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In the field of nano-composites, hybrid nano-fluids have noteworthy applications in aerospace, energy materials, thermal sensors, antifouling, etc. because of their ability to produce higher thermal conductivity than conventional nanofluids. Different combinations of nanocomposites have been found in the literature to develop the suitable hybrid-mixture, but no study has been found yet about the combined influence of ceria and zinc-oxide nanocomposites in the host liquid. In this article, unsteady 3 D transport of water driven hybrid nano-fluid with the consequences of brick shaped nanocomposites (ceria; and zinc oxide; ) has deliberated with the thermal link of heat source/sink. Variable thermal conditions have been supplied at the surface with the effect of a magnetic environment. Similarity relations have been used to articulate the transport equations into solvable forms and then solved numerically via Keller-Box method. Nusselt number and skin-friction coefficients have also plotted with the wide ranges of involved parameters. Thermal setup has also briefly been discussed with the non-uniformity of surface temperature. Rate of heat transfer has significantly improved with the amounts of ceria (1 wt% to 10 wt%) and zinc-oxide (1 wt% to 10 wt%) nanocomposites. The Nusselt number is reported in the range of 4.0 to 4.8 with the increasing amount of from −0.6 to −0.2, whereas it is reported in the range of 3.1 to 3.9 with the varying amount of from 0.2 to 0.6. Rate of heat transfer is observed higher for zinc-oxide nanoparticles as compared to ceria nanoparticles.

In this paper, the machining principle of variable hyperbolic circular-arc-tooth-trace (VH-CATT) cylindrical gears is analysed, and a modular configuration design of this special machine tool is carried out. Based on the importance of parameter and knowledge dependence of rough set theory, the degree of influence of the index hidden in the data on the decision-making results is mined, and an algorithm for calculating the objective weight of the index is given. The comprehensive weight of the index is then obtained, combining the analytic hierarchy process. After obtaining the results of the index's comprehensive weight and module attribute classification, a module retrieval strategy is formulated using the fuzzy similarity priority ratio algorithm. Based on the modular division of this special gear-making machine tool, the modular configuration sequence of the machine tool using the depth-first search algorithm is determined. According to the module retrieval strategy and configuration sequence, a traversal of module instances is carried out, and a variety of modular configuration schemes of the machine tool is obtained, which provides a practical and efficient method for the rapid design of a special machine tool for VH-CATT cylindrical gears.

A method of end-effector trajectory planning in Cartesian space based on multi-objective optimization is proposed in this paper to solve the collision problem during the motion of the redundant manipulator. First, a cosine polynomial function is used to interpolate the trajectory of the end effector, enabling it to reach the desired pose at a specific time. Then, the joint trajectory of the manipulator is solved by inverse kinematics, and the null space term is introduced as the joint limit constraint in the inverse kinematics equation. During the operation of the manipulator, the collision detection algorithm is employed to calculate the distance between the obstacle and each arm in real time. Finally, a multi-objective, multi-optimization model of trajectory that considers the obstacle avoidance, joint velocity, joint jerk and energy consumption is established and optimized with a multi-objective particle swarm optimization algorithm. The simulation results demonstrate that the proposed method can effectively accomplish the trajectory planning task and avoid obstacles; the joint trajectories obtained are smooth and meet the limit constraints; the joint jerk and energy consumption are well suppressed.

The increased use of spherical wheels has allowed mobile robots to have a higher degree of maneuverability, less complex path planning and less complex control schemes. The geometry and design of the mobile robot are the principal attributes that guarantee an omnidirectional motion. Furthermore, the platform uses an active spherical wheel and four passive spherical wheels to get the best stability when the robot uses a terminal element (Kärcher). The proposed model has been designed to improve the omnidirectional motion issues, such as vibration into the platform or lack of punctual contact between the wheel and the floor, compared to mobile robots using Mecanum wheels and more than one active wheel; due to the design concept, all the mathematical formulations, kinematics and dynamics presents how the models are validated with computer simulations.

There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models, and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The originality of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of MPEAs with targeted properties.

Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.

The deformation and damage mechanisms of tunnel-surrounding rock masses have always been a major problem in underground engineering, and studying these mechanisms plays an important role in preventing this kind of geological disaster. Against the background of the above project, in this paper, we propose a new type of support structure: a composite cantilever support structure. Studies involving the numerical calculation and application of this support structure are carried out, and the conducted investigation indicates that (1) for a relatively broken rock mass, the composite cantilever support structure can effectively restrain the deformation of the tunnel-surrounding rock mass, (2) through numerical calculation, the appropriate design parameters of the support structure are obtained, and (3) the design parameters and design structure of the composite cantilever support structure must be further studied and verified under complex geological conditions by engineering.

