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We present MISTIQS, a Multiplatform Software for Time-dependent Quantum Simulations. MISTIQS delivers end-to-end functionality for simulating the quantum many-body dynamics of systems governed by time-dependent Heisenberg Hamiltonians across multiple quantum computing platforms. It provides high-level programming functionality for generating intermediate representations of quantum circuits which can be translated into a variety of industry-standard representations. Furthermore, it offers a selection of circuit compilation and optimization methods and facilitates execution of the quantum circuits on currently available cloud-based quantum computing backends. MISTIQS serves as an accessible and highly flexible research and education platform, allowing a broader community of scientists and students to perform quantum many-body dynamics simulations on current quantum computers.

High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized nanoscale excitations. Recently, strong field excitation of dielectric nanoparticles has emerged as an avenue for studying catalysis in highly ionized environments, producing extreme electric fields. While the dynamics of laser-driven surface ion emission has been extensively explored, understanding the molecular dynamics leading to fragmentation has remained elusive. Here, we employ a multiscale approach performing nonadiabatic quantum molecular dynamics (NAQMD) simulations on hydrogenated silica surfaces in both bare and wetted environments under field conditions mimicking those of an ionized nanoparticle. Our findings indicate that hole localization drives fragmentation dynamics, leading to surface silanol dissociation within 50 fs and charge transfer-induced water splitting in wetted environments within 150 fs. Further insight into such ultrafast mechanisms is critical for the advancement of catalysis on the surface of charged nanosystems.

Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses great challenges in bridging the slow diffusion process and material failures. To tackle this problem, we propose an AI-guided long-term atomistic simulation approach: molecular autonomous pathfinder (MAP) framework based on deep reinforcement learning (DRL), where the RL agent is trained to uncover energy efficient diffusion pathways. We employ a Deep Q-Network architecture with distributed prioritized replay buffer, enabling fully online agent training with accelerated experience sampling by an ensemble of asynchronous agents. After training, the agents provide atomistic configurations of diffusion pathways with their energy profile. We use a piecewise nudged elastic band to refine the energy profile of the obtained pathway and the corresponding diffusion time on the basis of transition-state theory. With the MAP framework, we demonstrate atomistic diffusion mechanisms in amorphous silica with time scales comparable to experiments.

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+

Surface transfer doping is proposed to be a potential solution for doping diamond, which is hard to dope for applications in high-power electronics. While MoO 3 is found to be an effective surface electron acceptor for hydrogen-terminated diamond with a negative electron affinity, the effects of commonly existing oxygen vacancies remain elusive. We have performed reactive molecular dynamics simulations to study the deposition of MoO 3- x on a hydrogenated diamond (111) surface and used first-principles calculations based on density functional theory to investigate the electronic structures and charge transfer mechanisms. We find that MoO 3- x is an effective surface electron acceptor and the spatial extent of doped holes in hydrogenated diamond is extended, promoting excellent transport properties. Charge transfer is found to monotonically decrease with the level of oxygen vacancy, providing guidance for engineering of the surface transfer doping process.

Iodine oxides I 2 O y ( y = 4, 5, 6) crystallize into atypical structures that fall between molecular- and framework-base types and exhibit high reactivity in an ambient environment, a property highly desired in the so-called "agent defeat materials". Inelastic neutron scattering experiments were performed to determine the phonon density of states of the newly synthesized I 2 O 5 and I 2 O 6 samples. First-principles calculations were carried out for I 2 O 4 , I 2 O 5 , and I 2 O 6 to predict their thermodynamic properties and phonon density of states. Comparison of the INS data with the Raman and infrared measurements as well as the first-principles calculations sheds light on their distinctive, anisotropic thermomechanical properties.

We have developed an extension of the Neural Network Quantum Molecular Dynamics (NNQMD) simulation method to incorporate electric-field dynamics based on Born effective charge (BEC), called NNQMD-BEC. We first validate NNQMD-BEC for the switching mechanisms of archetypal ferroelectric PbTiO 3 bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching. In triaxially strained PbTiO 3 with strain conditions commonly seen in many superlattice configurations, we find that flux-closure texture can be induced with application of an electric field perpendicular to the original polarization direction. Upon field reversal, the flux-closure texture switches via a pair of transient vortices as the intermediate state, indicating an energy-efficient switching pathway. Our NNQMD-BEC method provides a theoretical guidance to study electro-mechano effects with existing machine learning force fields using a simple BEC extension, which will be relevant for engineering applications such as field-controlled switching in mechanically strained ferroelectric devices.

