Experimental test of a predicted dynamics-structure-thermodynamics connection in molecularly complex glass-forming liquids.

Understanding in a unified manner the generic and chemically specific aspects of activated dynamics in diverse glass-forming liquids over 14 or more decades in time is a grand challenge in condensed matter physics, physical chemistry, and materials science and engineering. Large families of conceptually distinct models have postulated a causal connection with qualitatively different "order parameters" including various measures of structure, free volume, thermodynamic properties, short or intermediate time dynamics, and mechanical properties. Construction of a predictive theory that covers both the noncooperative and cooperative activated relaxation regimes remains elusive. Here, we test using solely experimental data a recent microscopic dynamical theory prediction that although activated relaxation is a spatially coupled local-nonlocal event with barriers quantified by local pair structure, it can also be understood based on the dimensionless compressibility via an equilibrium statistical mechanics connection between thermodynamics and structure. This prediction is found to be consistent with observations on diverse fragile molecular liquids under isobaric and isochoric conditions and provides a different conceptual view of the global relaxation map. As a corollary, a theoretical basis is established for the structural relaxation time scale growing exponentially with inverse temperature to a high power, consistent with experiments in the deeply supercooled regime. A criterion for the irrelevance of collective elasticity effects is deduced and shown to be consistent with viscous flow in low-fragility inorganic network-forming melts. Finally, implications for relaxation in the equilibrated deep glass state are briefly considered.

Negative Dielectric Constant of Water at a Metal Interface.

Water polarizability at a metal interface plays an essential role in electrochemistry. We devise a classical molecular dynamics approach with an efficient description of metal polarization and a novel ac field method to measure the local dielectric response of interfacial water. Water adlayers next to the metal surface exhibit higher-than-bulk in-plane and negative out-of-plane dielectric constants, the latter corresponding physically to overscreening of the applied field. If we account for the gap region at the interface, the average out-of-plane dielectric constant is quite low (ε_{⊥}≈2), in agreement with reported measurements on confined thin films.

Structure and Bonding in Amorphous Red Phosphorus.

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bonding in a-P from first principles, combining machine-learning and density-functional theory (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phosphorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations-for example, of a-P-based battery materials.

Activated relaxation in supercooled monodisperse atomic and polymeric WCA fluids: Simulation and ECNLE theory.

We combine simulation and Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to study the activated relaxation in monodisperse atomic and polymeric Weeks-Chandler-Andersen (WCA) liquids over a wide range of temperatures and densities in the supercooled regime under isochoric conditions. By employing novel crystal-avoiding simulations, metastable equilibrium dynamics is probed in the absence of complications associated with size polydispersity. Based on a highly accurate structural input from integral equation theory, ECNLE theory is found to describe well the simulated density and temperature dependences of the alpha relaxation time of atomic fluids using a single system-specific parameter, ac, that reflects the nonuniversal relative importance of local cage and collective elastic barriers. For polymer fluids, the explicit dynamical effect of local chain connectivity is modeled at the fundamental dynamic free energy trajectory level based on a different parameter, Nc, that quantifies the degree of intramolecular correlation of bonded segment activated barrier hopping. For the flexible chain model studied, a physically intuitive value of Nc ≈ 2 results in good agreement between simulation and theory. A direct comparison between atomic and polymeric systems reveals that chain connectivity can speed up activated segmental relaxation due to weakening of equilibrium packing correlations but can slow down relaxation due to local bonding constraints. The empirical thermodynamic scaling idea for the alpha time is found to work well at high densities or temperatures but fails when both density and temperature are low. The rich and subtle behaviors revealed from simulation for atomic and polymeric WCA fluids are all well captured by ECNLE theory.

Theory of microstructure-dependent glassy shear elasticity and dynamic localization in melt polymer nanocomposites.

We present an integrated theoretical study of the structure, thermodynamic properties, dynamic localization, and glassy shear modulus of melt polymer nanocomposites (PNCs) that spans the three microstructural regimes of entropic depletion induced nanoparticle (NP) clustering, discrete adsorbed layer driven NP dispersion, and polymer-mediated bridging network. The evolution of equilibrium and dynamic properties with NP loading, total packing fraction, and strength of interfacial attraction is systematically studied based on a minimalist model. Structural predictions of polymer reference interaction site model integral equation theory are employed to establish the rich behavior of the interfacial cohesive force density, surface excess, and a measure of free volume as a function of PNC variables. The glassy dynamic shear modulus is predicted to be softened, reinforced, or hardly changed relative to the pure polymer melt depending on system parameters, as a result of the competing and qualitatively different influences of interfacial cohesion (physical bonding), free volume, and entropic depletion on dynamic localization and shear elasticity. The localization of polymer segments is the dominant factor in determining bulk PNC softening and reinforcement effects for moderate to strong interfacial attractions, respectively. While in the athermal entropy-dominated regime, the primary origin of mechanical reinforcement is the stress stored in the aggregated NP subsystem. The PNC shear modulus is often qualitatively correlated with the segment localization length but with notable exceptions. The present work provides the foundation for developing a theory of segmental relaxation, Tg changes, and collective NP dynamics in PNCs based on a self-consistent treatment of the cooperative activated motions of segments and NPs.