Precision-based drug delivery via remote triggering is fast becoming an attractive therapeutic design and is highly useful in complicated clinical situations that may require accurate site-delivery of drug while reducing the risk of collateral damage to surrounding healthy tissue. Of the many strategies available to achieve these desirable effects, silica/gold nano-assemblies offers a practical means to achieving these aims. Herein, as a proof-of-concept, a silica nanocapsule passivated with a gold outer nanoshell had been fabricated to deliver Doxorubicin, and this nano-assembly can be remotely triggered via two-photon excitation (TPE), even under in vivo setting. A polyethylene glycol (PEG) layer as well as AS1411 DNA aptamer had also been grafted to the surface to improve homing specificity toward MDA-MB-231 breast cancer tissue. The assembly of silica/gold nanocapsules was characterized via TEM, FTIR, and UV-Vis to validate the the nanoconstruct. Upon TPE irradiation, a higher expression level of Annexin V and Caspase-3 was observed in both in vitro and in vivo animal models. A significant reduction in tumor size on mice model was noticed after 21 days, and these results had suggested a viable nano-sized design serving as remotely triggered drug release platform based on current well-established silica nanoparticulate methodologies.

The tribological properties of the cylinder liner are of great significance in reducing energy loss from an internal combustion engine's system. In order to improve the antiwear performance of the cylinder liner at top dead center (TDC), the friction and wear of the honed cylinder liner during the running-in process are investigated. Using a UMT TriboLab multifunction friction wear tester, the coefficients of friction (COFs) are analyzed under different lubrication conditions and loads, and the surface roughness and profile of the cylinder liner are obtained using a three-dimensional (3D) laser scanning confocal microscope (LSCM). The COF, roughness and surface profile are compared in order to investigate the variation in tribology parameters under different operating conditions. To monitor the COF in real time, it is predicted using the polynomial fitting method. It is shown that the COF decreases with an increase in the lubrication oil and load and that the surface profile more easily becomes smooth under dry (lubrication) conditions; this can effectively shorten the operation time during the running-in process. The polynomial fitting method can achieve an assessment of the COF with a very small standard error.

Optical manipulation of coherent phonon frequency in two-dimensional (2D) materials could advance the development of ultrafast phononics in atomic-thin platforms. However, conventional approaches for such control are limited to doping, strain, structural or thermal engineering. Here, we report the experimental observation of strong laser-polarization control of coherent phonon frequency through time-resolved pump-probe spectroscopic study of van der Waals (vdW) materials Fe3GeTe2. When the polarization of the pumping laser with tilted incidence is swept between in-plane and out-of-plane orientations, the frequencies of excited phonons can be monotonically tuned by as large as 3% (~100 GHz). Our first-principles calculations suggest the strong planar and vertical inter-atomic interaction asymmetry in layered materials accounts for the observed polarization-dependent phonon frequencies, as in-plane/out-of-plane polarization modifies the restoring force of the lattice vibration differently. Our work provides insightful understanding of the coherent phonon dynamics in layered vdW materials and opens up new avenues to optically manipulating coherent phonons.

In the aerospace field, the precision and stiffness for 2R1T (R denotes the rotation and the T denotes the translation) degree of freedom (DOF) space posture adjustment mechanisms are required. Compliant parallel mechanisms (CPMs) with a suitable constrained branch (SCB) have the advantages of high precision and high stiffness. Based on screw theory, a new type synthesis approach for a 2R1T compliant parallel mechanism with a suitable constrained branch is proposed. The proposed approach is an improvement of the freedom and constraint topology approach. It combines with other methods, including the rigid-body-replacement method, the principle of symmetry, etc. In order to obtain CPMs with a suitable constrained branch, the criterion for the type synthesis is presented. Using this proposed type synthesis approach, a series of CPMs is obtained. They include, but are not limited to, the existing typical 2R1T CPMs with a suitable constrained branch. Furthermore, it identifies the correctness and effectiveness of the approach by analyzing the DOF of the synthesized mechanism. This approach is also suitable for the type synthesis of 4, 5, and 6 DOF compliant parallel mechanisms with a suitable constrained branch.

Among parallel robots, spherical robots occupy an important place. Most applications of spherical manipulators can be found in orienting devices, such as camera orienting and medical instrument alignment. A spherical parallel robot is, in general, made up of the base platform and the moving platform. This mobile platform and base are connected by three equally spaced legs, each consisting of revolute joints only. The axes of all joints intersect at a common point, which is called the center of rotation. The motion of the moving platform is confined on the surface of a sphere centered at the rotation center. A spherical parallel robot provides 3 degrees of freedom of pure rotations. These robots have been the subject of many papers dealing with the structure, the problems of position and velocity, workspace modeling, singularity analysis, and some problems with the dynamic analysis. However, not all the important problems have been solved. These concern the problem of accuracy. This paper presents accuracy of the spherical parallel. In the considered spherical manipulator, each leg consists of five kinematic pairs. The kinematic accuracy is determined on the kinematic problem. The dynamic accuracy is estimated on the equation of motion. Examples of solving the problem of determining the positioning error of the output level are presented.

Accelerated design of hard-coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models. We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials, such as binary and ternary nitrides. We use the high-throughput toolkit to automate the density functional theory calculation workflow. We present results, including elastic constants that are a key parameter determining mechanical properties of hard-coatings, for X1−xYxN ternary nitrides, where X,Y ∈ {Al, Ti, Zr, Hf} and fraction $$x=0,\frac{1}{4},\frac{1}{2},\frac{3}{4},1$$ x = 0 , 1 4 , 1 2 , 3 4 , 1 . We also explore ways for machine learning to support and complement the designed databases. We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides, suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials, in our case disordered hard-coating alloys.

