Browse Articles

Security verification framework for NDN access control

Exact factorization method for bound vibrational states: An analytical tool for accurate approximations

The Exact Factorization (XF) method represents an interesting formulation of the Schrödinger equation where subsystem wavefunctions are exactly coupled. Here, I show that the XF method can be employed as an analytical tool to study the quantum vibrational problem of bound systems. In particular, after elaborating suitable XF-based wavefunction Ansätze, the ground-state energy approximated expression for bilinearly and quartically coupled harmonic oscillators is estimated. The XF-based analytical solution is compared with adiabatic and perturbative ones, and it is usually found to be an order of magnitude more accurate than these for estimating the anharmonic and coupling correction part of the ground-state energy. This procedure will possibly increase the numerical stability and accuracy of perturbative or Hartree-product based methods when applied to bound state calculations.

AlphaFold prediction of structural ensembles of disordered proteins

Abstract Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.

Two-dimensional transition-metal halogenides with Mexican-hat-shaped band as a correlated topological insulator

Graphene, the atomic layer of carbon, is one of the most intensely studied objects since it was isolated for the first time in 2004. However, its Dirac bands are made up of pz orbitals, which creates certain limitations in correlated physics. Here, we replace carbon with transition-metal atoms (M: Ti, Zr, and Hf) to form a corrugated honeycomb lattice. Moreover, both the topside and downside are passivized by halogen atoms (X: F, Cl, Br, and I), making sure that all of them are thermodynamically stable. Due to the irregularly octahedral crystalline field on M-d orbitals, two energy bands derived from dxy and dx2−y2 orbitals intersect each other at the center of the Brillouin zone. Unlike graphene, this intersection is not conical but bell-shaped, further showing the Mexican-hat-shaped dispersion slightly higher than the Fermi level for TiCl, TiBr, and MI (M: Ti, Zr, and Hf). Moreover, the Mexican-hat coefficient could be controllably modulated by the biaxial strain. More interestingly, these intersecting points are robust on the on-site Coulomb interaction, but could be split by the spin-orbital coupling (SOC). Due to the strong SOC strength of correlated dxy and dx2−y2 orbitals, the gap induced by SOC is three orders of magnitude larger than that of pz orbitals in graphene. Furthermore, the topological invariant and edge-states spectrum are calculated to suggest that transition-metal halogenides are non-trivial topological insulators with the topological invariant Z2=1.

In vitro and in vivo assessment of a new acellular human amnion/chorion membrane device for guided bone regeneration

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity often makes it challenging to address potential finite-size effects. Here, we present a specialized graphics processing units (GPUs) implementation of PIMD methods, including ring-polymer molecular dynamics (RPMD) and thermostatted ring-polymer molecular dynamics (TRPMD), into the open-source Graphics Processing Units Molecular Dynamics (GPUMD) package, combined with highly accurate and efficient machine-learned neuroevolution potential (NEP) models. This approach achieves almost the accuracy of first-principles calculations with the computational efficiency of empirical potentials, enabling large-scale atomistic simulations that incorporate nuclear quantum effects, effectively overcoming finite-size limitations at a relatively affordable computational cost. We validate and demonstrate the efficacy of the combined NEP-PIMD approach by examining various thermal properties of diverse materials, including lithium hydride (LiH), three porous metal–organic frameworks (MOFs), liquid water, and elemental aluminum. For LiH, our NEP-PIMD simulations successfully capture the isotope effect, reproducing the experimentally observed dependence of the lattice parameter on the reduced mass. For MOFs, our results reveal that achieving good agreement with experimental data requires consideration of both nuclear quantum effects and dispersive interactions. For water, our PIMD simulations capture the significant impact of nuclear quantum effects on its microscopic structure. For aluminum, the TRPMD method effectively captures thermal expansion and phonon properties, aligning well with quantum mechanical predictions. This efficient GPU-accelerated NEP-PIMD implementation in the GPUMD package provides an alternative, accessible, accurate, and scalable tool for exploring complex material properties influenced by nuclear quantum effects, with potential applications across a broad range of materials.

Structural basis of anticancer drug recognition and amino acid transport by LAT1

Abstract LAT1 (SLC7A5) transports large neutral amino acids and plays pivotal roles in cancer proliferation, immune response and drug delivery. Despite recent advances in structural understanding of LAT1, how it discriminates substrates and inhibitors including the clinically relevant drugs remains elusive. Here we report six structures of LAT1 across three conformations with bound ligands, elucidating its substrate transport and inhibitory mechanisms. JPH203 (also known as nanvuranlat or KYT-0353), an anticancer drug in clinical trials, traps LAT1 in an outward-facing state with a U-shaped conformer, with its amino-phenylbenzoxazol moiety pushing against transmembrane helix 3 (TM3) and bending TM10. Physiological substrates like ʟ-Phe lack such effects, whereas melphalan poses steric hindrance, explaining its inhibitory activity. The “classical” system L inhibitor BCH induces an occluded state critical for transport, confirming its substrate-like behavior. These findings provide a structural basis for substrate recognition and inhibition of LAT1, guiding future drug design.