Local structure, thermodynamics, and phase behavior of asymmetric particle mixtures: Comparison between integral equation theories and simulation.

We study the structural pair correlations, thermodynamics, and fluid-fluid demixing phase behavior of dense binary sphere mixtures as predicted by integral equation theories with diverse closure approximations. The focus is on mixtures with a large size asymmetry over a wide range of compositions and strengths of interparticle attractive interactions with an emphasis on the nonperturbative strong bridging or network forming regime. Quantitative comparisons with simulations are carried out. At high volume fractions of the larger species, we find that all studied closures are reasonably good. However, large quantitative or even qualitative discrepancies compared with simulations emerge when the large species is the volumetrically minority component, under both entropic depletion and strong enthalpic bridging conditions. Overall, we find that using the modified-Verlet (MV) closure approximation for all three correlation functions leads to good predictions for structure, phase behavior, and the equation-of-state, along with assuring pair correlation functions which are rigorously positive. This symmetric or "triple MV" approximation has the advantage that the same closure can be used for any size ratio in all thermodynamic state regimes, in contrast to asymmetric closures. The good accuracy of the triple MV closure for particle mixtures provides as basis for developing improved theoretical descriptions of polymer nanocomposites and will serve as a crucial input to microscopic theories of slow dynamics in glass and gel forming systems.

A geometrical criterion for glass transition in soft-sphere fluids.

As glass-forming fluids become colder and denser, structural rearrangements become slow and eventually cease. For hard-sphere fluids, percolation of particles unable to change neighbors (T1-inactive particles) signals the glass transition. To investigate this geometrical criterion for mobility in soft-sphere systems, we simulate monodisperse fluids interacting with a generalized Weeks-Chandler-Andersen (WCA) potential in metastable equilibrium, using our previously developed crystal-avoiding method. We find that the vanishing diffusivity as the glass transition is approached can be described by a power law below the onset temperature of super-Arrhenius behavior. By mapping the soft spheres to hard spheres based on mean collision energy, we find that the diffusivity versus effective volume fraction curves collapse onto the hard-sphere curve for all systems studied. We find that the onset of super-Arrhenius behavior and the MCT dynamic glass transition correlate well with temperature when the T1-inactive particles form clusters of two particles on average and when the T1-inactive clusters percolate the entire system, respectively. Our findings provide new insight into the structural origin of glassy dynamics.

Structural entropy of glassy systems from graph isomorphism.

Configurational entropy plays a central role in thermodynamic scenarios of the glass transition. As a measure of the number of basins in the potential energy landscape, configurational entropy for a glass-forming liquid can be evaluated by explicitly counting distinct inherent structures. In this work, we propose a graph-theory based method to examine local structure and obtain the corresponding entropy of hard-particle systems. Voronoi diagrams of associated clusters are classified using a graph isomorphism algorithm. The statistics of these clusters reveal structural motifs such as icosahedron-like order, and also allow us to calculate the structural entropy SG. We find the structural entropy of an n-particle subsystem grows linearly with n. Thus the structural entropy per particle can be obtained from the slope dSG/dn. Our results are consistent with previous values for configurational entropy obtained via thermodynamic integration. Structural entropies per particle are measured for hard-disk and hard-sphere polydisperse systems, and extrapolated for monodisperse hard disks, all of which are nonzero at the dynamic glass transition.

Static lengths in glass-forming monodisperse hard-sphere fluids from periodic array pinning.

We explore the static length in glass-forming hard-sphere liquids revealed by the response of dynamical properties (diffusion coefficient D and α relaxation time τα) to a regular array of pinned particles. By assuming a universal scaling form, we find data can be excellently collapsed onto a master curve, from which relative length scales can be extracted. By exploiting a crystal-avoiding simulation method that suppresses crystallization while preserving dynamics, we can study monodisperse as well as polydisperse systems. The static length obtained from dynamical property Q (τα and D) scales as log Q ∼ ξ, with ψ ≈ 1.