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li3ClO antiperovskite. By leveraging ab initio, machine learning, and force-field descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.

The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature Tc. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α2F(ω). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α2F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α2F functions and numerical solutions of the Eliashberg equations, we derive a formula for Tc that performs as well as Allen–Dynes for low-Tc superconductors and substantially better for higher-Tc ones. This corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.

The discovery of two-dimensional (2D) magnetic van der Waals (vdW) materials has flourished an endeavor for fundamental problems as well as potential applications in computing, sensing and storage technologies. Of particular interest are antiferromagnets, which due to their intrinsic exchange coupling show several advantages in relation to ferromagnets such as robustness against external magnetic perturbations. Here we show that, despite of this cornerstone, the magnetic domains of recently discovered 2D vdW MnPS3 antiferromagnet can be controlled via magnetic fields and electric currents. We achieve ultrafast domain-wall dynamics with velocities up to ~3000 m s−1 within a relativistic kinematic. Lorentz contraction and emission of spin-waves in the terahertz gap are observed with dependence on the edge termination of the layers. Our results indicate that the implementation of 2D antiferromagnets in real applications can be further controlled through edge engineering which sets functional characteristics for ultrathin device platforms with relativistic features.

The fabrication of integrated circuits (ICs) employing two-dimensional (2D) materials is a major goal of semiconductor industry for the next decade, as it may allow the extension of the Moore’s law, aids in in-memory computing and enables the fabrication of advanced devices beyond conventional complementary metal-oxide-semiconductor (CMOS) technology. However, most circuital demonstrations so far utilizing 2D materials employ methods such as mechanical exfoliation that are not up-scalable for wafer-level fabrication, and their application could achieve only simple functionalities such as logic gates. Here, we present the fabrication of a crossbar array of memristors using multilayer hexagonal boron nitride (h-BN) as dielectric, that exhibit analog bipolar resistive switching in >96% of devices, which is ideal for the implementation of multi-state memory element in most of the neural networks, edge computing and machine learning applications. Instead of only using this memristive crossbar array to solve a simple logical problem, here we go a step beyond and present the combination of this h-BN crossbar array with CMOS circuitry to implement extreme learning machine (ELM) algorithm. The CMOS circuit is used to design the encoder unit, and a h-BN crossbar array of 2D hexagonal boron nitride (h-BN) based memristors is used to implement the decoder functionality. The proposed hybrid architecture is demonstrated for complex audio, image, and other non-linear classification tasks on real-time datasets.

Carbon nanotube fibers (CNTFs) have many desirable properties such as lightweight, high strength, high conductivity, and long lifetimes. Coiled CNTF is an ideal material for preparing electrochemically driven artificial muscles. While previous studies focused mainly on the actuation performance of artificial muscles made of CNTF, this study focuses on an actuator that mimics human finger movements (flexion). More specifically, the preparation of CNTF muscles were optimized by twisting with weight. Then, actuators are designed and assembled by combining all-solid-state CNTF muscles with polypropylene (PP) sheets. Moreover, a dual-electrode system, which is infiltrated by a gel electrolyte, is built into the muscle actuator. In addition, a robotic gripper is fabricated, which uses these actuators. This study can help improve the design of CNTF-based muscle-actuators and future applications in robotics.

Two-dimensional (2D) magnets have broad application prospects in the spintronics, but how to effectively control them with a small electric field is still an issue. Here we propose that 2D magnets can be efficiently controlled in a multiferroic heterostructure composed of 2D magnetic material and perovskite oxide ferroelectric (POF) whose dielectric polarization is easily flipped under a small electric field. We illustrate the feasibility of such strategy in the bilayer CrI3/BiFeO3(001) heterostructure by using the first-principles calculations. Different from the traditional POF multiferroic heterostructures which have strong interface interactions, we find that the interface interaction between CrI3 and BiFeO3(001) is van der Waals type. Whereas, the heterostructure has particular strong magnetoelectric coupling where the bilayer CrI3 can be efficiently switched between ferromagnetic and antiferromagnetic types by the polarized states of BiFeO3(001). We also discover the competing effect between electron doping and the additional electric field on the interlayer exchange coupling interaction of CrI3, which is responsible to the magnetic phase transition. Our results provide a new avenue for the tuning of 2D magnets with a small electric field., 7 pages, 5 figures

Modeling of ductile fracture in polycrystalline structures is challenging, since it requires integrated modeling of cracks, crystal plasticity, and grains. Here we extend the typical phase-field framework to the situations with constraints on the order parameters, and formulate two types of phase-field models on ductile fracture. The Type-I model incorporates three sets of order parameters, which describe the distributions of cracks, plastic strain, and grains, respectively. Crystal plasticity is employed within grain interiors accommodated by J2 plasticity at grain boundaries. The applications of the Type-I model to single crystals and bicrystals demonstrate the influences of grain orientations and grain boundaries on crack growth. In the Type-II model, J2 plasticity is assumed for the whole system and grain structures are neglected. Taking advantage of the efficiency of the fast Fourier transform, our Type-II model is employed to study low cycle fatigue. Crack closure and striation-like patterning of plastic strain are observed in the simulations. Crack growth rate is analyzed as a function of the J-integral, and the simulated fatigue life as a function of plastic strain agrees with the Coffin–Manson relation without a priori assumption.