Broadband metamaterial absorber in the C–Ku bands by exploiting FeCo–C

In this research, we have designed a hybrid metamaterial absorber (HMA) based magnetic material from C to the Ku-band. The magnetic material was prepared by the ball milling method with differences in the mass ratio of the FeCo alloy to C. The HMA structure consists of four layers: FeCo–C, metallic structure (two punched-in rings with four L-shape in the edges), dielectric (FR-4), and continuous metallic layer. The widest absorption spectrum of structure based FeCo–C (75%C) in a frequency range of 6.9–16.8 GHz with absorption exceeding 90%. The absorption mechanism has also been carefully analyzed through the distribution of surface current and power loss density. These findings may advance the design of devices leveraging metamaterials, including applications in stealth radar technology, electromagnetic shielding for health and safety, and reduction of electromagnetic interference for high-performance electronic and communication systems.

Biometry-based verification system with symmetric key generation method for internet of things environments

Simulating phase inversion processes by coupled map lattice: Toward the theoretical design of food texture and quality in dairy processing from fresh cream to butter via whipped cream

We present a theoretical model and simulation for the formation dynamics of diverse texture patterns that emerge spontaneously or self-organize during phase inversion processes of fresh cream by mechanical whipping. The results suggest that the model should be applied for theoretically designing the texture and quality of whipped cream and butter products. The modeling complexity in phase inversion processes, from fresh cream via whipped cream to butter, was overcome by using a well-established complex systems approach, the coupled map lattice (CML). The proposed CML consists of a minimal set of procedures (i.e., parameterized nonlinear maps), such as whipping, coalescence, and flocculation, acting on the appropriately coarse-grained field variables, surface energy, cohesive energy, and velocity (flow) of the emulsion defined on a two-dimensional square lattice. In the CML simulations, two well-known and different phase inversion processes are reproduced at high and low whipping temperatures. The overrun and viscosity changes simulated in these processes are at least qualitatively consistent with those observed in experiments. We characterize these processes exhibiting different texture patterns as the viscosity dominance at high whipping temperatures and as the overrun dominance at low whipping temperatures on the viscosity–overrun plane, which is one of the state diagrams.

Proteasomes accumulate in the plant apoplast where they participate in microbe-associated molecular pattern (MAMP)-triggered pathogen defense

Abstract Akin to mammalian extracellular fluids, the plant apoplastic fluid (APF) contains a unique collection of proteins, RNAs, and vesicles that drive many physiological processes ranging from cell wall assembly to defense against environmental challenges. Using an improved method to enrich for the Arabidopsis APF, we better define its composition and discover that the APF harbors active proteasomes though microscopic detection, proteasome-specific activity and immunological assays, and mass spectrometry showing selective enrichment of the core protease. Functional analysis of extracellular (ex)-proteasomes reveals that they help promote basal pathogen defense through proteolytic release of microbe-associated molecular patterns (MAMPs) such as flg22 from bacterial flagellin that induce protective reactive-oxygen-species (ROS) bursts. Flagellin-triggered ROS is also strongly suppressed by the enigmatic Pseudomonas syringae virulence effector syringolin-A that blocks ex-proteasome activity. Collectively, we provide a deep catalog of apoplast proteins and evidence that ex-proteasomes participate in the evolving arms race between pathogens and their plant hosts.

Atomic layer deposition of HfO2 as a charge-lean capping layer material for SiO2-modulation acceptor doping of silicon

Modulation doping of SiO2 by Al-induced acceptor states is a promising alternative to conventional impurity doping for silicon nanostructures, enabling the introduction of free holes in Si without direct impurity incorporation into the lattice. SiO2 modulation doping of Si is achieved by a short high-temperature anneal of a tunnel-SiO2 layer coated with an AlOx monolayer by atomic layer deposition (ALD). However, this ultra-thin modulation doping (MD) stack is highly susceptible to degradation when exposed to ambient air. In this work, we investigated ALD hafnium oxide (HfO2) as a reliable, charge-lean capping layer to protect the MD stack and preserve its doping properties. We optimized the ALD-HfO2 deposition process using tetrakis(ethylmethylamino)hafnium (TEMAHf) and various oxygen co-reactants (H2O, O3, O2-plasma) as well as different deposition temperatures and studied the effects of post-deposition RTA. Thermal ALD with H2O as the oxygen reactant at 200 °C yields HfO2 films with superior electrical properties, including low positive fixed charge densities (<1 × 1012 cm−2), minimal hysteresis, and high permittivity (ɛ = 17). The optimized HfO2 film was successfully integrated into modulation-doped metal–oxide–semiconductor capacitors (MD MOS-caps) to evaluate their effectiveness as capping layers. Notably, in situ capping, where the HfO2 layer is deposited immediately after the AlOX without breaking vacuum, results in higher modulation doping efficiency and demonstrated higher negative fixed charge densities compared to ex situ capping. These findings demonstrate that optimized ALD-HfO2 can serve as a charge-lean capping material, enhancing the stability and performance of modulation-doped Si nanostructures by effectively protecting the ultra-thin MD stack from ambient degradation.

