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Chronic thromboembolic pulmonary hypertension (CTEPH) develops due to the accumulation of blood clots in the lung vasculature that obstructs flow and increases pressure. The mechanobiological factors that drive progression of CTEPH are not understood, in part because mechanical and hemodynamic changes in the small pulmonary arteries due to CTEPH are not easily measurable. Using previously published hemodynamic measurements and imaging from a large animal model of CTEPH, we applied a subject-specific one-dimensional (1D) computational fluid dynamic (CFD) approach to investigate the impact of CTEPH on pulmonary artery stiffening, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) in extralobar (main, right, and left) pulmonary arteries and intralobar (distal to the extralobar) arteries. Our results demonstrate that CTEPH increases pulmonary artery wall stiffness and decreases TAWSS in extralobar and intralobar arteries. Moreover, CTEPH increases the percentage of the intralobar arterial network with both low TAWSS and high OSI, quantified by the novel parameter φ , which is related to thrombogenicity. Our analysis reveals a strong positive correlation between increases in mean pulmonary artery pressure (mPAP) and φ from baseline to CTEPH in individual subjects, which supports the suggestion that increased φ drives disease severity. This subject-specific experimental-computational framework shows potential as a predictor of the impact of CTEPH on pulmonary arterial hemodynamics and pulmonary vascular mechanics. By leveraging advanced modeling techniques and calibrated model parameters, we predict spatial distributions of flow and pressure, from which we can compute potential physiomarkers of disease progression. Ultimately, this approach can lead to more spatially targeted interventions that address the needs of individual CTEPH patients.

The human body represents a collection of interacting systems that range in scale from nanometers to meters. Investigations from a systems perspective focus on how the parts work together to enact changes across spatial scales, and further our understanding of how systems function and fail. Here, we highlight systems approaches presented at the 2022 Summer Biomechanics, Bio-engineering, and Biotransport Conference in the areas of solid mechanics; fluid mechanics; tissue and cellular engineering; biotransport; and design, dynamics, and rehabilitation; and biomechanics education. Systems approaches are yielding new insights into human biology by leveraging state-of-the-art tools, which could ultimately lead to more informed design of therapies and medical devices for preventing and treating disease as well as rehabilitating patients using strategies that are uniquely optimized for each patient. Educational approaches can also be designed to foster a foundation of systems-level thinking.

Predictive high-fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch-control, stall/vortex-induced vibrations, and aftermarket add-ons. The biggest challenge with high-fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high-performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU-enabled open-source incompressible-flow hybrid-computational fluid dynamics framework, comprising the near-body unstructured grid solver Nalu-Wind, and the off-body block-structured-grid solver AMR-Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds-averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds-averaged Navier–Stokes is used for near-body flow and large eddy simulation is used for off-body flow. Being two-way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry-resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt-scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

Lower back pain continues to be a global epidemic, limiting quality of life and ability to work, due in large part to symptomatic disc degeneration. Development of more effective and less invasive biological strategies are needed to treat disc degeneration. In vitro models such as macro- or micro-bioreactors or mechanically active organ-chips hold great promise in reducing the need for animal studies that may have limited clinical translatability, due to harsher and more complex mechanical loading environments in human discs than in most animal models. This review highlights the complex loading conditions of the disc in situ, evaluates state-of-the-art designs for applying such complex loads across multiple length scales, from macro-bioreactors that load whole discs to organ-chips that aim to replicate cellular or engineered tissue loading. Emphasis was placed on the rapidly evolving more customizable organ-chips, given their greater potential for studying the progression and treatment of symptomatic disc degeneration. Lastly, this review identifies new trends and challenges for using organ-chips to assess therapeutic strategies.

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.

Abstract: A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing Design-by-Morphing (DbM), Large Eddy Simulation (LES), and Bayesian Optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of 3 optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8 %. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity of computational design before materialization.