Posture transformation is an essential function for multi-posture wheelchairs. To improve the natural motion in posture transformation that is a popular problem in the design of multi-posture wheelchairs because the current wheelchair's posture transformation mechanism cannot remain consistent between the rotation center of the wheelchair and the rotation center of the human body joints. This paper proposes a sitting–standing–lying three-posture bionic transformation mechanism for a smart wheelchair. A human–wheelchair coupling model is described and analyzed according to the biomechanical characteristics of the posture transformation of human beings and their functional requirements. The configuration of the transformation mechanism is chosen by comparing the trails of the wheelchair rotation centers and the corresponding human joint rotation centers. The kinematics of the optimized configuration are discussed in detail to obtain the most bionic motion performance using the multivariable nonlinear constraint optimization algorithm. Finally, the mechanism is designed, and its posture transformation performance is simulated and verified using Adams (Automatic Dynamic Analysis of Mechanical Systems) software.

Palladium diselenide (PdSe2), a new type of two-dimensional noble metal dihalides (NMDCs), has received widespread attention for its excellent electrical and optoelectronic properties. Herein, high-quality continuous centimeter-scale PdSe2 films with layers in the range of 3L–15L were grown using Chemical Vapor Deposition (CVD) method. The absorption spectra and DFT calculations revealed that the bandgap of the PdSe2 films decreased with the increasing number of layers, which is due to the enhancement of orbital hybridization. Spectroscopic ellipsometry (SE) analysis shows that PdSe2 has significant layer-dependent optical and dielectric properties. This is mainly due to the unique strong exciton effect of the thin PdSe2 film in the UV band. In particular, the effect of temperature on the optical properties of PdSe2 films was also observed, and the thermo-optical coefficients of PdSe2 films with the different number of layers were calculated. This study provides fundamental guidance for the fabrication and optimization of PdSe2-based optoelectronic devices.

We present a method to model interatomic interactions such as energy and forces in a computationally efficient way. The proposed model approximates the energy/forces using a linear combination of random features, thereby enabling fast parameter estimation by solving a linear least-squares problem. We discuss how random features based on stationary and non-stationary kernels can be used for energy approximation and provide results for three classes of materials, namely two-dimensional materials, metals and semiconductors. Force and energy predictions made using the proposed method are in close agreement with density functional theory calculations, with training time that is 96% lower than standard kernel models. Molecular Dynamics calculations using random features based interatomic potentials are shown to agree well with experimental and density functional theory values. Phonon frequencies as computed by random features based interatomic potentials are within 0.1% of the density functional theory results. Furthermore, the proposed random features-based potential addresses scalability issues encountered in this class of machine learning problems.

A milling chamber consisting of a rice sieve and a rotating roller plays critical roles in modulating the milling performance of rice grains. However, the mechanism of how the geometries of the rice sieve and rotating roller affect the particle collisions and the interaction time remains not fully understood. Our experimental results show that the milling degree and rate of broken rice of the octagonal rice sieve are largest among the hexagonal sieve, octagonal sieve, and circular sieve. Through the discrete element method, we illustrate that the peak milling degree at the octagonal sieve is attributed to the competition between the decreasing force and increasing milling time with the increase in edges. In addition, the geometries of the convex ribs of the rotating roller are investigated to optimize the structure of the milling chamber. In the left-hand spiral or right-hand spiral of the convex ribs, the rice particles are accumulated in the inlet or outlet regions, respectively, which leads to an uneven milling degree in the axial direction. The uniformity of a milling process can be promoted by increasing the number of convex ribs, which will reduce the milling degree on the other hand.

We introduce three developments within the stochastic many-body perturbation theory: efficient evaluation of off-diagonal self-energy terms, construction of Dyson orbitals, and stochastic constrained random phase approximation. The stochastic approaches readily handle systems with thousands of atoms. We use them to explore the electronic states of twisted bilayer graphene (tBLG) characterized by giant unit cells and correlated electronic states. We document the formation of electron localization under compression; weakly correlated states are merely shifted in energy. We demonstrate how to efficiently downfold the correlated subspace on a model Hamiltonian with a screened frequency-dependent two-body interaction. For the 6° tBLG system, the onsite interactions are between 200 and 300 meV under compression. The Dyson orbitals exhibit spatial distribution similar to the mean-field single-particle states. Under pressure, the electron-electron interactions increase in the localized states; however, the dynamical screening does not fully balance the dominant bare Coulomb interaction.

Slide-ring materials with movable cross-links have received attention due to their excellent mechanical properties. However, due to the poor solubility of polyrotaxane and low synthesis efficiency, their applications are hindered. Here, we use hydroxypropyl-modified α-cyclodextrin (Hy-α-CD) and Acrylamide-PEG20000-Acrylamide (ACA-PEG20000-ACA) to construct a polypseudorotaxane with good water solubility. Through photo-initiated polymerization of polypseudorotaxane with acrylamide in-situ, the capped polyrotaxane was easily obtained and further cross-linked by 1,4-butanediol diglycidyl ether in sodium hydroxide solution to form a slide-ring supramolecular hydrogel. The hydrogel can be stretched to 25.4 times its original length, which recovers rapidly on unloading, and the addition of Ca2+ ions during crosslinking enhances ionic conductivity. The Ca2+-doped hydrogels are used to prepare wearable strain sensors for monitoring human motion.

