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We present extensive new ab initio path integral Monte Carlo (PIMC) results for a variety of structural properties of warm dense hydrogen and beryllium. To deal with the fermion sign problem—an exponential computational bottleneck due to the antisymmetry of the electronic thermal density matrix—we employ the recently proposed [Y. Xiong and H. Xiong, J. Chem. Phys. 157, 094112 (2022); T. Dornheim et al., J. Chem. Phys. 159, 164113 (2023)] ξ-extrapolation method and find excellent agreement with the exact direct PIMC reference data where available. This opens up the intriguing possibility of studying a gamut of properties of light elements and potentially material mixtures over a substantial part of the warm dense matter regime, with direct relevance for astrophysics, material science, and inertial confinement fusion research. [ABSTRACT FROM AUTHOR]

Pressure-induced phase transformation in iron and its alloys is a classic research topic in solid-state physics, material science, and geophysics. The crystal structure of iron undergoes a phase transformation at a hydrostatic pressure of 13 GPa, changing from a body-centered cubic system to a hexagonal close-packed system. Although extensive research has been carried out on the transformation in iron by using molecular dynamics simulations, there is very limited literature that focuses on the contribution of parent phase orientations, system size, and impurities to the phase evolution. In this work, classic molecular dynamics simulations have been employed to investigate the effects of system size, lattice orientation, and impurity concentration on the pressure-induced phase transformation of iron and iron alloys for the first time. Our results show that the lattice orientation has a strong influence on the phase transition behavior, while the influence of carbon is small. The phase transition is slightly delayed with increasing carbon content, whereas the transition pressure increases from [001] to [011] to [111] orientation. The amount of twinning and stacking faults highly depends on the orientation. It is easiest for solitary waves to travel through [111] lattice orientation. The addition of carbon has a slow-down effect on shock velocities, and this effect increases with carbon content and lattice orientation of the samples from [001] to [011] to [111]. [ABSTRACT FROM AUTHOR]

In a recent work, we have briefly introduced a new structural index for water that, unlike previous indicators, was devised specifically for generic contexts beyond bulk conditions, making it suitable for hydration and nanoconfinement settings. In this work, we shall study this metric in detail, demonstrating its ability to reveal the existence of a fine-tuned interplay between the local structure and energetics in liquid water. This molecular principle enables the establishment of an extended hydrogen bond network, while simultaneously allowing for the existence of network defects by compensating for uncoordinated sites. By studying different water models and different temperatures encompassing both the normal liquid and the supercooled regime, this molecular mechanism will be shown to underlie the two-state behavior of bulk water. In addition, by studying functionalized self-assembled monolayers and diverse graphene-like surfaces, we shall show that this principle is also operative at hydration and nanoconfinement conditions, thus generalizing the validity of the two-liquid scenario of water to these contexts. This approach will allow us to define conditions for wettability, providing an accurate measure of hydrophobicity and a reliable predictor of filling and drying transitions. Hence, it might open the possibility of elucidating the active role of water in the broad fields of biophysics and materials science. As a preliminary step, we shall study the hydration structure and hydrophilicity of graphene-like systems (parallel graphene sheets and carbon nanotubes) as a function of the confinement dimensionality. [ABSTRACT FROM AUTHOR]

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa2Cu3O6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa2Cu3O6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. In addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. [ABSTRACT FROM AUTHOR]

The design of metallic glasses (MGs) with good properties is one of the long-standing bottlenecks in materials science and engineering, which has been relying mostly on far less efficient traditional trial-and-error methods. Even the currently popular machine learning-based forward designs, which use manual input to navigate high dimensional compositional space, often become inefficient with the increasing compositional complexity in MGs. Here, we developed an inverse design machine learning model, leveraging the variational autoencoder (VAE), to directly generate the MGs with good glass-forming ability (GFA). We demonstrate that our VAE with the property prediction model is not only an expressive generative model but also able to do accurate property prediction. Our model allows us to automatically generate novel MG compositions by performing simple operations in the latent space. After randomly generating 3000MG compositions using the model, a detailed analysis of four typical metallic alloys shows that unreported MG compositions with better glass-forming ability can be predicted. Moreover, our model facilitates the use of powerful optimization algorithms to efficiently guide the search for MGs with good GFA in the latent space. We believe that this is an efficient way to discover MGs with excellent properties. [ABSTRACT FROM AUTHOR]

In this study, we observed natural methane (CH4) hydrate sediments, which are a type of unconventional natural gas resources, using x-ray computed tomography (CT). Because CH4 hydrates are formed by hydrogen bonding of water molecules with CH4, material decomposition becomes challenging when CH4 hydrates coexist with liquid or solid water in natural sediments. Tri-contrast (absorption, refraction, and scattering) imaging was performed via diffraction enhanced x-ray CT optics using monochromatic synchrotron x rays. The quantitative characterization of the contrast changes successfully enabled the decomposition of CH4 hydrates coexisting with frozen seawater (ice) in natural sediments obtained from the Okhotsk Sea. This study reveals complementary structural information about the microtexture and spatial relation among CH4 hydrates, ice, and pores by utilizing the distinct physical properties of x rays when passing through the materials. These results highlight the exceptional capabilities of high-resolution multicontrast x-ray tomography in materials science and geoscience applications. [ABSTRACT FROM AUTHOR]