We present a study directly measuring the electron‐beam‐induced plasticity of amorphous Al2O3 coatings. Core–shell nanostructures are employed as small‐scale model systems for two‐dimensional coatings made by atomic layer deposition (ALD). Copper nanowires (NWs) are used as substrates for ALD deposition, representing a model system for interconnects commonly found in integrated circuits. Experiments are performed in situ in a transmission electron microscope (TEM) and further analyzed with electron energy loss spectroscopy (EELS). Our in situ TEM tensile experiments reveal the highly plastic behavior of the ALD shell, which withstands a maximum strain of 188%. Comparable samples under beam‐off conditions show a brittle fracture, which underlines the effect of electron irradiation. The electron‐beam‐activated bond switching within the amorphous network enables compensation of the applied tensile strain, leading to viscous flow. By incorporating an intermediate nanocrystalline layer within the Al2O3 shell, the plasticity is suppressed and brittle fracture occurs. This work directly demonstrates the tuning of mechanical properties in amorphous ALD structures through electron irradiation.

Coupling of power-to-gas processes with underground gas storage could effectively allow surplus electricity to be stored for later use. Depleted hydrocarbon reservoirs could be used as stores, but practical experience of hydrogen storage in such sites is limited. Here we present data from a field trial that stored 119,353 m3 of hydrogen admixed to natural gas in a depleted hydrocarbon reservoir. After 285 days, hydrogen recovery was 84.3%, indicating the process’s technical feasibility. Additionally, we report that microbes mediated hydrogen conversion to methane. In laboratory experiments studying mesocosms that mimic real reservoirs, hydrogen and carbon dioxide were converted to methane (0.26 mmol l−1 h−1 evolution rate) reproducibly over 14 cycles in 357 days. This rate theoretically allows 114,648 m3 of methane per year to be produced in the test reservoir (equivalent to ~1.08 GWh). Our research demonstrates the efficiency of hydrogen storage and the importance of geo-methanation in depleted hydrocarbon reservoirs.

Layered double hydroxide (LDH) phases that form during cement hydration can incorporate a variety of interlayer anions in their interlayer positions. Here, a range of phases of general formula [MII(1−x)MIII(x)(OH)2][An−]x/n·zH2O were synthesized, where MII = Mg2+ (hydrotalcite) or Ca2+ (AFm), MIII = Al3+ such that [MII/Al] = 2 (Ca and Mg, atomic units) or 3 (Mg only), and A = Cl−, Br−, or I−. All the synthesized phases were characterized to assess their composition, density, and crystal structure. By approach from undersaturation, the solubility data of these compounds was measured at 5, 25, and 60°C. This thermochemical data was used to successfully model their formation using thermodynamic modeling and to infer the fields of stability of these compounds for conditions of relevance to cementitious systems. It is seen that halide-containing hydrotalcite phases strongly compete with hydroxide-containing hydrotalcite, with the latter prevailing at high pH. In contrast, halide-containing AFm compounds are more stable compared with hydroxide-containing AFm compositions.

In turbulent flows, energy production is associated with highly organized structures, known as coherent structures. Since these structures are three dimensional, their detection remains challenging in the most common situation in experiments, when single-point temporal measurements are considered. While previous research on coherent structure detection from time series employs a thresholding approach, either in spectral or temporal domain, the thresholds are ad hoc and vary significantly from one study to another. To circumvent this issue, we introduce the level-crossing method and show how specific features of a turbulent time series associated with coherent structures can be objectively identified, without assigning a priori any arbitrary threshold. By using two wall-bounded turbulence time-series datasets (at a Reynolds number of 104), we successfully extract through level-crossing analysis the impacts of coherent structures on turbulent dynamics and therefore open an alternative avenue in experimental turbulence research. By utilizing this framework further, we discover a metric, characterized by a statistical asymmetry between the peaks and troughs of a turbulent signal, to quantify inner-outer interaction in wall turbulence. Most importantly, through phase-randomized surrogate data modeling, we demonstrate that the level-crossing statistics are quite sensitive to the nonlinear dependencies in a turbulent signal. Physically, this finding implies that the large-scale coherent structures modulate the near-wall turbulent dynamics through a nonlinear interaction associated with low-speed streaks, a mechanism not identifiable from spectral analysis alone. Moreover, a connection is established between extreme value statistics and level-crossing analysis, thereby allowing additional possibilities to study extreme events in other dynamical systems.