T1 process and dynamics in glass-forming hard-sphere liquids.

To study the relationship between dynamics and structure in a glass-forming liquid, we introduce a purely geometric criterion for locally mobile particles in a dense hard-sphere fluid: namely, "T1-active" particles, which can gain or lose at least one Voronoi neighbor by moving within their free volume with other particles fixed. We obtain geometrical and dynamical properties for monodisperse hard-sphere fluids with 0.40 < ϕ < 0.64 using a "crystal-avoiding" MD simulation that effectively suppresses crystallization without altering the dynamics. We find that the fraction of T1-active particles vanishes at random close packing, while the percolation threshold of T1-inactive particles is essentially identical to the commonly identified hard-sphere glass transition, ϕg ≈ 0.585.

Identification of drug use degree by integrating multi-modal features with dual-input deep learning method.

Most of studies on drug use degree are based on subjective judgments without objective quantitative assessment, in this paper, a dual-input bimodal fusion algorithm is proposed to study drug use degree by using electroencephalogram (EEG) and near-infrared spectroscopy (NIRS). Firstly, this paper uses the optimized dual-input multi-modal TiCBnet for extracting the deep encoding features of the bimodal signal, then fuses and screens the features using different methods, and finally fused deep encoding features are classified. The classification accuracy of bimodal is found to be higher than that of single modal, and the classification accuracy is up to 89.9%.

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module.

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380-1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Hyperparameter Optimization for Atomic Cluster Expansion Potentials.

Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb2Te3. More generally, our work emphasizes the importance of hyperparameter selection in the development of advanced ML potential models.

Cluster-Aware Grid Layout.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution.

Spinal cord injury (SCI) profoundly affects an individual's ability to move. Fortunately, recent advancements in neuromodulation, particularly the spatio-temporal epidural electrical stimulation (EES) targeting the spinal nerve roots, promoted rapid rehabilitation of SCI patients. Such neuromodulation techniques require precise anatomical modelling of spinal cord. However, the lack of spine imaging datasets, especially high-quality magnetic resonance imaging (MRI) datasets highlighting nerve roots, hinders the translation of EES into medical practice. To address this problem, we introduce an open-access lumbosacral spine MRI dataset acquired in 14 healthy adults, using constructive interference in steady state (CISS) sequence, double echo steady state (DESS) sequence, and T2-weight turbo spin echo (T2-TSE) sequence, with enhanced nerve root resolution. The dataset also includes the corresponding anatomical annotations of nerve roots and the final reconstructed 3D spinal cord models. The quality of our dataset is assessed using image quality metrics implemented in MRI quality control tool (MRIQC). Our dataset provides a valuable platform to promote a wide range of spinal cord neuromodulation research and collaboration among neurorehabilitation engineers.

Discovery of a Meisoindigo-Derived PROTAC as the ATM Degrader: Revolutionizing Colorectal Cancer Therapy via Synthetic Lethality with ATR Inhibitors.

Meisoindigo (Mei) has long been recognized in chronic myeloid leukemia (CML) treatment. To elucidate its molecular target and mechanisms, we embarked on designing and synthesizing a series of Mei-derived PROTACs. Through this endeavor, VHL-type PROTAC 9b was identified to be highly cytotoxic against SW620, SW480, and K562 cells. Employing DiaPASEF-based quantitative proteomic analysis, in combination with extensive validation assays, we unveiled that 9b potently and selectively degraded ATM across SW620 and SW480 cells in a ubiquitin-proteasome-dependent manner. 9b-induced selective ATM degradation prompted DNA damage response cascades, thereby leading to the cell cycle arrest and cell apoptosis. This pioneering discovery renders the advent of ATM degradation for anti-cancer therapy. Notably, 9b-induced ATM degradation synergistically enhanced the efficacy of ATR inhibitor AZD6738 both in vitro and in vivo. This work establishes the synthetic lethality-inducing properties of ATR inhibitors in the ATM-deficient context, thereby providing new avenues to innovative therapies for colorectal cancer.

Detection and Genomic Characterization of Bovine Rhinitis Virus in China.