Effects of lateral compression on out-of-plane deformation of two-dimensional MoSe 2 layers are investigated. A MoSe 2 monolayer develops periodic wrinkles under uniaxial compression and Miura-Ori patterns under biaxial compression. When a flat MoSe 2 monolayer is placed on top of a wrinkled MoSe 2 layer, the van der Waals (vdW) interaction transforms wrinkles into ridges and generates mixed 2H and 1T phases and chain-like defects. Under a biaxial strain, the vdW interaction induces regions of Miura-Ori patterns in bilayers. Strained systems analyzed using a convolutional neural network show that the compressed system consists of semiconducting 2H and metallic 1T phases. The energetics, mechanical response, defect structure, and dynamics are analyzed as bilayers undergo wrinkle-ridge transformations under uniaxial compression and moiré transformations under biaxial compression. Our results indicate that in-plane compression can induce self-assembly of out-of-plane metasurfaces with controllable semiconducting and metallic phases and moiré patterns with unique optoelectronic properties.

Mechanical controllability of recently discovered topological defects ( e.g ., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO 3 and PbTiO 3 , we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.

The nature of hydrogen bonding in condensed ammonia phases, liquid and crystalline ammonia has been a topic of much investigation. Here, we use quantum molecular dynamics simulations to investigate hydrogen bond structure and lifetimes in two ammonia phases: liquid ammonia and crystalline ammonia-I. Unlike liquid water, which has two covalently bonded hydrogen and two hydrogen bonds per oxygen atom, each nitrogen atom in liquid ammonia is found to have only one hydrogen bond at 2.24 Å. The computed lifetime of the hydrogen bond is t ≅ 0.1 ps. In contrast to crystalline water-ice, we find that hydrogen bonding is practically nonexistent in crystalline ammonia-I.

The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager-Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the "ground-truth" molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ 38 from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.

Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.

A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe 2 glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe 2 glass at 10 K and melt at 1100 K.

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP - optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.

The typical layered transition metal dichalcogenide (TMDC) material, MoS 2 , is considered a promising candidate for the next-generation electronic device due to its exceptional physical and chemical properties. In chemical vapor deposition synthesis, the sulfurization of MoO 3 powders is an essential reaction step in which the MoO 3 reactants are converted into MoS 2 products. Recent studies have suggested using an H 2 S/H 2 mixture to reduce MoO 3 powders in an effective way. However, reaction mechanisms associated with the sulfurization of MoO 3 by the H 2 S/H 2 mixture are yet to be fully understood. Here, we perform quantum molecular dynamics (QMD) simulations to investigate the sulfurization of MoO 3 flakes using two different gaseous environments: pure H 2 S precursors and a H 2 S/H 2 mixture. Our QMD results reveal that the H 2 S/H 2 mixture could effectively reduce and sulfurize the MoO 3 reactants through additional reactions of H 2 and MoO 3 , thereby providing valuable input for experimental synthesis of higher-quality TMDC materials.

Photoexcitation can drastically modify potential energy surfaces of materials, allowing access to hidden phases. SrTiO 3 (STO) is an ideal material for photoexcitation studies due to its prevalent use in nanostructured devices and its rich range of functionality-changing lattice motions. Recently, a hidden ferroelectric phase in STO was accessed through weak terahertz excitation of polarization-inducing phonon modes. In contrast, whereas strong laser excitation was shown to induce nanostructures on STO surfaces and control nanopolarization patterns in STO-based heterostructures, the dynamic pathways underlying these optically induced structural changes remain unknown. Here nonadiabatic quantum molecular dynamics reveals picosecond amorphization in photoexcited STO at temperatures as low as 10 K. The three-stage pathway involves photoinduced charge transfer and optical phonon activation followed by nonlinear charge and lattice dynamics that ultimately lead to amorphization. This atomistic understanding could guide not only rational laser nanostructuring of STO but also broader "quantum materials on demand" technologies.

