In contrast, the dynamics of structural reorganization occur at a timescale of 0.58 ps in H2 O. Hence, the time dependence of the frequency-frequency correlation function decay hints at the local molecular structure and ultrafast ion dynamics of the SCN- probe. The loss of frequency correlation read from the peak shape changes in the 2D correlation spectrum as a function of waiting time is faster in H2 O than in EAN due to the enhanced structural ordering and higher viscosity of the latter. We provide an atomic-level interpretation of the solvation environment around SCN- in EAN and water, which indicates multiple ensembles of the hydrogen bond network in EAN.

Activation of Mechanophores in a Thermoset Matrix by Instrumented Scratch.

Scratches in polymer coatings and barrier layers negatively impact optical properties (haze, light transmission, etc.), initiate routes of degradation or corrosion (moisture permeability), and nucleate delamination of the coating. Detecting scratches in coatings on advanced materials systems is an important component of structural health monitoring but can be difficult if the defects are too small to be detected by the naked eye. The primary focus of the present work is to investigate scratch damage using fluorescence lifetime imaging microscopy (FLIM) and mechanical activation of a mechanophore (MP)-containing transparent epoxy coating. The approach utilizes a Berkovich tip to scratch MP-epoxy coatings under a linearly increasing normal load. The goal is to utilize the fluorescent behavior of activated MPs to enable the detection of microscale scratches and molecular scale changes in polymeric systems. Taking advantage of the amine functionality present in a polyetheramine/bisphenol A epoxy network, a modified rhodamine dye is covalently bonded into a transparent, thermoset polymer network. Following instrumented scratch application, subsequent fluorescence imaging of the scratched MP-epoxy reveals the extent of fluorescence activation induced by the mechanical deformation. In this work, the rhodamine-based mechanophore is used to identify both ductile and fracture-dominated processes during the scratch application. The fluorescence intensity increases linearly with the applied normal load and is sensitive to fracture dominated processes. Fluorescence lifetime and hyperspectral imaging of damage zones provide additional insight into the local (nanoscopic) environment and molecular structure of the MP around the fracture process zone, respectively. The mechanophore/scratch deformation approach allows a fluorescence microscope to probe local yielding and fracture events in a powerful way that enhances the optical characterization of damage zones formed by standard scratch test methods and leads to novel defect detection strategies.

Light Scattering and Absorption Complementarities to Neutron Scattering: In Situ FTIR and DLS Techniques at the High-Intensity and Extended Q-Range SANS Diffractometer KWS-2

Understanding soft and biological materials requires global knowledge of their microstructural features from elementary units at the nm scale up to larger complex aggregates in the micrometer range. Such a wide range of scale can be explored using the KWS-2 small-angle neutron (SANS) diffractometer. Additional information obtained by in situ complementary techniques sometimes supports the SANS analysis of systems undergoing structural modifications under external stimuli or which are stable only for short times. Observations at the local molecular level structure and conformation assists with an unambiguous interpretation of the SANS data using appropriate structural models, while monitoring of the sample condition during the SANS investigation ensures the sample stability and desired composition and chemical conditions. Thus, we equipped the KWS-2 with complementary light absorption and scattering capabilities: Fourier transform infrared (FTIR) spectroscopy can now be performed simultaneously with standard and time-resolved SANS, while in situ dynamic light scattering (DLS) became available for routine experiments, which enables the observation of either changes in the sample composition, due to sedimentation effects, or in size of morphologies, due to aggregation processes. The performance of each setup is demonstrated here using systems representative of those typically investigated on this beamline and benchmarked to studies performed offline.

Construction of a spherically bent crystal analyzer through anodic bonding for a non-resonant inelastic X-ray scattering spectrometer

The anodic bonding method was used to construct spherically bent crystal analyzers for a synchrotron radiation based non-resonant inelastic X-ray scattering spectrometer to probe the local electronic and molecular structure. In order to test the performance of the fabricated analyzers, the O K-edge X-ray Raman scattering spectrum of a lithium-ion battery electrolyte solution was measured and compared to the spectrum in the literature, which was measured by using a conventional X-ray absorption spectroscopy. The X-ray Raman scattering spectrometer based on a 1 m Rowland circle provided to record the data with a resolution of 0.8 eV at ∼10 keV. The obtained experimental results showed that the anodic bonding was achieved with a high bonding quality.