Optical devices capable of suppressing diffraction nature of light are of great technological importance to many nanophotonic applications. One important technique to achieve diffractionless optics is to exploit field canalization effect. However, current technological platforms based on metamaterial structures typically suffer from strict loss-confinement trade-off, or lack dynamic reconfigurability over device operations. Here we report an integrated canalization platform that can alleviate this performance trade-off. It is found that by leveraging material absorption of anisotropic 2D materials, the dispersion of this class of materials can flatten without increasing propagation losses and compromising confinement. The realization of such plasmon canalization can be considered using black phosphorus (BP), where topological transition from elliptic to hyperbolic curves can be induced by dynamically leveraging material absorption of BP. At the transition point, BP film can support long range, deeply subwavelength, near-diffractionless field propagation, exhibiting diffraction angle of 5.5°, propagation distance of 10λspp, and λspp 0/300.

Achieving high-performance p-type semiconductors has been considered one of the most challenging tasks for three-dimensional vertically integrated nanoelectronics. Although many candidates have been presented to date, the facile and scalable realization of high-mobility p-channel field-effect transistors (FETs) is still elusive. Here, we report a high-performance p-channel tellurium (Te) FET fabricated through physical vapor deposition at room temperature. A growth route involving Te deposition by sputtering, oxidation and subsequent reduction to an elemental Te film through alumina encapsulation allows the resulting p-channel FET to exhibit a high field-effect mobility of 30.9 cm2 V−1 s−1 and an ION/OFF ratio of 5.8 × 105 with 4-inch wafer-scale integrity on a SiO2/Si substrate. Complementary metal-oxide semiconductor (CMOS) inverters using In-Ga-Zn-O and 4-nm-thick Te channels show a remarkably high gain of ~75.2 and great noise margins at small supply voltage of 3 V. We believe that this low-cost and high-performance Te layer can pave the way for future CMOS technology enabling monolithic three-dimensional integration.

The scalable production of two-dimensional (2D) materials is needed to accelerate their adoption to industry. In this work, we present a low-cost in-line and enclosed process of exfoliation based on high-shear mixing to create aqueous dispersions of few-layer graphene, on a large scale with a Yw ~ 100% yield by weight and throughput of ϕ ~ 8.3 g h−1. The in-line process minimises basal plane defects compared to traditional beaker-based shear mixing which we attribute to a reduced Reynolds number, Re ~ 105. We demonstrate highly conductive graphene material with conductivities as high as σ ∼ 1.5 × 104 S m−1 leading to sheet-resistances as low as Rs ∼ 2.6 Ω □−1 (t ∼ 25 μm). The process is ideal for formulating non-toxic, biocompatible and highly concentrated (c ∼ 100 mg ml−1) inks. We utilise the graphene inks for inkjet printable conductive interconnects and lithium-ion battery anode composites that demonstrate a low-rate lithium storage capability of 370 mAh g−1, close to the theoretical capacity of graphite. Finally, we demonstrate the biocompatibility of the graphene inks with human colon cells and human umbilical vein endothelial cells at high c ∼ 1 mg ml−1 facilitating a route for the use of the graphene inks in applications that require biocompatibility at high c such as electronic textiles.

How particular bonds form in quantum materials has been a long-standing puzzle. Two key concepts dealing with charge degrees of freedom are dimerization (forming metal-metal bonds) and charge ordering (CO). Since the 1930s, these two concepts have been frequently invoked to explain numerous exciting quantum materials, typically insulators. Here we report dimerization and CO within the dimers coexisting in metallic NaRu$_2$O$_4$. By combining high-resolution x-ray diffraction studies and theoretical calculations, we demonstrate that this unique phenomenon occurs through a new type of bonding, which we call Z-type ordering. The low-temperature superstructure has strong dimerization in legs of zigzag ladders, with short dimers in legs connected by short zigzag bonds, forming Z-shape clusters: simultaneously, site-centered charge ordering also appears. Our results demonstrate the yet unknown flexibility of quantum materials with the intricate interplay among orbital, charge, and lattice degrees of freedom., 37 pages, 9 figures, 4 tables

Cu2O is a promising p-type semiconductor for low-cost photovoltaics and transparent optoelectronics. However, low-cost and low-temperature fabrication of Cu2O films with good transport properties remains challenging, thus limiting their widespread adoption in devices. Here, we report Cu2O thin films of 20–80 nm thickness with hole mobility up to 92 cm2V−1s−1 using atmospheric-pressure spatial atomic layer deposition at temperatures below 260 °C, from a copper (I) hexafluoro-2,4-pentanedionate cyclooctadiene precursor. Raman spectroscopy indicates the presence of copper split vacancies and shows that the high hole mobility can be correlated to a low concentration of shallow acceptor defects. The optical bandgap of deposited films can be tuned between 2.08 eV and 2.5 eV, depending on the deposition temperature. All-oxide semitransparent Cu2O/ZnO solar harvesters are fabricated, showing efficiency values comparable to devices that incorporate much thicker Cu2O layers. Our work provides a promising approach towards cost-efficient, all-oxide solar harvesters, and for other (opto)electronic devices. Semiconducting Cu2O is attractive for photovoltaic and optoelectronic devices, though balancing high hole mobility with low-cost fabrication is challenging. Here, Cu2O thin films with high hole mobility of 92 cm²V−1s−1 are deposited in air, and applied in a semi-transparent solar harvester.