For the conductive patterns of electronic textiles (e-textiles), it is still challenging to maintain low electrical resistance, even under large or cyclic tensile deformation. This study investigated a double-layered pattern with different crack configurations as a possible solution. Patterns with single crack growth exhibit a low initial resistance and resistance change rate. In contrast, patterns with multiple crack growth maintain their conductivity under deformation, where electrical failure occurs in those with single crack growth. We considered that a double-layered structure could combine the electrical characteristics of patterns with single and multiple crack growths. In this study, each layer was theoretically designed to control the crack configuration. Then, meandering copper patterns, silver ink patterns, and their double layers were fabricated on textiles as patterns with single and multiple crack growths and double-layered patterns, respectively. Their resistance changes under the single (large) and cyclic tensile deformations were characterized. The results confirmed that the double-layered patterns maintained the lowest resistance at the high elongation rate and cycle. The resistance change rates of the meandering copper and silver ink patterns were constant, and changed monotonically against the elongation rate/cycle, respectively. In contrast, the change rate of the double-layered patterns varied considerably when electrical failure occurred in the copper layer. The change rate after the failure was much higher than that before the failure, and on the same order as that of the silver ink patterns.

Reverse osmosis through a polyamide (PA) membrane is an important technique for water desalination and purification. In this study, molecular dynamics simulations were performed to study the biofouling mechanism (i.e., protein adsorption) and nonequilibrium steady-state water transfer of a cross-linked PA membrane. Our results demonstrated that the PA membrane surface's roughness is a key factor of surface's biofouling, as the lysozyme protein adsorbed on the surface's cavity site displays extremely low surface diffusivity, blocking water passage, and decreasing water flux. The adsorbed protein undergoes secondary structural changes, particularly in the pressure-driven flowing conditions, leading to strong protein-surface interactions. Our simulations were able to present water permeation close to the experimental conditions with a pressure difference as low as 5 MPa, while all the electrolytes, which are tightly surrounded by hydration water, were effectively rejected at the membrane surfaces. The analysis of the self-intermediate scattering function demonstrates that the dynamics of water molecules coordinated with hydrogen bonds is faster inside the pores than during the translation across the pores. The pressure difference applied shows a negligible effect on the water structure and content inside the membrane but facilitates the transportation of hydrogen-bonded water molecules through the membrane's sub-nanopores with a reduced coordination number. The linear relationship between the water flux and the pressure difference demonstrates the applicability of continuum hydrodynamic principles and thus the stability of the membrane structure.

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

Directly printing conductive ink on textiles is simple and compatible with the conventional electronics manufacturing process. However, the conductive patterns thus formed often show high initial resistance and significant resistance increase due to tensile deformation. Achieving conductive patterns with low initial resistance and reduced deformation-induced resistance increase is a significant challenge in the field of electronic textiles (e-textiles). In this study, the passivation layers printed on conductive patterns, which are necessary for practical use, were examined as a possible solution. Specifically, the reduction of the initial resistance and deformation-induced resistance increase, caused by the curing shrinkage of passivation layers, were theoretically and experimentally investigated. In the theoretical analysis, to clarify the mechanism of the reduction of deformation-induced resistance increase, crack propagation in conductive patterns was analyzed. In the experiments, conductive patterns with and without shrinking passivation layers (polydimethylsiloxane) cured at temperatures of 20-120 °C were prepared, and the initial resistances and resistance increases due to cyclic tensile and washing in each case were compared. As a result, the initial resistance was reduced further by the formation of shrinking passivation layers cured at higher temperatures, and reduced to 0.45 times when the curing temperature was 120 °C. The cyclic tensile and washing tests confirmed a 0.48 and a 0.011 times reduction of resistance change rate after the 100th elongation cycle (10% in elongation rate) and the 10th washing cycle, respectively, by comparing the samples with and without shrinking passivation layers cured at 120 °C.