Between 1982 and 1986, 126 patients who had undergoneACL reconstruction were followed in a prospectivemanner. One year follow-up statistics were reviewedfor the presence of 13 different complications.The most prevalent complications were quadricepsweakness, flexion contracture, and patellofemoral pain.Quadriceps weakness (strength less than 80% of thenormal side) was present in 65% of patients and correlatedpositively with flexion contracture, patellar irritability,and ACL reconstructions using patellar tendongrafts. Flexion contracture of 5° or more was presentin 24% of patients and correlated positively with increasedage and patellar irritability. Patellofemoral painwas present in 19% of patients and correlated positivelywith flexion contracture.Clinical relevance: The three most common complicationsof knee ligament surgery are shown to bestrongly interrelated. It is likely that a causal relationshipis present in which flexion contracture causes patellofemoralirritability, and that both of these factors, aloneor in combination, result in quadriceps weakness. If thistheory is correct, then it is crucial that postoperativerehabilitation programs place a major emphasis on theavoidance of flexion contracture.

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.

This paper describes a synthesis technique that constrains a spatial serial chain into a single degree-of-freedom mechanism using planar six-bar function generators. The synthesis process begins by specifying the target motion of a serial chain that is parameterized by time. The goal is to create a mechanism with a constant velocity rotary input that will achieve that motion. To do this we solve the inverse kinematics equations to find functions of each serial joint angle with respect to time. Since a constant velocity input is desired, time is proportional to the angle of the input link, and each serial joint angle can be expressed as functions of the input angle. This poses a separate function generator problem to control each joint of the serial chain. Function generators are linkages that coordinate their input and output angles. Each function is synthesized using a technique that finds 11 position Stephenson II linkages, which are then packaged onto the serial chain. Using pulleys and the scaling capabilities of function generating linkages, the final device can be packaged compactly. We describe this synthesis procedure through the design of a biomimetic device for reproducing a flapping wing motion.

Polycrystalline silicon (polysilicon) is the most commonly used structural material of microscopic electromechanical devices, such as sensors and actuators. Almost all of these miniaturized devices contain mechanical elements experiencing high-frequency loading cycles. Due to the rapidly accumulating loading cycles and very small thicknesses of these microstructures, basic understanding of fatigue crack growth in polysilicon at the microscale is critical to the design of durable microdevices that satisfy application requirements. In this investigation, fatigue crack growth in a typical polysilicon microstructure subjected to multi-axial loading was analyzed with the finite element method. To account for the inherent heterogeneity and anisotropy of polysilicon at the microscale, a Poisson-Voronoi tessellation was incorporated in the highly stressed region of the resonating microdevice to model a polycrystalline microstructure. Simulation results illuminated the effect of local texture on the direction and rate of crack growth. Transgranular or intergranular crack growth were predicted, depending on the angle between the crack-path direction and the grain boundary and the fracture resistance of the grain and the grain boundary. From a fundamental fracture mechanics perspective, the computational approach developed in this study provides a capability for examining the effect of local texture anisotropy on cracking in polycrystalline microstructures.