The article discusses ways to solve engineering problems in the study of technological processes using methods of system analysis. The essence of this method is to study the technology as a cybernetic system with an assessment of the" reactions” of this system to external influences formed during an active experiment. At the same time, optimization problems are solved analytically. Analytical optimization is based on two main principles. The regression equations obtained as a result of processing experimental data and testing statistical hypotheses are models that adequately describe real processes. Each of these equations is an algebraic function of several variables, to which methods of mathematical analysis are applicable, including the study of extremums of functions in partial derivatives. The next step is to develop a process algorithm and develop computer programs that allow you to select the composition and predict the properties of the product. As an engineering interpretation, it is possible to construct optimized nomograms that allow solving both direct and inverse problems; that is, predicting the result or selecting technological factors. The research methods described in the article are implemented in the study of technologies of cellular concrete, foam concrete, cement-polymer concrete and products made of mineral wool and foam glass. As an example, the article considers the optimization of the selection of the composition of fine-grained concrete reinforced with chopped glass fiber. The implementation of the developed method allowed us to determine the optimal value of the determining parameters, including the consumption of fiber and plasticizer, as well as to form a method for studying the properties of products.

Numerical solution of the problem for Poisson’s equation with the use of Daubechies wavelet discrete continual finite element method (specific version of wavelet-based discrete-continual finite element method) is under consideration in the distinctive paper. The operational initial continual and discrete-continual formulations of the problem are given, several aspects of finite element approximation are considered. Some information about the numerical implementation and an example of analysis are presented.

A review of the most significant domestic and, due to numerical superiority, foreign works on physical modelling of snow transport and snow accumulation processes, in particular, for the purpose of determining snow loads on roofs with arbitrary geometry, is presented. The existing practice of development of recommendations on assignment of snow loads in Russian laboratories is considered and critically evaluated. Comparison of do-mesticworks with scientific articles in the advanced world scientific journals and foreign regulatory documents leads to unfavorable conclusions. Recommendations on assigning snow loads, issued by Russian laboratories on the basisof extremely outdated and poorly substantiated methodology, bear a serious risk for evaluating mechan-ical safety of modern structures, for which such recommendations are developed. Recommendations are offered to remedy this current dangerous practice. The article also gives some suggestions on forming a basis for field observations of snow loads on existing roofs.

In this paper the problem of numerical simulation of composite bending elements dynamic considering internal (material) damping. For this purpose the nonlocal in time damping model, called damping with memory, is proposed as an alternative to the classic local Kelvin-Voigt model. Damping with memory makes damping forces not only dependent on the instant value of the strain rate, but also on the previous history of the vibration process. Since finite element analysis is the most common method of structural analysis, the nonlocal damping model is integrated into FEA algorithm. The FEA dynamic equilibrium equation is solved using the explicit scheme. The damping matrix was developed using the stationary full energy requirement. One-dimensional nonlocal in time model was implemented in MATLAB software package. The results of three-dimensional numerical simulation of the composite beam vibration obtained in SIMULIA Abaqus were used for model calibration. The obtained results were compared to the results based on classic Kelvin-Voight damping model.

In the present paper, the forced driven nonlinear vibrations of an elastic plate in a viscoelastic medium and resting on a viscoelastic Winkler-type foundation are studied. The damping features of the surrounding medium and foundation are described by the Kelvin-Voigt model and standard linear solid model with fractional derivatives, respectively. The dynamic response of the plate is described by the set of nonlinear differential equations with due account for the fact that the plate is being under the conditions of the internal resonance accompanied by the external resonance. The expressions for the stress function and nonlinear coefficients for different types of boundary conditions are presented.

The article discusses ways to improve the accuracy of solving problems of nonlinear structural mechanics. It is shown that the combination of the method of sequential loading and the Newton-Kantorovich method can improve the accuracy of the solution and reduce the complexity of obtaining results. The solution of the given linear equations can be obtained by numerical and approximate methods known in the literature.

An innovative method for calculating thermal fields inside monolithic structures has been developed, based on the use and analysis of nonlinear differential equations. The innovativeness of the method lies in the approach to the analysis of nonlinear physical processes using nonlinear differential equations. Thanks to the method of group analysis, 13 expressions are obtained from complex mathematical equations, which are easy to use and depend on several empirical coefficients. It is assumed that this calculation method is a priori more accurate than the existing ones, as well as available to people at a construction site without higher mathematical education, which makes it a priority for research. The applicability of this method must be proven by linking empirical coefficients and variables to the conditions of the experiments, while obtaining reliable data that will turn out to be more accurate than the existing calculation methods. This article demonstrates a systematic approach to establishing the suitability of using the method of group analysis of differential equations for problems of winter concreting on the basis of laboratory experiments under stationary conditions. The equations were subject to verification, which, according to the physical description, correspond to the real conditions of the course of thermal processes inside monolithic structures. Based on the obtained processing results, it was decided that it was necessary to further study the innovative method in the conditions of the construction site, but only for some expressions that showed the best results at the stage of laboratory tests.

