Search

Abstract We use magnetization measurements under pressure along with ab initio and cluster many-body calculations to investigate magnetism of the Kitaev candidate Li2RhO3. Hydrostatic compression leads to a decrease in the magnitude of the nearest-neighbor ferromagnetic Kitaev coupling K 1 and the corresponding increase in the off-diagonal anisotropy Γ1, whereas the experimental Curie-Weiss temperature changes from negative to positive with the slope of +40 K/GPa. On the other hand, spin freezing persists up to at least 3.46 GPa with the almost constant freezing temperature of 5 K that does not follow the large changes in the exchange couplings and indicates the likely extrinsic origin of spin freezing. Magnetic frustration in Li2RhO3 is mainly related to the interplay between ferromagnetic K 1 and antiferromagnetic Γ1, along with the weakness of the third-neighbor coupling J 3 that would otherwise stabilize zigzag order. The small J 3 distinguishes Li2RhO3 from other Kitaev candidates.

Abstract Pyroelectric and photovoltaic effects are vital in cutting-edge broadband sensors and solar energy harvesting. Recent advances revealed great potential of the bulk photovoltaic effect in two-dimensional (2D) materials to surpass the Shockley-Queiseer limit. Moreover, the atomic thickness, high thermal conductivity and room-temperature ferroelectricity endow 2D ferroelectrics with a superior pyroelectric response. Herein, we combined direct pyroelectric-photovoltaic measurements in 2D α-In2Se3. The results reveal a gigantic pyroelectric coefficient of ∼30.7 mC/m2K and a figure of merit of ∼135.9 m2/C. Moreover, a coupled pyroelectric-photovoltaic effect was demonstrated, where the pyroelectric current follows the temperature derivative, while the short-circuit current follows temperature. Finally, we utilized the intercoupled ferroelectricity of In2Se3 to realize a non-volatile, self-powered photovoltaic memory operation, demonstrating stable short-circuit current switching with 103 ON-OFF ratio. The coupled pyroelectric-photovoltaic effect, along with reconfigurable photocurrent, pave the way for a novel integrated thermal and optical response, in-memory logic and energy harvesting.

Abstract Van der Waals heterostructures offer unprecedented opportunities to design next stage functional electronic 2D devices. Most architectures of those devices incorporate large bandgap insulator – hBN as an encapsulating or tunnel barrier layers. Here, we use an architecture of gated vertical tunnelling transistors to study a generic phenomenon of electron resonant tunnelling through adjacent localised electronic states in hBN barriers. We demonstrate that in the case of two localised electronic states, the tunnelling can be of inelastic nature giving rise to explicitly strong resonant features. It allows accurate tunnelling spectroscopy of delicate features of emitting and collecting layer electronic density of states, such as second neutrality point bandgap of moiré monolayer and electric field induced bandgap of Bernal bilayer graphene. Our findings enrich the perception of interaction mechanisms among the localised electronic states in hBN barriers paving the way for future explorations into their applications.

Abstract The successful integration of ultrathin high-κ insulators is essential for the advancement of ultra-scaled field-effect transistors (FETs) based on two-dimensional (2D) semiconductors in future technology nodes. However, defects within the high-κ stack or at the interfaces can significantly degrade the performance of these “interface-only” devices, raising questions regarding their long-term reliability. Here, we study the reliability of monolayer MoS2 FETs on ultra-thin high-κ HfO2. Interestingly, we observe a two-stage threshold voltage shift (ΔV TH) under positive bias temperature stress (PBTS) and hot carrier degradation (HCD). This two-stage ΔV TH is absent in devices fabricated on exfoliated hBN, suggesting that the donor state generation (negative ΔV TH) is induced by atomic-layer-deposition (ALD) processes in HfO2-based devices. Elastic Recoil Detection Analysis (ERDA) indicates that hydrogen, likely from the ALD precursor, is a probable cause, highlighting the need for ALD process refinement to improve 2D FET stability for CMOS compatibility.

Abstract Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard U and V parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications.

Abstract Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.

Abstract Biofunctionalized magnetite nanoparticles offer unique multifunctional capabilities that can drive nanomedical innovations. Designing synthetic bioorganic coatings and controlling their molecular behavior is crucial for achieving superior performance. However, accurately describing the interactions between bio-inorganic nanosystem components requires reliable computational tools, with empirical force fields at their core. In this work, we integrate first-principles calculations with mainstream force fields to construct and simulate atomistic models of pristine and biofunctionalized magnetite nanoparticles with quantum mechanical accuracy. The practical implications of this approach are demonstrated through a case study of PEG (polyethylene glycol)-coated magnetite nanoparticles in physiological conditions, where we investigate how polymer chain length, in both heterogeneous and homogeneous coatings, impacts key functional properties in advanced nanosystem design. Our findings reveal that coating morphology controls polymer ordering, conformation, and polymer corona hydrogen bonding, highlighting the potential of this computational toolbox to advance next-generation magnetite-based nanosystems with enhanced performance in nanomedicine.