The solid-electrolyte interphase (SEI) critically governs the performance of rechargeable batteries. An ideal SEI is expected to be electrically insulative to prevent persistently parasitic reactions between the electrode and the electrolyte and ionically conductive to facilitate Faradaic reactions of the electrode. However, the true nature of the electrical properties of the SEI remains hitherto unclear due to the lack of a direct characterization method. Here we use in situ bias transmission electron microscopy to directly measure the electrical properties of SEIs formed on copper and lithium substrates. We reveal that SEIs show a voltage-dependent differential conductance. A higher rate of differential conductance induces a thicker SEI with an intricate topographic feature, leading to an inferior Coulombic efficiency and cycling stability in Li∣∣Cu and Li∣∣LiNi0.8Mn0.1Co0.1O2 cells. Our work provides insight into the targeted design of the SEI with desired characteristics towards better battery performance.

Abstract: This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input–output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion–extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject’s kinematics data.

Abstract: This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An SVM soft margin training process is introduced as a classification of heterogeneous material points, and image segmentation is accomplished by identifying support vectors through a local regularized optimization problem. In addition, an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces. The proposed method modifies the smooth kernel functions with a regularized Heaviside function concerning the material interfaces to alleviate Gibb's oscillations. This IM-RKPM is formulated without introducing duplicated degrees of freedom associated with the interface nodes commonly needed in the conventional treatments of weak discontinuities in the meshfree methods. Moreover, IM-RKPM can be implemented with various domain integration techniques, such as Stabilized Conforming Nodal Integration (SCNI). The extension of the proposed method to 3-dimension is straightforward, and the effectiveness of the proposed method is validated through the image-based modeling of polymer-ceramic composite microstructures.

Correction to: Nature Energy. Published online 25 May 2023. This paper was originally published under a standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the online version of the article.

Fuel cells are a promising next-generation energy-conversion technology designed to replace internal combustion engines in transportation applications. However, much work remains to optimize them. Operation at high humidities causes liquid water droplet formation on Pt catalyst particles during oxygen reduction, potentially impeding reactant arrival to the reactive electrode. In this work, four different cases of water droplet growth in fuel-cell catalyst layers are considered: pinned or advancing droplets on a bare Pt surface, advancing droplets on a Nafion film, and water-layer growth in carbon nanopores. Transient drop growth is captured with a combination of mass, species mass, and momentum balances, and the subsequent limiting current is determined via oxygen diffusion and Tafel kinetics. Water droplets are found not to be mass-transfer limiting due to the relatively large liquid-gas area compared to the Pt nanoparticle. Mass-transfer-limited behavior is calculated in carbon nanopores.

Individual demand for emerging technologies can be influenced by the demand of other individuals within defined peer groups. These so-called peer effects have been demonstrated in emerging clean energy technologies such as rooftop solar. To date, peer effects have disproportionately driven solar adoption among relatively affluent households. Here, we use household-level income estimates of rooftop solar adopters to explore how peer effects drive adoption for low-income households. We find evidence of peer effects for both high- and low-income households and find that peer effects are generally stronger within than across income groups. Our results indicate that peer effects translate to adoption less frequently among low-income households. These results suggest that low-income peer effects are mitigated by barriers to low-income adoption. Heterogeneous peer influence is another demand shifter that explains the inequitable adoption of emerging technologies.

Noninvasive compression-induced anterior cruciate ligament rupture (ACL-R) is an easy and reproducible model for studying post-traumatic osteoarthritis (PTOA) in mice. However, equipment typically used for ACL-R is expensive, immobile, and not available to all researchers. In this study, we compared PTOA progression in mice injured with a low-cost custom ACL-rupture device (CARD) to mice injured with a standard system (ElectroForce 3200). We quantified anterior-posterior (AP) joint laxity immediately following injury, epiphyseal trabecular bone microstructure, and osteophyte volume at 2 and 6 weeks post injury using micro-computed tomography, and osteoarthritis progression and synovitis at 2 and 6 weeks post injury using whole-joint histology. We observed no significant differences in outcomes in mice injured with the CARD system compared to mice injured with the Electroforce (ELF) system. However, AP joint laxity data and week 2 micro-CT and histology outcomes suggested that injuries may have been slightly more severe and PTOA progressed slightly faster in mice injured with the CARD system compared to the ELF system. Altogether, these data confirm that ACL-R can be successfully and reproducibly performed with the CARD system and that osteoarthritis (OA) progression is mostly comparable to that of mice injured with the ELF system, though potentially slightly faster. The CARD system is low cost and portable, and we are making the plans and instructions freely available to all interested investigators in the hopes that they will find this system useful for their studies of OA in mice.