For the improvement of the performance and function of electronic textiles (e-textiles), methods for electronic component mounting of textile circuits with electrical and mechanical durability are necessary. This manuscript presents a component mounting method for durable e-textiles, with a simpler implementation and increased compatibility with conventional electronics manufacturing processes. In this process, conductive patterns are directly formed on a textile by the printing of conductive ink with deep permeation and, then, components are directly soldered on the patterns. The stiffness of patterns is enhanced by the deep permeation, and the enhancement prevents electrical and mechanical breakages due to the stress concentration between the pattern and solder. This allows components to be directly mounting on textile circuits with electrical and mechanical durability. In this study, a chip resistor was soldered on printed patterns with different permeation depths, and the durability of the samples were evaluated by measuring the variation in resistance based on cyclic tensile tests and shear tests. The experiments confirmed that the durability was improved by the deep permeation, and that the samples with solder and deep permeation exhibited superior durability as compared with the samples based on commercially available elastic conductive adhesives for component mounting. In addition, a radio circuit was fabricated on a textile to demonstrate that various types of components can be mounted based on the proposed methods., Competing Interests: The authors declare no conflict of interest except for the aforementioned patent application.

Metal combustion reaction is highly exothermic and is used in energetic applications, such as propulsion, pyrotechnics, powering micro- and nano-devices, and nanomaterials synthesis. Aluminum (Al) is attracting great interest in those applications because of its high energy density, earth abundance, and low toxicity. Nevertheless, Al combustion is hard to initiate and progresses slowly and incompletely. On the other hand, ultrathin carbon nanomaterials, such as graphene, graphene oxide (GO), and graphene fluoride (GF), can also undergo exothermic reactions. Herein, we demonstrate that the mixture of GO and GF significantly improves the performance of Al combustion as interactions between GO and GF provide heat and radicals to accelerate Al oxidation. Our experiments and reactive molecular dynamics simulation reveal that GO and GF have strong chemical and thermal couplings through radical reactions and heat released from their oxidation reactions. GO facilitates the dissociation of GF, and GF accelerates the disproportionation and oxidation of GO. When the mixture of GO and GF is added to micron-sized Al particles, their synergistic couplings generate reactive oxidative species, such as CF x and CF x O y , and heat, which greatly accelerates Al combustion. This work demonstrates a new area of using synergistic couplings between ultrathin carbon nanomaterials to accelerate metal combustion and potentially oxidation reactions of other materials.

Conductive patterns on textiles are one of the key components for electronic textiles (E-textiles). The patterns with deeper permeation of inks into the textiles show better durability against cyclic tensile deformation. However, other requirements for improving the durability and the behavior of resistance under deformation are still unclear. In this study, the resistance during cyclic tensile deformation was measured with changing conditions, and the resistance variation was analyzed while considering the stress variation. Silver inks were printed on a plain weave, and the pattern width and tensile direction against weft yarns were changed. Measurements confirmed that the resistance increased less with wider pattern widths and when the tensile direction was horizontal to the axis of the weft yarns. Through scanning electron microscopy (SEM) observation, we also confirmed that the growth rate of cracks, at the crossing point of yarns, was changed by the tensile direction. These results indicate that the durability is improved when the electricity path redundancy within the pattern is robust, and the crack growth rate at the yarn crossing points is low. The analysis also confirmed both increasing and decreasing behavior of resistance during stretching in the cyclic tensile deformation, indicating the behavior results from the stress variation of a plain weave.

We realized the implementation of an ultrathin piezoresistive Si chip and stretchable printed wires on a flexible film substrate using simple screen-offset printing technology. This process does not require a special MEMS fabrication equipment and is applicable to face-up chips where electrodes are formed on the top surface of the chip, as well as to face-down chips where electrodes are formed on the bottom surface of the chip. This fabrication process is quite useful in the field of flexible hybrid electronics (FHE) as a method for mounting and wiring electronic components on a flexible substrate. In this study, we confirmed that face-up and face-down chips could be mounted on polyimide film tape. Furthermore, it was confirmed that the two types of chips could be simultaneously mounted even if they exist on the same substrate. Five-μm-thick piezoresistive Si chips were transferred and wired on a polyimide film tape using screen-offset printing, and a band-plaster type blood pulse sensor was fabricated. Moreover, we successfully demonstrated that the blood pulse could be measured with neck, inner elbow, wrist, and ankle.