Gait impairment associated with neuroimaging biomarkers in Alzheimer’s disease

Isolating non-adiabatically enhanced ground state quantum beats through two-dimensional electronic spectroscopy

Resonant vibrational–electronic (vibronic) couplings in donor–acceptor systems may play a crucial role in driving non-adiabatic internal conversion reported in natural photosynthesis, organic photovoltaic polymers, and singlet exciton fission. Quantum beats arising from impulsive excitation are often employed as spectroscopic reporters of the specific vibrational modes driving this process. However, distinguishing these promoter modes from spectator modes, which do not participate in vibronic mixing and simply accompany ultrafast internal conversion, remains a challenge. This is so because vibrational quantum beats arising from uncoupled monomers can modulate pump–probe transients by themselves. In this paper, we show that vibronic mixing induces quantum beats whose amplitude is anisotropic with respect to the polarization of the light. We propose a readily implementable polarization-controlled two-dimensional electronic spectroscopy experiment to uniquely identify signatures of excited state vibronic resonance using ground state quantum beats by discriminating against vibrational motions (and corresponding quantum beats) that are simply spectators. Through analytical expressions and simulation of two-dimensional electronic spectra, we show that the resulting 2D spectra are expected to exhibit distinct spectral lineshapes with a strong temperature dependence that arises solely due to the excited state vibronic mixing. Our findings suggest an interesting experiment to decipher the presence of excited state vibronic resonances.

Failure to replicate a superiority effect in crowding

Enhanced exploration of LiF–NaF thermal conductivity through transferable equivariant graph neural networks

Although molten salt reactors and thermal storage systems are attracting increasing interest, our understanding of the physicochemical properties of molten salts is still incomplete. This is largely due to the difficulty of conducting experiments under extreme temperatures with strict control of impurities and corrosion. Ab initio calculations, machine-learned force fields, and classical molecular dynamics have helped to alleviate some of these issues. However, discrepancies between experimental and theoretical computations of the thermal conductivity of fluoride molten salts have become of increasing concern. In this paper, we present a modernized method for training a transferable equivariant graph neural network force fields to model a simple fluoride molten salt system, LiF–NaF, using minimal ab initio calculations. Using this transferable machine-learned force field, the thermal conductivity as well as various other functions of LiF–NaF were computed at various chemical temperatures and ratios in order to gain new insights into the limitations and behaviors of molten salts in relation to their thermal conductivity. Results show discrepancies between experimental and theoretical computations of the thermal conductivity as a function of temperature but good agreement between experimental and theoretical computations of the thermal conductivity as a function of ratio. Secondary results show compelling agreement of a machine-learned force field with first-principles computations and the ability to interpolate and extrapolate various chemical ratios.

Allosteric modulation and direct activation of glycine receptors by a tricyclic sulfonamide

Ionic association and Wien effect in 2D confined electrolytes

Recent experimental advances in nanofluidics have allowed to explore ion transport across molecular-scale pores, in particular, for iontronic applications. Two-dimensional nanochannels—in which a single molecular layer of electrolyte is confined between solid walls—constitute a unique platform to investigate fluid and ion transport in extreme confinement, highlighting unconventional transport properties. In this work, we study ionic association in 2D nanochannels, and its consequences on non-linear ionic transport, using both molecular dynamics simulations and analytical theory. We show that under sufficient confinement, ions assemble into pairs or larger clusters in a process analogous to a Kosterlitz–Thouless transition, here modified by the dielectric confinement. We further show that the breaking of pairs results in an electric-field dependent conduction, a mechanism usually known as the second Wien effect. However the 2D nature of the system results in non-universal, temperature-dependent, scaling of the conductivity with electric field, leading to ionic coulomb blockade in some regimes. A 2D generalization of the Onsager theory fully accounts for the non-linear transport. These results suggest ways to exploit electrostatic interactions between ions to build new nanofluidic devices.

Elliptical ejecta of asteroid Dimorphos is due to its surface curvature

Experimental and numerical study of stress wave generation and attenuation in copper during laser shock peening

A physically based model of laser shock peening is established and experimentally verified. The laser-induced generation of stress wave in the confined geometry is considered directly through the heating and evaporation of the surface layer of copper described by a wide-range equation of state. The structure and attenuation of the stress wave is described by the dislocation plasticity model. In the experimental part, copper plates of three different thicknesses (0.5, 0.8, and 1.0 mm) were irradiated by 11-ns (FWHM) 1064-nm laser with energy densities of 64, 95, 127, and 191 J/cm2, and the back free surface velocity histories were registered by means of photonic Doppler velocimetry. Consideration of different plate thicknesses allows us to decouple the effects of stress wave generation and attenuation and to verify independently the corresponding parts of the model. It is shown that the widely used Fabbro's model tends to underestimate the interface pressure pulse in copper because the stationary plasma expansion assumed in this model is established only after 30–60 ns of laser irradiation with a constant power density. The efficiency value of φ=1 in Fabbro's model is optimal to reproduce the interface pressure pulse at nanosecond irradiation in contrast to the efficiency value of φ=0.5, which is optimal to estimate the stationary level of pressure established for constant power density.