The assessment of the punching shear capacity for reinforced concrete slabs, carried out according to the regulatorydocuments of a number of countries, leads to significantly various results. At the same time, the results of thecalculated forecast may have great differences from the experimental data. A great influence on the accuracy of the resultsof the calculated forecast is exerted by the thickness of the examined slabs, as well as the value of longitudinal reinforcement.These parameters determine the features of the mechanisms of destruction of slabs in case of the punching shearmechanism, as indicated by individual interpretations of the results of experimental studies. In order to determine thefeatures of the punching shear mechanism of reinforced concrete slabs of various thicknesses, numerical studies of theprocess of cracking and destruction of slabs of different thicknesses have been performed. Differences in the mechanismof formation and development of cracks in thin and thick slabs are revealed. The paper shows that the behavior of thinand thick slabs has qualitative distinctions at the initial stages of formation and development of the cracks leading todestruction. The authors have also shown the difference between stress-strain state of thick and thin slabs before destruction.In conclusion, it was established that the influence of longitudinal reinforcement on the strength during punching inthick slabs is much less than in thin ones.When evaluating the punching shear capacity of reinforced concrete slabs, the regulatory documents of different countries give significantly different results. In this case, the calculation results may differ significantly from the experimental data. The deterioration of the thickness of the calculated slabs, as well as the value of the longitudinal reinforcement has a great influence on the accuracy of the calculation results. These parameters determine the features of the destruction mechanisms of slabs under punching. This fact is indicated by some interpretations of the results of experimental studies. In order to establish the peculiarities of the punching shear mechanism of reinforced concrete slabs of different thicknesses, a numerical investigation of the cracking and destruction of slabs of different thicknesses have been performed. Differences in the mechanism of formation and development of cracks in thin and thick slabs have been revealed. The paper shows that the behavior of thin and thick slabs has qualitative differences at the initial stages of the cracks formation and development that leads to destruction. The difference between stress-strain state of thick and thin slabs before breaking have been shown. It was found that the effect of longitudinal reinforcement on the punching shear strength in thick slabs is much less than in thin ones.

Based on the lumped parameter theory, a nonlinear bending torsion coupling dynamic model of planetary gear transmission system was established by considering the backlash, support clearance, time-varying meshing stiffness, meshing damping, transmission error and external periodic excitation. The model was solved by the Runge–Kutta method, the dynamic response was analyzed by a time domain diagram and phase diagram, and the nonlinear vibration characteristics were studied by the response curve of the speed vibration displacement. The vibration test of the planetary gearbox was carried out to verify the correctness of frequency domain response characteristics. The results show that the vibration response in the planetary gear system changes from a multiple periodic response to a single periodic response with the increase in input speed. Under the action of the backlash, time-varying meshing stiffness and meshing damping, the speed vibration displacement response curves of internal and external meshing pairs appear to form a nonlinear jump phenomenon and have a unilateral impact area, and the system presents nonlinear characteristics. The nonlinear vibration of the system can be effectively suppressed by decreasing the mesh stiffness or increasing the mesh resistance, while the vibration response displacement of the system increases by increasing the external exciting force, and the nonlinear characteristics of the system remain basically unchanged. The backlash is the main factor affecting the nonlinear frequency response of the system, but it can restrain the resonance of the system in a certain range. The spectrum characteristics of the vibration displacement signal of the planetary gearbox at different speeds are similar to the simulation results, which proves the validity of the simulation analysis model and the simulation results. It can provide a theoretical basis for the system vibration and noise reduction and a dynamic structural stability design optimization.

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the ML models. Therefore, we devised an intuitive method of interpreting such opaque ML models through SHapley Additive exPlanations (SHAP) values and coupling them with the HT approach for finding efficient 2D water-splitting photocatalysts. We developed a new database of 3099 2D materials consisting of metals connected to six ligands in an octahedral geometry, termed as 2DO (octahedral 2D materials) database. The ML models were constructed using a combination of composition and chemical hardness-based features to gain insights into the thermodynamic and overall stabilities. Most importantly, it distinguished the target properties of the isocompositional 2DO materials differing in bond connectivities by combining the advantages of both elemental and structural features. The interpretable ML regression, classification, and data analysis lead to a new hypothesis that the highly stable 2DO materials follow the HSAB principle. The most stable 2DO materials were further screened based on suitable band gaps within the visible region and band alignments with respect to standard redox potentials using the GW method, resulting in 21 potential candidates. Moreover, HfSe2 and ZrSe2 were found to have high solar-to-hydrogen efficiencies reaching their theoretical limits. The proposed methodology will enable materials scientists and engineers to formulate predictive models, which will be accurate, physically interpretable, transferable, and computationally tractable.