The present experimental study investigated the dynamics of single- and multi-port gaseous jet diffusion flames exposed to acoustic excitation via a standing wave situated in a closed waveguide at atmospheric pressure. High-speed imaging of the oscillatory flame was analysed via proper orthogonal decomposition (POD), revealing distinct signatures in both mode shapes and phase portraits for transitions in the acoustically coupled combustion process. For Reynolds numbers between 20 and 100, and for low to moderate forcing amplitudes, the flame exhibited sustained oscillatory combustion (SOC) that was highly coupled to the acoustic forcing. Frequency analysis of the temporal POD modes accurately recovered the forcing frequency and its higher harmonics. At higher forcing amplitudes, a multi-frequency response was observed, resulting from a combination of the forcing frequency and much lower frequency oscillations due to periodic lift-off and reattachment (PLOR) of the flame, preceding a transition to flame blow-off (BO). For both single- and triple-jet flames, transitions from SOC to PLOR to BO were characterized by significant alterations in primary modal energetic content, deflection and eventual smearing in phase portraits, and the development of additional frequencies in modal spectra, although transitional behaviour for the triple jet flames involved additional complexity in the dynamics due to its structure. These features provide the potential for the development of reduced-order models that can characterize and predict acoustically coupled combustion behaviour.

The “U-phase,” a sodium-containing (alumino-ferrite-monosubstituent) AFm phase, has been observed to form in sodium-enriched highly alkaline cementitious systems, for example, of relevance to nuclear waste, and saline industrial brine management. But, minimal information is available of the U-phase's (e.g., solubility or thermodynamic properties) due to its limited stability and its tendency to transform into ettringite or monosulfoaluminate. Herein, the U-phase was systematically synthesized at four temperatures (5, 15, 25, and 50°C) and fully characterized in terms of its thermochemical properties. The average composition of the synthesized U-phase (4CaO·Al2O3·1.85SO3·0.85Na2O·12H2O) deviates slightly from typical disclosures in the literature. The solubility product of the U-phase formation was measured from conditions of oversaturation. The measured thermodynamic data accurately predicted experimental observations of U-phase formation in cementitious environments. In general, it was noted that the U-phase forms and persists (i.e., remains stable) at pH > 13.7 and [Na+] concentrations superior to 1 mol/L.

Modeling deformation twin nucleation in magnesium has proven to be a challenging task. In particular, the absence of a heterogeneous twin nucleation model which provides accurate energetic descriptions for twin-related structures indicates a need to more deeply understand twin energetics. To address this problem, molecular dynamics simulations are performed to follow the energetic evolution of { 10 1 ‾ 2 } tension twin embryos nucleating from an asymmetrically-tilted grain boundary. The line, surface and volumetric terms associated with twin nucleation are identified. A micromechanical model is proposed where the stress field around the twin nucleus is estimated using the Eshelby formalism, and the contributions of the various twin-related structures to the total energy of the twin are evaluated. The reduction in the grain boundary energy arising from the change in character of the prior grain boundary is found to be able to offset the energy costs of creating the other interfaces. The defect structures bounding the stacking faults that form inside the twin are also found to possibly have significant energetic contributions. These results suggest that both of these effects could be critical considerations when predicting twin nucleation sites in magnesium.

Damage tolerant structures require accurate fatigue crack growth rate models for life prediction. Central to these models are fracture mechanics similitude and empirically gathered growth rates. In this work, standard test methods failed to achieve similitude for 17-4PH stainless steel round-rod but succeeded for plate. Material product form differences are interrogated through constant ΔK tests, crack path analysis, residual stress (RS) characterization, and growth rate simulation. Analyses revealed that quench-induced RS in the round-rod promoted a non-planar, 3D crack path with closure effects. Strategies to mitigate the RS/path effects are evaluated by controlling constraint, closure, environment, and heat treatment.