The dynamics of a soft particle suspended in a viscous fluid can be changed by the presence of an elastic boundary. Understanding the mechanisms and dynamics of soft–soft surface interactions can provide valuable insights into many important research fields, including biomedical engineering, soft robotics development, and materials science. This work investigates the anomalous transport properties of a soft nanoparticle near a visco-elastic interface, where the particle consists of a polymer assembly in the form of a micelle and the interface is represented by a lipid bilayer membrane. Mesoscopic simulations using a dissipative particle dynamics model are performed to examine the impact of micelle's proximity to the membrane on its Brownian motion. Two different sizes are considered, which correspond to ≈ 10 − 20 n m in physical units. The wavelengths typically seen by the largest micelle fall within the range of wavenumbers where the Helfrich model captures fairly well the bilayer mechanical properties. Several independent simulations allowed us to compute the micelle trajectories during an observation time smaller than the diffusive time scale (whose order of magnitude is similar to the membrane relaxation time of the largest wavelengths), this time scale being hardly accessible by experiments. From the probability density function of the micelle normal position with respect to the membrane, it is observed that the position remains close to the starting position during ≈ 0.05 τ d (where τd corresponds to the diffusion time), which allowed us to compare the negative excess of mean-square displacement (MSD) to existing theories. In that time range, the MSD exhibits different behaviors along parallel and perpendicular directions. When the micelle is sufficiently close to the bilayer (its initial distance from the bilayer equals approximately twice its gyration radius), the micelle motion becomes quickly subdiffusive in the normal direction. Moreover, the temporal evolution of the micelle MSD excess in the perpendicular direction follows that of a nanoparticle near an elastic membrane. However, in the parallel direction, the MSD excess is rather similar to that of a nanoparticle near a liquid interface. [ABSTRACT FROM AUTHOR]

Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels. [ABSTRACT FROM AUTHOR]

Interfacial water is ubiquitous on Earth, playing a crucial role in biology, chemistry, physics, materials science, and environmental science. Multiscale, hierarchical water motions on the surface of different materials under different conditions (temperature, hydration extent, pressure, etc.) and the coupling of this motion with the substrate/solute dynamics and the influence of these couplings on the material functions are complex, long lasting, interdisciplinary research topics. We herein focus on the coupling between the picosecond dynamical onset of substrates and their surface water at temperatures lower than the freezing point (273 K) and discuss the recent progress in the study of its molecular mechanisms. [ABSTRACT FROM AUTHOR]

The characteristic x-ray emission direction of a material indicates the direction of the bonding orbitals and spatial symmetry of the electron orbitals. Accordingly, the intensity of x-ray emission, which varies with the direction of emission and crystal orientation, provides crucial information regarding anisotropic electronic structures. This study utilized angle-resolved soft x-ray emission spectroscopy (SXES) on a layered material, NaAlSi, to ascertain the spatial distribution of the valence electrons. Distinct alterations in the spectral intensity distributions were observed in the Al–L2,3 and Si–L2,3 spectra with respect to the emission angle. To interpret the anisotropic SXES spectra, the spatial distribution of each valence electronic state was simulated using first-principle calculations. Although the anisotropic emission intensity could not explain the symmetry of the spatial distributions of the isolated s and d atomic orbitals, the anisotropy of the SXES spectra could be interpreted as the spatial distribution of these orbitals when hybridized with p orbitals. Furthermore, the spectral structure corresponding to the electronic states near the Fermi level reflected the characteristics of the d orbitals. Therefore, angle-resolved SXES measurements can effectively discern the spatial distribution of hybridized electron orbitals with specific energy levels, which could enhance techniques related to electron distribution analysis, with potential applications in material science and electronic structure characterization. [ABSTRACT FROM AUTHOR]

Realizing an oxide thin film with proper stoichiometry is one of the most challenging objects in materials science. Owing to the growth dynamics as well as kinetics, the physical properties of thin films often differ from their bulk counterparts. Here, we report pulsed laser-deposited thin films of LaBO3 (B = Mn, Fe, Co) grown on a SrTiO3 (001) substrate under various thermodynamic conditions. Structural, magnetic, and optical studies have been carried out. The x-ray diffraction study confirms that an appropriate choice of growth thermodynamics may help one to realize epitaxially grown films on the SrTiO3 substrate with out-of-plane lattice parameters 3.976, 3.984, and 3.825 Å for LaMnO3 (LMO), LaFeO3 (LFO), and LaCoO3 (LCO), respectively. A mixed valence state of Mn2+, 3+, 4+ for LMO, a Fe3+ state for LFO, and a mixed state of Co2+, 3+ for LCO have been confirmed by x-ray photoelectron spectroscopy, which is in good agreement with the Ellingham diagram. The optical study showed a bandgap of 1.2, 2.5, and 1.5 eV for LMO, LFO, and LCO, respectively. Ultraviolet photoelectron spectroscopy (UPS) shows a glimpse of the valence band maximum and Fermi level position. UV, UPS, and photoconductive study simultaneously results in a type II band bending, i.e., staggered type bending is observed at these interfaces. Room temperature weak ferromagnetism along with the insulating nature and a sign of photovoltaic application of these thin films fascinate to carry forward rigorous study from fundamental as well as technological points of view. [ABSTRACT FROM AUTHOR]