Abstract We conduct a systematic investigation of the role of Hubbard U corrections in electronic structure calculations of two-dimensional (2D) materials containing 3d transition metals. Specifically, we use density functional theory (DFT) with the PBE and PBE+U approximations to calculate the crystal structure, band gaps, and magnetic parameters of 638 monolayers. Based on a comprehensive comparison to experiments we first establish that the inclusion of the U correction worsens the accuracy for the lattice constants. Consequently, PBE structures are used for subsequent property evaluations. The band gaps show a significant dependence on U. In particular, for 134 (21%) of the materials the U parameter induces a metal-to-insulator transition. For the magnetic materials we calculate the magnetic moment, magnetic exchange coupling, and magnetic anisotropy parameters. In contrast to the band gaps, the size of the magnetic moments shows only weak dependence on U. Both the exchange energies and magnetic anisotropy parameters are systematically reduced by the U correction. On this basis we conclude that the Hubbard U correction will lead to lower predicted Curie temperatures in 2D materials. All the calculated properties are available in the Computational 2D Materials Database (C2DB).

Abstract The electric field control of magnetization is of significant interest in materials science due to potential applications in many devices such as sensors, actuators, and magnetic memories. Here, we report magnetization changes generated by an electric field in ferromagnetic Ga1−x Mn x N grown by molecular beam epitaxy. Two classes of phenomena have been revealed. First, over a wide range of magnetic fields, the magnetoelectric signal is odd in the electric field and reversible. Employing a macroscopic spin model and atomistic Landau-Lifshitz-Gilbert theory with Langevin dynamics, we demonstrate that the magnetoelectric response results from the inverse piezoelectric effect that changes the trigonal single-ion magnetocrystalline anisotropy. Second, in the metastable regime of ferromagnetic hystereses, the magnetoelectric effect becomes non-linear and irreversible in response to a time-dependent electric field, which can reorient the magnetization direction. Interestingly, our observations are similar to those reported for another dilute ferromagnetic semiconductor Cr x (Bi1−y Sb y )1−x Te3, in which magnetization was monitored as a function of the gate electric field. Those results constitute experimental support for theories describing the effects of time-dependent perturbation upon glasses far from thermal equilibrium in terms of an enhanced effective temperature.

Abstract Continuous 1D growth of crystals is among one of the long-standing goals in material science. Classical generic methods for 1D crystal growth such as guided disposition restrict side growth by external bounding, and become difficult as the aspect ratio increases. Here, we find that continuous 1D growth of crystals can be achieved during dendrite formation, where the fractal dendrite crystal growth intrinsically restricts side broadening. We induce nanoparticle alignment in a solvent system with a polymer nanoparticle dispersion. This polymer additive further enabled an oriented aggregation mechanism during dendrite growth. The integration of these two mechanisms into the dendrite growth process regulates the dendrite shape. Such aggregated mono-crystalline dendrite branches can reach an ultra high aspect ratio (over 10000:1) with uniform diameter and orientation. We show an example application of such dendrite to prepare a high aspect ratio nanowire. This pathway may be extended for general 1D crystal growth system in the future.

Abstract Achieving homogeneous oil and water mixing requires thorough droplet dispersions of both liquids. An additional component, such as a surfactant, particle, solvent or ion is normally required to stabilize the small liquid droplets by providing sufficiently low interfacial tensions or strong repulsive forces. Here we show that very stable oil-in-water mixtures can be created even at very high oil volume fractions by building multiple, high-strength hydrogen bonding interactions between water and a biocompatible and biodegradable oil trimethylolpropane trioleate, which make the oil molecules at the contact interface behave as pseudo-surfactants with ultrahigh interfacial activity. The resultant oil microdroplets can be reversibly thermosecreted from the continuous aqueous phase and lead to controllable formation/dissociation and switchable lubrication of the binary liquid mixture owing to the inherent thermal responsiveness of the intermolecular hydrogen bonding. Interestingly, although water is uncovered to possess a relatively poor lubricity, the liquid mixture yields an optimized lubrication that is even exceedingly comparable to pure oil at a water volume fraction as high as 80%.