Superparamagnetic iron oxide nanoparticles (SPIONs) have attracted the particular interest of scientists from various disciplines since their obtaining to the present day. The physicochemical and pharmacokinetic properties of SPIONs-containing magnetic nanofluids, and their applicability in biomedicine, largely depend on the stability of the colloidal system, particle size, size distribution, net magnetic moment, phase composition, and type and properties of stabilizers. Also, in some cases, when using magnetic nanoparticles for biomedical purposes, it is necessary that the stabilizing ligands of nanoparticles should not significantly change the magnetic properties. From this point of view, the preparation of stable colloidal systems containing bare iron oxide nanoparticles (BIONs) in water at physiological pH attracts particular attention and becomes increasingly popular in scientific circles. This study is focused on the development of the synthesis of aqueous suspensions of SPIONs stabilized with various organic molecules (oleic acid [OA] and poly(ethylene glycol) monooleate - with molecular weights 460 and 860) using a modified controlled chemical coprecipitation reaction, as well as stable nanofluids containing BIONs in an aqueous medium at neutral pH (near-physiological). The obtained samples were characterized using X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy, small-angle x-ray scattering (SAXS), dynamic light scattering (DLS), electrophoretic light scattering (ELS), and Vibrating Sample Magnetometry.

In the present study, twenty seven [(Fe–35wt%Mn)100−x –Cu x ] alloy samples were processed using high-energy ball milling, followed by uniaxial compaction under different processing conditions. The compressibility behavior in terms of relative density (RD) was examined with milling time (MT: 1 h, 5.5 h, and 10 h), ball-to-powder mass ratio (BPMR: 5:1, 10:1, and 15:1), milling speed (MS: 100 rev/min, 200 rev/min, and 300 rev/min), compaction pressure (CP: 25–1,100 MPa), and alloy composition (Cu content [CC]: 0 wt%, 5 wt%, 10 wt%). Particle size analysis using X-ray diffraction (XRD) and high-resolution scanning electron microscopy (HRSEM) combined with energy-dispersive X-ray spectroscopy (EDS) were applied for microstructural characterization. The experiments were conducted based on the central composite design of response surface methodology (RSM), and the results for the compaction behavior were examined with the input parameters. Analysis of variance (ANOVA) test was applied to determine the most significant input parameters. The attained results revealed that increasing ball milling parameters (MT, MS, and BPMR) resulted in significant enhancements in the microstructural features, such as improved elemental dispersion and occurrence of refined particles with substantial decrease in the crystallite size. On the other hand, increasing the input parameters exhibited a detrimental influence on the compactibility and RD of the alloys. In addition, increasing the CC resulted in a substantial improvement in the compressibility and RD of the developed alloys. The recommended combination of the studied variables includes MT for 5 h, MS for 150 rev/min, BPMR of 10:1, and 10 wt%Cu to attain an acceptable compromise of enhanced microstructure features, improved compaction response, and RD.

Degenerate points/lines in the bulk band structures of crystals have become a staple of the growing number of topological materials. The bulk-boundary correspondence provides a relation between bulk topology and surface states. While line degeneracies of bulk excitations have been extensively characterized, line degeneracies of surface states are not well understood. We show that SnIP, a quasi-one-dimensional van der Waals material with a double helix crystal structure, exhibits topological nodal rings/lines in both the bulk phonon modes and their corresponding surface states. Using a combination of first-principles calculations, symmetry-based indicator theories and Zak phase analysis, we find that two neighbouring bulk nodal rings form doubly degenerate lines in their drumhead-like surface states, which are protected by the combination of time-reversal and glide mirror symmetries $\mathcal{T}\bar{M}_y$. Our results indicate that surface degeneracies can be generically protected by symmetries such as $\mathcal{T}\bar{M}_y$, and phonons provide an ideal platform to explore such degeneracies., Comment: 13 pages, 5 figures

A six-wheeled companion exploration robot with an adaptive climbing mechanism is proposed and released for the complicated terrain environment of planetary exploration. Benefiting from its three-rocker-arm structure, the robot can adapt to complex terrain with its six wheels in contact with the ground during locomotion, which improves the stability of the robot. When the robot moves on the flat ground, it moves forward through the rotation of the wheels. When it encounters obstacles in the process of moving forward, the front obstacle-crossing wheels hold the obstacle, and the rocker arms on both sides rotate themselves with mechanical adaptivity to drive the robot to climb and cross the obstacle like crab legs. Furthermore, a parameterized geometric model is established to analyze the motion stability and the obstacle-crossing performance of the robot. To investigate the feasibility and correctness of design theory and robot scheme, a group of design parameters of the robot are determined. A prototype of the robot is developed, and the experiment results show that the robot can maintain stability in rugged terrain environments and has a certain ability to surmount obstacles.

The bandgap energy of the dilute bismuth GaBi x Sb1−x alloy vs. temperature is investigated in this study. Its reduced temperature-sensitiveness is because of the localized character of the valence band states (VBS). In order to describe the reduced temperature-sensitiveness of the bandgap energy, a new term including localized energy is added to Varshni's equation. It is found that the localized energy exhibits an increasing trend as the bismuth fraction increases, which indicates that the localized character of the VBS becomes strong with the increasing bismuth fraction. It is also found that the influence of the bismuth fraction on the temperature dependence of the bandgap energy of GaBi x Sb1−x is smaller than that of GaBi x As1−x . In addition, the element indium is undoubtedly a good candidate to lessen the bismuth fraction to realize that the spin-orbit-splitting (SOP) energy surpasses the bandgap energy in GaBi x Sb1−x .