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. [ABSTRACT FROM AUTHOR]

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency. [ABSTRACT FROM AUTHOR]

Micelles of anionic or zwitterionic surfactants produced depletion interactions between nanofibrils leading to weak gels, while cationic surfactants adsorbed to the nanofibrils, screening repulsions leading to tough gels or phase separation. Are fundamentally and technologically interesting due to their diverse building blocks, network-supporting attractive forces, and gelation pathways. As described below, the 29 articles that appear in this issue shed new light on a wide variety of related topics, including limited valence networks, bridged colloidal networks and coagulant gels, depletant-mediated gels, I in situ i structural characterization of gel aging, mechanical and rheological properties of colloidal networks, hybrid and deformable particle assemblies, and nanocrystal gels with tunable optical response. Bridged networks and coagulant gels Colloidal networks formed by nonspecific bridging polymers represent another interesting class of gel formers, although it can be challenging to design dispersible systems with tunable polymer-colloid interactions. [Extracted from the article]

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments. [ABSTRACT FROM AUTHOR]

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies. [ABSTRACT FROM AUTHOR]

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results. In the field of material science, we study the electronic structure properties of cuprates through the quasi-1D Fermi–Hubbard model derived from CuBr2, where we find that trial wave functions with both significantly larger fidelities and lower energies over a mean-field solution do not necessarily lead to AFQMC results closer to the exact ground state energy. [ABSTRACT FROM AUTHOR]

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree–Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45 min using 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency. [ABSTRACT FROM AUTHOR]

Probing the chemistry and materials science of electrochemical energy materials is a central topic in both chemical physics and energy chemistry due to the increasingly important role of energy devices in the current and future energy system and industry. Especially, understanding the chemical physics of electrochemical energy materials is the key to enhance the performance of energy storage and conversion devices such as batteries, fuel cells, electrolyzers, and supercapacitors. This special topic focuses on the fundamental understanding of electrochemical energy applications, including electrochemistry fundamentals, structural dynamics and degradation mechanism of materials, optimization strategies for improving electrochemical performance of energy devices, and emerging simulation and characterization methods applied to advanced energy materials. [ABSTRACT FROM AUTHOR]

With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while reducing environmental impact. The BAMline, a real-life sample materials research beamline, provides unique insights into materials' electronic and chemical structure at different time and length scales. The beamline specializes in x-ray absorption spectroscopy, x-ray fluorescence spectroscopy, and tomography experiments. This enables real-time optimization of material properties and performance for various applications, such as energy transfer, energy storage, catalysis, and corrosion resistance. This paper gives an overview of the analytical methods and sample environments of the BAMline, which cover non-destructive testing experiments in materials science, chemistry, biology, medicine, and cultural heritage. We also present our own synthesis methods, processes, and equipment developed specifically for the BAMline, and we give examples of synthesized materials and their potential applications. Finally, this article discusses the future perspectives of the BAMline and its potential for further advances in sustainable materials research. [ABSTRACT FROM AUTHOR]

Using quantum computers for computational chemistry and materials science will enable us to tackle problems that are intractable on classical computers. In this paper, we show how the relative energy of defective graphene structures can be calculated by using a quantum annealer. This simple system is used to guide the reader through the steps needed to translate a chemical structure (a set of atoms) and energy model to a representation that can be implemented on quantum annealers (a set of qubits). We discuss in detail how different energy contributions can be included in the model and what their effect is on the final result. The code used to run the simulation on D-Wave quantum annealers is made available as a Jupyter Notebook. This Tutorial was designed to be a quick-start guide for the computational chemists interested in running their first quantum annealing simulations. The methodology outlined in this paper represents the foundation for simulating more complex systems, such as solid solutions and disordered systems. [ABSTRACT FROM AUTHOR]

In this paper, we show that by extending the concept of "microsphere-assisted imaging" into thermal lens (TL) detection, enhancement of photothermal detection is achieved. Furthermore, by integration of TL detection and digital holographic microscopy (DHM), we provide simultaneous measurement of photothermal properties as well as complementary imaging of the sample. Simultaneous acquisition of photothermal properties and imaging is essential for characterization of a variety of samples in biochemistry, material science, and process technologies. TL is a sensitive methodology to detect low concentrations of analytes in low-loss samples, and DHM in the transmission mode is an effective technique for label-free, non-contact, and real-time imaging and measurement of phase objects, such as biomaterials. We demonstrate the usefulness of the TL-DHM integrated system by applying it for acquisition of photothermal maps of samples as well as obtaining their morphometrical information. Additionally, we measure the thermal diffusivity of polydimethylsiloxane because of the focusing effect of polystyrene microspheres. The system has the potential to be applied for a variety of objects including bio-samples and may be proposed as a bench-top characterization device. [ABSTRACT FROM AUTHOR]