Monolayer MoS 2 is an outstanding candidate for a next-generation semiconducting material because of its exceptional physical, chemical, and mechanical properties. To make this promising layered material applicable to nanostructured electronic applications, synthesis of a highly crystalline MoS 2 monolayer is vitally important. Among different types of synthesis methods, chemical vapor deposition (CVD) is the most practical way to synthesize few- or mono-layer MoS 2 on the target substrate owing to its simplicity and scalability. However, synthesis of a highly crystalline MoS 2 layer remains elusive. This is because of the number of grains and defects unavoidably generated during CVD synthesis. Here, we perform multimillion-atom reactive molecular dynamics (RMD) simulations to identify an origin of the grain growth, migration, and defect healing process on a CVD-grown MoS 2 monolayer. RMD results reveal that grain boundaries could be successfully repaired by multiple heat treatments. Our work proposes a new way of controlling the grain growth and migration on a CVD-grown MoS 2 monolayer.

Somal translocation in long bipolar neurons is regulated by actomyosin contractile forces, yet the precise spatiotemporal sites of force generation are unknown. Here we investigate the force dynamics generated during somal translocation using traction force microscopy. Neurons with a short leading process generated a traction force in the growth cone and counteracting forces in the leading and trailing processes. In contrast, neurons with a long leading process generated a force dipole with opposing traction forces in the proximal leading process during nuclear translocation. Transient accumulation of actin filaments was observed at the dipole center of the two opposing forces, which was abolished by inhibition of myosin II activity. A swelling in the leading process emerged and generated a traction force that pulled the nucleus when nuclear translocation was physically hampered. The traction force in the leading process swelling was uncoupled from somal translocation in neurons expressing a dominant negative mutant of the KASH protein, which disrupts the interaction between cytoskeletal components and the nuclear envelope. Our results suggest that the leading process is the site of generation of actomyosin-dependent traction force in long bipolar neurons, and that the traction force is transmitted to the nucleus via KASH proteins., (Copyright © 2018 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

Optical ignition of solid energetic materials, which can rapidly release heat, gas, and thrust, is still challenging due to the limited light absorption and high ignition energy of typical energetic materials ( e.g., aluminum, Al). Here, we demonstrated that the optical ignition and combustion properties of micron-sized Al particles were greatly enhanced by adding only 20 wt % of graphene oxide (GO). These enhancements are attributed to the optically activated disproportionation and oxidation reactions of GO, which release heat to initiate the oxidization of Al by air and generate gaseous products to reduce the agglomeration of the composites and promote the pressure rise during combustion. More importantly, compared to conventional additives such as metal oxides nanoparticles ( e.g., WO 3 and Bi 2 O 3 ), GO has much lower density and therefore could improve energetic properties without sacrificing Al content. The results from Xe flash ignition and laser-based excitation experiments demonstrate that GO is an efficient additive to improve the energetic performance of micron-sized Al particles, enabling micron-sized Al to be ignited by optical activation and promoting the combustion of Al in air.

We fabricate a wearable blood leakage sensor on a cotton textile by combining two newly developed techniques. First, we employ a screen-offset printing technique that avoids blurring, short circuiting between adjacent conductive patterns, and electrode fracturing to form an interdigitated electrode structure for the sensor on a textile. Furthermore, we develop a scheme to distinguish blood from other substances by utilizing the specific dielectric dispersion of blood observed in the sub-megahertz frequency range. The sensor can detect blood volumes as low as 15 μL, which is significantly lower than those of commercially available products (which can detect approximately 1 mL of blood) and comparable to a recently reported value of approximately 10 μL. In this study, we merge two technologies to develop a more practical skin-friendly sensor that can be applied for safe, stress-free blood leakage monitoring during hemodialysis., Competing Interests: The authors declare no conflict of interest except for the aforementioned patent application.

Precise control of neuronal migration is essential for the development of the neocortex. However, the molecular mechanisms underlying neuronal migration remain largely unknown. Here we identified helix-loop-helix transcription factor Ebf3 as a Prdm8 target gene, and found that Ebf3 is a key regulator of neuronal migration via multipolar-to-bipolar transition. Ebf3 knockdown cells exhibited severe defects in the formation of leading processes and an inhibited shift to the locomotion mode. Moreover, we found that Ebf3 knockdown represses NeuroD1 transcription, and NeuroD1 overexpression partially rescued migration defects in Ebf3 knockdown cells. Our findings highlight the critical role of Ebf3 in multipolar-to-bipolar transition via positive feedback regulation of NeuroD1 in the developing neocortex., (Copyright © 2017 Elsevier Inc. All rights reserved.)