We present comparisons of kinetic Monte Carlo (kMC) simulations of isothermal short-range ordering (SRO) and clustering (SRC) kinetics in binary FCC alloys with a mean-field concentration wave (CW) model. We find that the CW model is able to give order-of-magnitude agreement with kMC simulations for ordering/clustering relaxation times over a wide range of temperatures and compositions. The advantage of the CW model is that it does not require parameterization of vacancy hopping energy barriers, which, for a concentrated alloy, becomes prohibitive. We assess limits in the accuracy of the model, and discuss the effect of cooling rates as well as the extension to multi-component systems. Ultimately, the simplicity and performance of the CW model compared to kMC simulations suggests that it is a useful tool to connect with models of properties dependent on SRO/SRC as well as for designing thermal treatments to control formation of SRO/SRC.

The creation of van der Waals (vdW) ferromagnets with tunable Curie temperature (TC) and magnetic anisotropy is essential in developing vdW magnet-based devices. Here, we report an effective and reliable method for modulating the magnetic properties of vdW Fe5GeTe2 by site-specific Ga+ implantation. In this study, we report an easy axis in the ab-plane for bulk Fe5GeTe2 (TC = 310 K) and an axis out of the plane for thin Fe5GeTe2 flakes (TC = 290 K). Combining element-resolved photoemission electron microscopy and spatially resolved magneto-optic Kerr microscopy, we find that the implantation of a tiny amount of 10−3 Ga+·Å−3 in Fe5GeTe2 greatly enhances the TC from 290 to 360 K and switches the magnetic easy axis from the out-of-plane c axis to the ab-plane. The room-temperature x-ray magnetic circular dichroism signal is enhanced from 0% to 9% at an implantation level of 10−2 Ga+·Å−3. These results provide new opportunities for tailoring the magnetic properties of vdW materials beyond room temperature.

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal of the magnetization by an ultrashort laser pulse is extremely important. We demonstrate single-shot switching of Tb/Co-multilayer based nanoscale MTJs by combining the optical writing and the electrical read-out methods. A 90-fs-long laser pulse switches the magnetization of the storage layer (SL). The change in the tunneling magnetoresistance (TMR) between the SL and a reference layer (RL) is probed electrically across the oxide barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The nature of the magnetization reversal of both SL and RL in a continuous film is probed with a depth-resolved magneto-optical Kerr effect (MOKE) magnetometry. The ultrafast dynamics in the continuous full-MTJ stack is investigated with the time-resolved pump–probe technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices.

Poly-ether-ether-ketone (PEEK) and PEEK composites are outstanding candidates for biomedical applications, such as orthopedic devices, where biocompatibility and modulus match with surrounding tissue are requisite for long-term success. The mechanical properties can be optimized by incorporating fillers such as continuous and chopped carbon fibers. While much is known about the mechanical and tribological behavior of PEEK composites, there are few articles that summarize the viability of using PEEK reinforced with carbon fibers in orthopedic implants. This paper reviews biocompatibility, tribological, and mechanical studies on PEEK and their composites with carbon fibers, notably PEEK reinforced with polyacrylonitrile (PAN)-based carbon fibers and PEEK reinforced with pitch-based carbon fibers, for application in orthopedics and total joint replacements (TJRs). The main objectives of this review are two-fold. Firstly, this paper aims to assist designers in making informed decisions on the suitability of using PEEK and PEEK composites in orthopedic applications; as it is not well understood how these materials perform on the whole in orthopedics and TJRs. Secondly, this paper aims to serve as a centralized paper in which researchers can gain information on the tribological and mechanical advancements of PEEK and PEEK composites.