Transport Properties and Local Structure of an Imidazole/Protic Ionic Liquid Mixture Confined in the Mesopores of Hydrophobic Silica

The local structure and the molecular dynamics of an imidazole/protic ionic liquid mixture have been investigated while confined in only ca. 5 nm mesopores of silica particles. The walls of the silica pores were functionalized with trioctyl groups to ensure a hydrophobic character, and a series of hybrid materials with varying liquid-to-silica ratios were investigated. Results from vibrational spectroscopy (both Raman and infrared) indicate that the local ion–ion interactions as well as the nature of hydrogen bonds inside the nanopores are not significantly different from the case of the bulk liquid mixture. Nevertheless, the ionic conductivity decreases rapidly and monotonically with decreasing amount of liquid, while the self-diffusion coefficients measured by pulsed field gradient nuclear magnetic resonance (NMR) show a distinct dependence on composition. The population of molecules outside the particles seems to contribute with an enhanced diffusivity, while the molecules inside the mesopores diffuse at a rate comparable to that observed in the bulk liquid. In addition, when experimentally possible, we have measured higher diffusivities for the exchangeable −NH proton than for any other molecular species, which is indicative of a decoupled proton motion. Results from X-ray scattering, employed to elucidate the local molecular structure, reveal an additional feature characteristic of the nanoconfined state, which is associated with a real space distance of about 3.5 nm. This distance describes a specific molecular organization inside the mesopores and may reflect the formation of a monolayer of the octyl-imidazolium cations self-assembled at the hydrophobic silica surface. Such a local structure would favor the localization of charges, including the exchangeable protons. In addition, the analysis of molar conductivity suggests that a major problem with a low pore filling is the emergence of discontinuities throughout the liquid phase.

Influence of Polymorphs and Local Defect Structures on NMR Parameters of Graphite Fluorides

The role of local molecular structure on calculated 13C and 19F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computatio...

Light-induced excited spin state trapping in iron(iii) complexes

This review discusses the correlation of the local and whole molecular structure of iron(iii) complexes with the magnetic properties including the light-induced excited spin-state trapping (LIESST) effect.

Selected Configuration Interaction in a Basis of Cluster State Tensor Products.

Selected configuration interaction (SCI) methods are currently enjoying a resurgence due to several recent developments which improve either the overall computational efficiency or the compactness of the resulting SCI vector. These recent advances have made it possible to get full CI (FCI) quality results for much larger orbital active spaces compared to conventional approaches. However, due to the starting assumption that the FCI vector has only a small number of significant Slater determinants, SCI becomes intractable for systems with strong correlation. This paper introduces a method for developing SCI algorithms in a way which exploits local molecular structure to significantly reduce the number of SCI variables. The proposed method is defined by first grouping the orbitals into clusters over which we can define many-particle cluster states. We then directly perform the SCI algorithm in a basis of tensor products of cluster states instead of Slater determinants. While the approach is general for arbitrarily defined cluster states, we find significantly improved performance by defining cluster states through a Tucker decomposition of the global (and sparse) SCI vector. To demonstrate the potential of this method, called tensor product selected configuration interaction (TPSCI), we present numerical results for a diverse set of examples: (1) modified Hubbard model with different inter- and intracluster hopping terms, (2) less obviously clusterable cases of bond breaking in N2 and F2, and (3) ground state energies of large planar π-conjugated systems with active spaces of up to 42 electrons in 42 orbitals. These numerical results show that TPSCI can be used to significantly reduce the number of SCI variables in the variational space, thus paving a path for extending these deterministic and variational SCI approaches to a wider range of physical systems.

Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics

The binary interaction parameters of the nonrandom two liquid (NRTL) thermodynamic model are predicted for several organic mixtures using molecular simulations. Based on the theoretical framework of the two-fluid theory, the binary interaction parameters are expressed in terms of the interaction energies, size of the molecules, and size of the local molecular domains; these quantities are calculated from molecular simulations. We show that our technique is robust in terms of its predictions involving organic mixtures with compatible chemical characteristics while we propose possible modifications in the case of mixtures involving incompatible chemical components or significant size disparity, where there is a notable difference between the interaction parameters calculated from simulations and those obtained from experimental data regression. We further demonstrate that the binary interaction parameters calculated from data regression are not unique and that molecular simulations can guide the parameter selection process by identifying physically relevant binary interaction parameters. Requiring only the local molecular structure information from molecular simulations, the method offers fast and reliable prediction of phase equilibrium properties, especially in cases where limited experimental data are available.

Rotational diffusion dynamics of Alexa flour dyes in aqueous organic environment

The rotational reorientation times of three dye probes namely Alexa fluor-350 (neutral but polar), Alexa fluor-430 (monoanion) and Alexa fluor-488 (dianion) were determined in series of binary mixtures of DMSO-water, DMA-water, and 2-propanol-water. All the three series of mixtures show bivalued profile for viscosity as a function of mole fraction of organic solvent in water. The dielectric properties such as dielectric constant (ε) and Debye dielectric relaxation time (τD) of solutions change considerably across the composition range. The variation of rotational reorientation time (τr) with viscosity (η) for all the probes is studied. Alexa fluor 350 and alexa fluor 488 show hook type profile in all the three series of aqueous mixtures with rotational reorientation time (τr) being longer in organic solvent rich zone compared to water rich zone mainly due to dielectric friction and hydrogen bonding. Alexa fluor 430 shows hook type profile in 2-propanol-water mixtures similar to other two probes but in DMSO-water and DMA-water it has almost linear profile as rotational reorientation dynamics is almost similar in both organic solvent rich zone and water rich zone. The hook type profiles obtained suggest the importance of local molecular structure of binary mixture and its interaction with probe. Results obtained are discussed in light of SED (hydrodynamic) theory, dielectric friction theories and friction due to hydrogen bonding.

Physical insights into stress–strain process of polymers under tensile deformation via machine learning

ABSTRACT Strain localization is a ubiquitous phenomenon of soft matters subjected to strain. Polymeric materials are a very important class of soft materials and widely used nowadays. Polymers have unique strain localization behavior such as crazing during tensile deformation. How to understand the mechanism at the molecular level of strain localization in polymeric materials has become an important topic in material science. In this work, tensile deformation process of polymers both under a melt state and a glassy state are investigated in MD simulations using a generic coarse-grained model. We use a machine learning technique, i.e., support vector machine (SVM) algorithm, to understand the local molecular structure and the dynamical properties during tensile deformation. By defining “softness” from the SVM model, we investigate the stress–strain behavior of both ductile polymer above glass transition temperature and brittle polymer glass during tensile deformation. We demonstrated that the softness can be used to predict physical properties efficiently; the softness provides deep physical insights into the non-equilibrium stress–strain process. We also find that the Hookean behavior of polymer glasses is mostly contributed by the hard regions of the system, and the elastic limit is quantitatively discussed as well.

Molecular insight into the lower critical solution temperature transition of aqueous alkyl phosphonium benzene sulfonates

Ionic liquid (IL)-water mixtures can exhibit a lower critical solution temperature (LCST) transition, but changes in long-range order and local molecular environment during this transition are not comprehensively understood. Here we show that in IL-H2O LCST mixtures, the IL forms loosely held aggregate structures that grow in size leading up to a critical temperature, whereas the aggregation of a fully miscible aqueous mixture, obtained by minor chemical modification of the anion, decreases with increasing temperature. Radial distribution functions from molecular dynamics simulations support the observation of aggregation phenomena in the IL-H2O mixtures. A local molecular structure of the ions is derived from multi-dimensional NMR experiments in conjunction with reported molecular dynamics simulations. In addition to considerable shifts of water’s hydrogen bonding network in the fully miscible phase, by NMR we observe the anion’s protons response to the intermolecular thermal environment and the intramolecular environment and find that the responses are determined by the sulfonate ionic functional group.