With the development of high spatial resolution spin image and magnetic field distribution measurement in atomic vapor cell, one can localize the position and calculate the magnetic moment of the field source around the cell. However, traditional cubic or spherical vapor cell can only measure the magnetic field distribution on one side of the field source, which limits the precision of the field inversion results. Here, we use a recessed atomic vapor cell to obtain field distribution around the source, which is positioned at the center of the cell. The magnetic field distributions around five sides of the field source are measured using movable stages and digital micro-mirror device. We combine the Levenberg–Marquardt algorithm with a genetic algorithm as the magnetic source localization algorithm to realize a quick global search as well as a precise local extreme point search. We obtain a spatial resolution of 219.2 × 387.0 × 451.5 μ m 3 and a magnetic field sensitivity of 0.06 nT / Hz 1 / 2 in a volume pixel of 0.0383 mm 3. The error in the localization of the magnetic source is 1.295 mm in the x direction, 185 μ m in the y direction, and 40 μ m in the z direction. The field distribution measurement method using recessed atomic vapor cell and related inversion algorithm demonstrated here have great potential applications in small scale field source detection of biology and materials science. [ABSTRACT FROM AUTHOR]

Terthiophene derivatives attract interest due to their prospective applications in optoelectronic or sensor devices. Due to their nontoxicity they can be considered as suitable candidates in biological applications. Supramolecular organization of the matter is one of the most interesting topics in contemporary materials science. Amphiphilic chromophores based on substituted terthiophenes are capable of self‐assembly into supramolecular architectures. In this work, we aim at simulation of the spectral properties of terthiophene with oligo(ethylene oxide) substituents by the methods of quantum chemistry and molecular dynamics (MD). The potential energy surface (PES) of this molecule was determined by the methods of density functional theory (DFT) for the ground state and time‐dependent density‐functional theory (TD‐DFT) for the excited state. MD simulations in water than revealed the most frequented molecular conformations in both these states. Absorption and fluorescence spectra were determined for all these conformations, including the surrounding water molecules, using TD‐DFT and averaged over the conformation space to obtain the final absorption and fluorescence spectrum. The calculated spectra were compared with their experimental counterparts and the differences were discussed in context of the supramolecular structure revealed by confocal microscopy. In spite of its simplicity, this approach provides a satisfactory approximation of absorption and fluorescent spectra of these molecules obtained by computational methods. [ABSTRACT FROM AUTHOR]

This article explores the potential of imidazo[1,2-a]pyridine-based donor-acceptor chromophores for the development of novel fluorophores and donor-acceptor chromophores. The authors conducted density functional theory calculations and proposed a modular synthesis approach to access the desired structures. The results show promising potential for the development of thermally activated delayed fluorescence emitters. The article also discusses the synthesis and characterization of a series of donor-acceptor chromophores with a 3-aminoimidazo[1,2-a]pyridine donor motif, highlighting the need for further adjustments and optimizations to improve their optoelectronic properties. The modular synthesis approach used in this study provides a versatile platform for the development of new fluorescent emitters. [Extracted from the article]

The article discusses the use of the inverse-electron-demand Diels-Alder (IEDDA) reaction as a synthetic tool for creating complex molecular structures. The authors focus on the use of bidentate Lewis acid catalysts to facilitate the IEDDA reaction between phthalazines and cyclooctynes. They found that the catalyst significantly increased the reaction rate and yield of the desired products. The authors also explored the scope of the reaction by testing different substituted phthalazines and cyclooctynes. The method presented in the article provides a convenient and efficient way to synthesize polycyclic aromatic hydrocarbons fused to eight-membered carbocycles. [Extracted from the article]

This article provides an overview of recent advancements in quinoline-based macrocycles, which are large ring-shaped compounds with various applications. The focus is on nitrogen-containing macrocycles, such as porphyrins, and their functional properties, including photophysical characteristics, host-guest chemistry, and catalytic reactions. The authors specifically discuss three quinoline-based macrocycles: TriQuinoline (TQ), oxa-TriQuinoline (o-TQ), and TEtraQuinoline (TEQ), examining their structures, synthesis, and properties. The article also explores potential applications of these macrocycles in DNA intercalation, catalytic dehydrogenative oxidation, and gem-difluoroalkenylation reactions. Furthermore, the article delves into the synthesis and properties of tetrameric quinoline macrocycles (TEQ), which have a rigid saddle-shaped structure and can form metal complexes with transition metal cations. The authors also discuss modifications of TEQ and its fluorescence properties. Additionally, the article touches on larger quinoline-based macrocycles with bridges and their synthesis, highlighting their potential applications in organometallic chemistry and supramolecular chemistry. Overall, the chemistry of quinoline macrocycles is an evolving field with opportunities for further research and applications. [Extracted from the article]

This article discusses a method for synthesizing functionalized γ-lactones using a radical-initiated photoredox cyclization process. γ-Lactones are important in organic chemistry and have diverse bioactivities. The method described in the article involves the use of 1-fluorosulfonylbenzoimidazolium triflate salts as the fluorosulfonyl radical precursor and blue LED irradiation. The reaction conditions were optimized, and a range of unsaturated carboxylic acids were successfully converted to the desired products in good yields. The article also provides chemical data for various compounds, including NMR spectra and mass spectrometry results, which may be useful for researchers studying these compounds. [Extracted from the article]