Bovine rhinitis virus (BRV) is an etiological agent of bovine respiratory disease complex (BRDC) and can be divided into two genotypes-bovine rhinitis A virus (BRAV) and bovine rhinitis B virus (BRBV). However, knowledge about the prevalence and molecular information of BRV in China is still limited. In this study, 163 deep nasal swabs collected from bovines with BRDC syndrome on 16 farms across nine provinces of China were tested for BRAV and BRBV by a duplex real-time RT-PCR assay. The results showed that 28.22% (46/163) of the samples were BRV-positive, and the positive rates were 22.09% (36/163) for BRAV and 9.2% (15/163) for BRBV. The co-circulation of both BRV genotypes was observed on two farms. Furthermore, five near-complete BRV genomes, including three BRAVs and two BRBVs, were obtained. The phylogenetic analysis showed that the three obtained BRAVs were phylogenetically independent, while the two BRBVs exhibited significant genetic heterogeneity. Recombination analysis revealed that three BRAVs and one BRBV strain obtained in this study were recombinants. The present study confirmed the presence and prevalence of BRAV in China, and it found that both types of BRV are circulating in beef cattle, which contributes to a better understanding of the prevalence and molecular characteristics of BRV.

Synthesis and characterization of cellulose nanocrystal-Fe composite nanoparticles and their digestion behavior in simulated gastric fluid.

Cellulose nanocrystals (CNC) exhibit great potential as a food emulsifier or functional material template. Herein, CNC-Fe nanoparticles were successfully prepared via an in situ chemical reduction approach. Zeta potential measurements, low-field nuclear magnetic resonance spectroscopy, and atomic force microscopy showed that Fe(III) ions were adsorbed onto CNC when FeCl3 was added to a CNC dispersion. Micromorphological analysis revealed small (diameter = 10.0 ± 2.4 nm) spherical nanoparticles synthesized on the surface of aggregated CNC after the reduction of the Fe(III) ions. Fourier transform infrared spectroscopy revealed an intense peak at 779 cm-1 in the CNC-Fe nanoparticles, which was attributed to FeO stretching vibrations. X-ray photoelectron spectroscopy indicated that the valence state of Fe in CNC-Fe nanoparticles was predominantly ferrous. The synthesized CNC-Fe nanoparticles demonstrated excellent colloidal stability in a dispersion for 21 d and complete, rapid, and spontaneous dissolution in vitro simulated gastric fluid. Our results highlight the potential use of CNC as a template for loading Fe into nanoparticles for Fe fortification in food.

Surface effects on the crystallization kinetics of amorphous antimony.

Elemental antimony (Sb) is regarded as a promising candidate to improve the programming consistency and cycling endurance of phase-change memory and neuro-inspired computing devices. Although bulk amorphous Sb crystallizes spontaneously, the stability of the amorphous form can be greatly increased by reducing the thickness of thin films down to several nanometers, either with or without capping layers. Computational and experimental studies have explained the depressed crystallization kinetics caused by capping and interfacial confinement; however, it is unclear why amorphous Sb thin films remain stable even in the absence of capping layers. In this work, we carry out thorough ab initio molecular dynamics (AIMD) simulations to investigate the effects of free surfaces on the crystallization kinetics of amorphous Sb. We reveal a stark contrast in the crystallization behavior between bulk and surface models at 450 K, which stems from deviations from the bulk structural features in the regions approaching the surfaces. The presence of free surfaces intrinsically tends to create a sub-nanometer region where crystallization is suppressed, which impedes the incubation process and thus constrains the nucleation in two dimensions, stabilizing the amorphous phase in thin-film Sb-based memory devices.

Tailoring the optical and mechanical properties of cellulose nanocrystal film by sugar alcohols and its pH/humidity-responsive behavior.

Cellulose nanocrystals (CNC) have gained widespread attention in intelligent food packaging because of their iridescent optical properties. Here, we report a CNC composite film employing CNC, sugar alcohols (e.g., maltol, erythritol, mannitol, sorbitol, and xylitol) and natural pigment anthocyanins, which has a special iridescent color that can be used as a pH and humidity sensor. The effects of five sugar alcohols with different addition ratios on the structural, optical, and mechanical properties of the CNC films were investigated. The results demonstrated that the addition of sugar alcohol made composite films exhibiting a red-shift of λmax, a more uniform color in visual observation, and a larger pitch. Among them, the CNC-mannitol composite film with a ratio of 10:1 exhibited the best mechanical properties, possessing a tensile stress strength of 57 MPa and toughness of 137 J/m3. Subsequently, anthocyanins were incorporated to this composite film, which showed a marked color change along with the pH from 2 to 12 and exhibited a reversible color change from red to transparent upon a relative humidity change from 35 % to 85 %. Overall, such multi-environment-responsive iridescent films with excellent mechanical properties have a great potential for use in intelligent food packaging applications.