Predicting the Physical Stability of Amorphous Tenapanor Hydrochloride Using Local Molecular Structure Analysis, Relaxation Time Constants, and Molecular Modeling.

The conformational flexibility of organic molecules introduces more structural options for crystallization to occur but has potential complications, such as, reduced crystallization tendency and conformational polymorphism. Although a variety of energetically similar conformers could be anticipated, it is extremely difficult to predict the crystal conformation for conformationally flexible molecules. The present study investigates differences in thermodynamic parameters for the free base, c-FB, and an amorphous dihydrochloride salt, a-Di-HCl, of a conformationally flexible drug substance, tenapanor (RDX5791). A variety of complementary techniques such as, thermal analysis, powder X-ray diffraction (PXRD), and molecular modeling were used to assess the thermodynamic properties and the propensity of crystallization for a-FB and a-Di-HCl, tenapanor. Molecular modeling and total scattering measurements suggested that the a-Di-HCl salt exists in an open elongated state with local 1D stacking, which extends only to the first nearest neighbor, while the a-FB shows local stacking extending to the third nearest neighbor. The overall relaxation behavior, which typically is an indicator for physical stability, as measured by modulated temperature differential scanning calorimetry and PXRD suggested a nontypical dual relaxation process for the dihydrochloride salt form. The first relaxation was fast and occurred on warming from the quench conditions without any thermal annealing, while the second relaxation step followed a more traditional glass relaxation model, exhibiting an infinite relaxation time. Similar analysis for the a-FB suggested a comparatively shorter relaxation time (about 19 days) that results in its rapid crystallization. This observation is further validated with the extensive amount of physical stability data collected for the a-Di-HCl salt form of tenapanor under accelerated and stress stability conditions, as well as long-term storage for more than 3 years that show no change in its amorphous state.

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis.

We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input. The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecular orbital properties, such as Fock, Coulomb, and exchange matrix elements. With the aim of maximizing transferability across chemical systems and compactness of the feature set, we avoid the usual specification of ML features in terms of atom- or geometry-specific information, such atom/element-types, bond-types, or local molecular structure. ML predictions of MP2 and CCSD energies are presented for a range of systems, demonstrating that the method maintains accuracy while providing transferability both within and across chemical families; this includes predictions for molecules with atom-types and elements that are not included in the training set. The method holds promise both in its current form and as a proof-of-principle for the use of ML in the design of generalized density-matrix functionals.

Relation between molecular electronic structure and nuclear spin-induced circular dichroism

The recently theoretically described nuclear spin-induced circular dichroism (NSCD) is a promising method for the optical detection of nuclear magnetization. NSCD involves both optical excitations of the molecule and hyperfine interactions and, thus, it offers a means to realize a spectroscopy with spatially localized, high-resolution information. To survey the factors relating the molecular and electronic structure to the NSCD signal, we theoretically investigate NSCD of twenty structures of the four most common nucleic acid bases (adenine, guanine, thymine, cytosine). The NSCD signal correlates with the spatial distribution of the excited states and couplings between them, reflecting changes in molecular structure and conformation. This constitutes a marked difference to the nuclear magnetic resonance (NMR) chemical shift, which only reflects the local molecular structure in the ground electronic state. The calculated NSCD spectra are rationalized by means of changes in the electronic density and by a sum-over-states approach, which allows to identify the contributions of the individual excited states. Two separate contributions to NSCD are identified and their physical origins and relative magnitudes are discussed. The results underline NSCD spectroscopy as a plausible tool with a power for the identification of not only different molecules, but their specific structures as well.