This article discusses the development of a visible-light-mediated three-component radical difunctionalization of alkenes, which allows for the synthesis of alkynyl-functionalized organoborons. The researchers optimized the reaction conditions and found that the use of specific reagents and a photocatalyst led to the desired product in good yield. The reaction was found to be chemoselective and had a broad substrate scope. The authors propose a plausible reaction mechanism based on their experimental findings. This method has potential applications in drug discovery and offers a feasible strategy for constructing alkynyl-functionalized organoborons. The text also provides detailed information about the physical and chemical properties of the product. Additionally, the document provides information on the synthesis and characterization of various chemical compounds, including derivatives of chroman and imidazole. The compounds are described in terms of their physical properties and spectroscopic data. Supporting information is available online. [Extracted from the article]

This article discusses the synthesis and spectroscopic properties of 3-/3,5-styryl-substituted BODIPY dyes with N-bridged annulation. The study focuses on modifying the BODIPY structure to enhance its properties for interdisciplinary fields like biomedicine and materials science. The authors report the successful synthesis of the desired compounds and analyze their spectroscopic properties, including absorption and emission spectra, fluorescence quantum yield, and fluorescence lifetime. The compounds exhibited high fluorescence quantum yield and red fluorescence emission, making them potentially useful in organic biological materials. Supporting information, including spectra of the new compounds, is available online. [Extracted from the article]

• Control of the iron state in iron-containing pharmaceuticals. • Mössbauer spectroscopy of iron-containing pharmaceuticals. • The iron valence state and iron-containing contaminations in pharmaceuticals. Various iron-containing medicaments, vitamins and dietary supplements are used or developed for treatment and prevention of the iron deficiency anemia which is very dangerous for human and may cause various disorders. From the other hand, blood losses, iron poor diet, microelements (co-factors) deficiency, metabolic failures, absorption problems, etc. can change the iron status and affect the health. These pharmaceuticals contain iron compounds in the ferrous and ferric states. It is known that ferrous salts are more suitable for the intestinal intake than ferric ones. On the other hand, pharmaceutically important ferritin analogues contain ferric hydrous oxides and appear to be effective for both injections and peroral administration. 57Fe Mössbauer spectroscopy is a unique physical technique which allows one to study various iron-containing materials including pharmaceuticals. Therefore, this technique was applied to study iron-containing pharmaceuticals for the analysis of the iron state, identification of ferric and ferrous compounds, revealing some structural peculiarities and for detection of aging processes in relation to the iron compounds. This review considers the main results of a long experience in the study of iron-containing pharmaceuticals by Mössbauer spectroscopy with critical analysis that may be useful for pharmacists, biochemists, biophysicists, and physicians. [ABSTRACT FROM AUTHOR]

Background: The potential effect of image noise artefacts on Digital Volume Correlation (DVC) analysis has not been thoroughly studied and, more particularly quantified, even though DVC is an emerging technique widely used in life and material science over the last decade. Objective: This paper presents the results of a sensitivity study to shed light on the effect of various noise artefacts on the full-field kinematic fields generated by DVC, both in zero and rigid body motion. Methods: Various noise artefacts were studied, including the Gaussian, Salt & Pepper, Speckle noise and embedded Ring Artefacts. A noise-free synthetic microstructure was generated using Discrete Element Modelling (DEM), representing an idealistic case, and acting as the reference dataset for the DVC analysis. Noise artefacts of various intensities (including selected extreme cases) were added to the reference image datasets using MATLAB (R2022) to form the outline of the parametric study. DVC analyses were subsequently conducted employing AVIZO (Thermo Fisher). A subset-based local approach was adopted. A three-dimensional version of the Structural Similarity Index Measure (SSIM) was used to define the similarity between the compared image datasets on each occasion. Sub-pixel rigid body motion was applied on the DEM-generated microstructure and subsequently "poisoned" with noise artefacts to evaluate mean bias and random error of the DVC analysis. Results: When the local approach is implemented, the sensitivity study on zero motion data revealed the insignificant effect of the Gaussian, Salt & Pepper, and Speckle noise on the DVC-computed kinematic field. Therefore, the presence of such noise artefacts can be neglected when DVC is executed. On the contrary, Ring Artefacts can pose a considerable challenge and therefore, DVC results need to be evaluated cautiously. A linear relationship between SSIM and the correlation index is observed for the same noise artefacts. Gaussian noise has a pronounced effect on the mean bias error for sub-pixel rigid body motion. Conclusions: Generating synthetic image datasets using DEM enabled the investigation of a variety of noise artefacts that potentially affect a DVC analysis. Given that, any microstructure – resembling the material studied – can be simulated and used for a DVC sensitivity analysis, supporting the user in appropriately evaluating the computed kinematic field. Even though the study is conducted for a two-phase material, the method elaborated in this paper also applies to heterogeneous multi-phase materials also. The conclusions drawn are valid within the environment of the AVIZO DVC extension module. Alternative DVC algorithms, utilising different approaches for the cross-correlation and the sub-pixel interpolation methods, need to be investigated. [ABSTRACT FROM AUTHOR]

In this study, a highly dense GdB 6 bulk ceramic material was fabricated using one-step in-situ reaction spark plasma sintering technology. The sample with a relative density of 96.7 % and a purity of 96.9 wt% was successfully obtained by sintering at 1800 °C for 30 min under a uniaxial pressure of 50 MPa. The Vickers hardness, elastic modulus and fracture toughness of the as-obtained GdB 6 bulk ceramics were 18.9 GPa, 209 GPa and 1.6 MPa·m1/2, respectively. The thermal conductivity of GdB 6 at room temperature was 28.68 W·m−1·K−1, 70 % of which was mainly attributed to phonon vibration, while the remaining 30 % to the electrical thermal conductivity. In addition, the as-obtained GdB 6 ceramics showed no phase transformation after heat treatment at a relatively high temperature of 2100 °C for 20 min in Ar atmosphere. The highly covalent octahedral boron framework played a critical role in maintaining the phase stability of the GdB 6 crystal lattice. This suggested that GdB 6 ceramics could be used as potential ultra-high temperature ceramics for aerospace applications and high-power vacuum electronic devices operated at high temperatures. [ABSTRACT FROM AUTHOR]

Yttrium oxide is an ideal choice for titanium aluminum alloy melting and casting, its wider use is hindered by sintering challenges and low thermal shock resistance. To address this, we propose a gel casting method for calcium oxide-doped yttrium oxide production. We pretreated yttrium oxide with phosphoric acid to prevented hydrolysis and adjusted the isoelectric point of yttrium oxide to match calcium carbonate's. Anions from calcium carbonate increased slurry solid content (up to 58 vol%) while maintaining low viscosity (57.78 mPa·s at 40 s−1 shear rate). After sintering at 1650 °C for 2 h, the material showed a flexural strength of 78 MPa, with a 90% retention rate after thermal shock test. The introduction of calcium oxide induced lattice distortion, improving mechanical properties by aiding sintering and grain boundary diffusion. XPS and STEM analysis confirmed random replacement of Y3+ by Ca2+ at the C 2 position with 1 wt% doping. [ABSTRACT FROM AUTHOR]

Rare earth compounds (RE 2 O 3), typically Eu 2 O 3 and Gd 2 O 3 , are considered as absorbing materials due to their long chain neutron absorbing ability and high efficiency of neutron absorption. In this paper, a specifically designed x Eu 2 O 3 -(1-x)Gd 2 Zr 2 O 7 (x = 0.6, 0.7, 0.8, 0.85) composite ceramics with excellent thermal properties were synthesized through direct thermal decomposition reactions followed a high-temperature sintering method under vacuum conditions. X-ray diffraction (XRD) and Raman spectroscopy analysis indicated that the obtained ceramics consisted of monoclinic Eu 2 O 3 and fluorite Gd 2 Zr 2 O 7 phases. The hardness of x Eu 2 O 3 -(1-x)Gd 2 Zr 2 O 7 ceramics (x = 0.8, 0.85) have no significantly changes in the test temperature range (25–350 °C). The thermal expansion coefficients (TECs) (7.3–10 ×10-6 K-1) are lower than Dy 2 TiO 5 (9.3–10.4 ×10-6 K-1), and the thermal expansion rate changes linearly with the increase of temperature, which indicates that the x Eu 2 O 3 -(1-x)Gd 2 Zr 2 O 7 ceramics exhibit excellent high temperature phase stability. Furthermore, the range of thermal conductivities is from 0.96 W·m-1·K-1 to 2.02 W·m-1·K-1 (25–800 °C), which is higher than Dy 2 TiO 5 (0.27–0.55 W/m·K, 220–650 °C). Compared with the state-of-art Dy 2 TiO 5 ceramics, x Eu 2 O 3 -(1-x)Gd 2 Zr 2 O 7 have superior thermal properties and are therefore expected to be the next generational of neutron absorbent materials used in ash rod. [ABSTRACT FROM AUTHOR]

This study undertakes vacuum pressureless brazing of SiC ceramics with TiSi 2 powder, achieving MAX bonding. Microscopic characterization elucidates the microstructural evolution of the joined seam across various brazing temperatures and durations. The formation process of Ti 3 SiC 2 is detailed through thermodynamic and kinetic analyses. At 1550 ℃ for 30 min or 1600 ℃ for 15 min, the filler undergoes in-situ reaction with SiC, yielding a dense, continuous Ti 3 SiC 2 (MAX) phase, with no other phases observed. This suggests that increased temperature reduces MAX synthesis time. However, prolonged brazing causes voids in the center of the joined seam. Si volatilization was key for Ti 3 SiC 2 in-situ formation, and this reaction equation was proposed. Maximum average shear strength at room temperature is 127.15 MPa at 1550 ℃ for 30 min and 118.15 MPa at 1600 ℃ for 15 min. • In-situ formation of Ti 3 SiC 2 for pressureless joining of SiC ceramics. • Explain the growth principles of Ti 3 SiC 2 layer through the Ti-Si-C ternary phase diagram and kinetic principles. • Joint shear strength: 127.15 MPa. Explain strength variations using microscopic analysis. [ABSTRACT FROM AUTHOR]

Cracks generated in sheet coating of water-based slurries are an increasingly important concern in the development of diverse material science, such as fine ceramics, Pt catalysts, and Li transition metal oxides, from an environmental safety perspective. However, a comprehensive understanding of crack formation remains elusive due to the intricately entangled interplay among various characteristics of slurries. Here, we systematically ascertain the causes at work in crack initiation by analyzing the physical parameters of a series of aqueous alumina slurries using machine learning. The analysis reveals a strong correlation between rheological hysteresis and sheet cracking. As a verification of the correlation, the crack suppression by controlling the rheological hysteresis is experimentally confirmed. Furthermore, we visualize the microstructure in the slurry that induces cracks with a liquid-phase scanning electron microscope, clarifying the rheological dynamics. These results disentangle that rheological hysteresis is a good indicator directly reflecting microstructural durability against drying stress. [Display omitted] [ABSTRACT FROM AUTHOR]

Motivated by important applications in materials science and biology, we consider a nonstationary coagulation equation with influx and outflux of particles (cargo). An exact analytical solution to this integrodifferential equation is found in a parametric form. This solution behaves as a decaying exponent with respect to the particle volume (fluorescence intensity in the case of coagulation of endosomes). We demonstrate that our exact solution substantially depends on coagulation kernels (fusion and fission rates) as well as the influx and outflux processes of cargo. [ABSTRACT FROM AUTHOR]

Recycling of carbon fiber‐reinforced polymer composites (CFRCs) based on thermosetting plastics is difficult. In the present study, high‐performance CFRCs are fabricated through complexation of aromatic pinacol‐cross‐linked polyurethane (PU−AP) thermosets with carbon fiber (CF) cloths. PU−AP thermosets exhibit a breaking strength of 95.5 MPa and toughness of 473.6 MJ m−3 and contain abundant hydrogen‐bonding groups, which can have strong adhesion with CFs. Because of the high interfacial adhesion between CF cloths and PU−AP thermosets and high toughness of PU−AP thermosets, CF/PU−AP composites possess a high tensile strength of >870 MPa. Upon heating in N,N‐dimethylacetamide (DMAc) at 100 °C, the aromatic pinacols in the CF/PU−AP composites can be cleaved, generating non‐destructive CF cloths and linear polymers that can be converted to high‐performance elastomers. The elastomers are mechanically robust, healable, reprocessable, and damage‐resistant with an extremely high tensile strength of 74.2 MPa and fracture energy of 149.6 kJ m−2. As a result, dissociation of CF/PU−AP composites enables the recovery of reusable CF cloths and high‐performance elastomers, thus realizing the upcycling of CF/PU−AP composites. [ABSTRACT FROM AUTHOR]

Recycling of carbon fiber‐reinforced polymer composites (CFRCs) based on thermosetting plastics is difficult. In the present study, high‐performance CFRCs are fabricated through complexation of aromatic pinacol‐cross‐linked polyurethane (PU−AP) thermosets with carbon fiber (CF) cloths. PU−AP thermosets exhibit a breaking strength of 95.5 MPa and toughness of 473.6 MJ m−3 and contain abundant hydrogen‐bonding groups, which can have strong adhesion with CFs. Because of the high interfacial adhesion between CF cloths and PU−AP thermosets and high toughness of PU−AP thermosets, CF/PU−AP composites possess a high tensile strength of >870 MPa. Upon heating in N,N‐dimethylacetamide (DMAc) at 100 °C, the aromatic pinacols in the CF/PU−AP composites can be cleaved, generating non‐destructive CF cloths and linear polymers that can be converted to high‐performance elastomers. The elastomers are mechanically robust, healable, reprocessable, and damage‐resistant with an extremely high tensile strength of 74.2 MPa and fracture energy of 149.6 kJ m−2. As a result, dissociation of CF/PU−AP composites enables the recovery of reusable CF cloths and high‐performance elastomers, thus realizing the upcycling of CF/PU−AP composites. [ABSTRACT FROM AUTHOR]

BACKGROUND: Material biomechanics is the investigation of the mechanical microenvironment of biomaterials through a material science approach, which belongs to the frontier discipline of sustainable development. OBJECTIVE: To explore the current research hotspots and development trends of material biomechanics and provide theoretical references for subsequent research. METHODS: Based on CiteSpace bibliometric software, we generated keyword co-occurrence and clustering, emergent word detection, and other knowledge maps based on 3 182 material biomechanics literature in the Web of Science core database during 2012-2022 for visualization analysis. RESULTS AND CONCLUSION: The number of research articles on the biomechanics of materials has been increasing in the last decade, and the United States, China, and Germany are the top three countries in terms of the number of articles published, and the research is mainly concentrated on major universities and research institutions, with significant aggregation. Material biomechanics research mainly involves the disciplines of material science, biomedical engineering, and kinesiology. Finite element analysis, material properties, mechanical performance of biomaterials, and molecular biomechanics are the focus of scholars’ attention and research. At present, the research in the field of material biomechanics focuses on the use of finite element analysis to calculate the mechanical response of different biomedical materials under different conditions of loading and mechanical properties for the prevention of sports diseases, surgical planning, and postoperative rehabilitation. The application of inorganic materials and the mode of transport of molecular signals may be the prospect of future research. [ABSTRACT FROM AUTHOR]

Rechargeable batteries, typically represented by lithium-ion batteries, have taken a huge leap in energy density over the last two decades. However, they still face material/chemical challenges in ensuring safety and long service life at temperatures beyond the optimum range, primarily due to the chemical/electrochemical instabilities of conventional liquid electrolytes against aggressive electrode reactions and temperature variation. In this regard, a gel polymer electrolyte (GPE) with its liquid components immobilized and stabilized by a solid matrix, capable of retaining almost all the advantageous natures of the liquid electrolytes and circumventing the interfacial issues that exist in the all-solid-state electrolytes, is of great significance to realize rechargeable batteries with extended working temperature range. We begin this review with the main challenges faced in the development of GPEs, based on extensive literature research and our practical experience. Then, a significant section is dedicated to the requirements and design principles of GPEs for wide-temperature applications, with special attention paid to the feasibility, cost, and environmental impact. Next, the research progress of GPEs is thoroughly reviewed according to the strategies applied. In the end, we outline some prospects of GPEs related to innovations in material sciences, advanced characterizations, artificial intelligence, and environmental impact analysis, hoping to spark new research activities that ultimately bring us a step closer to realizing wide-temperature rechargeable batteries. [ABSTRACT FROM AUTHOR]

With the advent of bioconjugation chemistry in the last two decades, highlighted by the Nobel Prize 2022, the quest for possible novel applications has been greatly intensified, broadening the prospects of these mostly simple, specific, and high‐yield reactions. The advancement of bioconjugation methods is anticipated to expand the scope of bioinstructive and bioadaptive materials science in the future. This perspective article will discuss the reactions developed in this research area over the last 10 years for coupling various biological entities such as polysaccharides, oligonucleotides, peptides, and proteins. Building on this, the impact of bioconjugation reactions in materials science and 3D printing, including their challenges and requirements is shown. Established procedures for modifying molecular structures such as Covalent and Metal Organic Frameworks (COF/MOF) or hybrid materials for biomedical applications and the scope for future research and optimization will be presented. [ABSTRACT FROM AUTHOR]

With the advent of mechanobiology, cell shape and forces have emerged as essential elements of cell behavior and fate, in addition to biochemical factors such as growth factors. Cell shape and forces are intrinsically linked to the physical properties of the environment. Extracellular stiffness guides migration of single cells and collectives as well as differentiation and developmental processes. In confined environments, cell division patterns are altered, cell death or extrusion might be initiated, and other modes of cell migration become possible. Tools from materials science such as adhesive micropatterning of soft elastic substrates or direct laser writing of 3D scaffolds have been established to control and quantify cell shape and forces in structured environments. Herein, a review is given on recent experimental and modeling advances in this field, which currently moves from single cells to cell collectives and tissue. A very exciting avenue is the combination of organoids with structured environments, because this will allow one to achieve organotypic function in a controlled setting well suited for long‐term and high‐throughput culture. [ABSTRACT FROM AUTHOR]

Molecular photoswitches are widely used in material sciences, physics, chemistry, and biology. As needs grow more complex, materials have to react more than one‐dimensionally. The use of multiple photoswitches at once opens manifold opportunities for further improved and more complicated systems. However, this requires independent addressability, i.e., orthogonality, and reversible processes. Herein, the first study on ultrafast excited state dynamics of two orthogonal photoswitches, a push‐pull azobenzene and a donor‐acceptor Stenhouse adduct is reported. In order to gain detailed insight in their interactions and mutual influences on their photoswitching behavior, they are addressed individually and simultaneously via transient absorption spectroscopy supported by quantum chemical calculations. They show reversible photoswitchability and in addition, can be used in 4D printing to provide easy access to a plethora of functional devices. Furthermore, environmental influences on the excited state dynamics are examined using different solvents and thin films. Both compounds photoisomerize independently when addressed individually or simultaneously and only little impacts on the excited state dynamics are found. Especially the vibrational relaxation is affected by different surroundings changing the energy dissipation while hardly affecting the electronic states involved. The orthogonal and simultaneous addressability is thereby crucial for their usage in 4D printed microactuators. [ABSTRACT FROM AUTHOR]

Bulk metallic glasses (BMGs) have been receiving extensive attention in the community of physics and materials science due to their attractive properties. The traditional trial-and-error approach is inefficient in designing good BMGs, then it is imperative to elaborate a prediction scheme to accelerate the development of BGMs. This work combines a composite feature selection (c-FS) and machine learning (ML) algorithm to construct a more effective strategy for predicting the critical diameter (Dmax) of BMGs. Apart from the characteristic temperatures, parameters involving domain knowledge such as thermodynamics, dynamics, and topology are also considered as the candidate features. A c-FS was devised to optimize the features, and a most efficient model was developed using the optimal subset. Our model holds the highest R2 score (0.807) compared to the 29 criteria reported. In addition, the SHapley Additive exPlanation (SHAP) approach was introduced to boost the interpretability of the model. Benefiting from our model and SHAP approach, a formation rule for large-size BMGs is uncovered. The efficacy of this model provides new guidance for the design of BMGs. [ABSTRACT FROM AUTHOR]