Abstract Li-S batteries are a promising energy storage technology due to their high theoretical capacity, but they suffer from issues such as poor cycle stability and capacity loss over time. Here, we investigate the impact of carrageenan, a polysaccharide binder derived from red algae, on the performance of Li-S batteries. Electrode slurries are prepared without the toxic solvent N-methyl-2-pyrrolidone, using only water as a solvent and dispersant, making the process potentially scalable and cost-effective. With the optimal amount of carrageenan, we observe a capacity retention of 69.1% at 4 C after 1000 charge-discharge cycles. Carrageenan-based electrodes deliver 30% higher capacity than those made with the industry-standard polyvinylidene fluoride binder. X-ray photoelectron spectroscopy analysis confirms the chemical binding of carrageenan to the sulfur active material, and transmission X-ray absorption spectroscopy reveals that carrageenan effectively traps shorter-chain lithium polysulfides, improving the overall battery performance.

Abstract Lightweight aluminum alloy is one of the widely used structural materials for various industries due to its low density, high strength-to-weight ratio, good corrosion resistance, and excellent recyclability. However, complex service conditions often result in material degradation due to simultaneous mechanical and corrosion attacks on the metal surfaces, such as tribocorrosion. This phenomenon represents a complex multiphysics challenge, wherein the tribocorrosion-induced material loss emerges as a function of varied environmental, mechanical, and electrochemical descriptors, each entailing distinct yet interlinked physical processes. The pursuit of simultaneous optimization across multiple material properties to enhance the overall tribocorrosion resistance is hampered by the inherent trade-offs between wear and corrosion resistance. Addressing this complexity, our study develops a novel methodology fusing machine-learning (ML) and genetic algorithm (GA)-based optimization techniques to tailor aluminum-based alloys for enhanced tribocorrosion resistance. Leveraging an experimentally validated multiphysics finite element analysis (FEA) model, we have used six key material parameters to model the tribocorrosion performance of Al alloys over a large property space. The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. Crucially, our high-throughput screening pinpoints corrosion current density and yield strength as two pivotal parameters influencing tribocorrosion behavior. Harnessing GA optimization alongside the ML model, we efficiently identify a suite of optimal material properties—encompassing both mechanical and electrochemical aspects—for aluminum alloys, resulting in superior tribocorrosion resistance. This selection is substantiated through validation against high-fidelity FEA simulation results. This data-driven framework holds promise for tailoring tribocorrosion-resistant materials beyond aluminum alloys, adaptable to a wide range of metals and service environments.

Abstract One major concern in molten salt reactor (MSR) is structural material corrosion. In this study, the corrosion performance of 316L stainless steel weld joint in NaCl–MgCl2 eutectic molten salt loop was studied and compared with its base metal. The corrosion of 316L stainless steel in a molten salt loop appears to create voids. The created voids appear densely in base metal while spread discretely as dendritic structures in the weld joint.

Abstract Increasing the proximity of microelectrode arrays (MEA) to targeted neural tissues can establish efficient neural interfaces for both recording and stimulation applications. This has been achieved by constructing protruding three-dimensional (3D) structures on top of conventional planar microelectrodes via additional micromachining steps. However, this approach adds fabrication complexities and limits the 3D structures to certain shapes. We propose a one-step fabrication of MEAs with versatile microscopic 3D structures via “microelectrothermoforming (μETF)” of thermoplastics, by utilizing 3D-printed molds to locally deform planar MEAs into protruding and recessing shapes. Electromechanical optimization enabled a 3D MEA with 80 μm protrusions and/or recession for 100 μm diameter. Its simple and versatile shaping capabilities are demonstrated by diverse 3D structures on a single MEA. The benefits of 3D MEA are evaluated in retinal stimulation through numerical simulations and ex vivo experiments, confirming a threshold lowered by 1.7 times and spatial resolution enhanced by 2.2 times.

Abstract The magnetoelectric (ME) effect is a fundamental concept in modern condensed matter physics and represents the electrical control of magnetic polarisations or vice versa. Two-dimensional (2D) van-der-Waals (vdW) magnets have emerged as a new class of materials and exhibit novel ME effects with diverse manifestations. This review emphasizes some important recent discoveries unique to vdW magnets: multiferroicity on two dimensions, spin-charge correlation, atomic ME effect and current-induced intrinsic spin-orbit torque, and electrical gating control and magnetic control of their electronic properties. We also highlight the promising route of utilizing quantum magnetic hetero- or homo-structures to engineer the ME effect and corresponding spintronic and optoelectronic device applications. Due to the intrinsic two-dimensionality, vdW magnets with those ME effects are expected to form a new, exciting research direction.

Abstract Motivated by the on-going discussion on the nature of magnetism in the quantum Ising chain CoNb2O6, we present a first-principles-based analysis of its exchange interactions with additional modeling, addressing drawbacks of a purely density functional theory ansatz. This method allows us to extract and understand the origin of the magnetic couplings—including all symmetry-allowed terms - and resolve conflicting model descriptions in CoNb2O6. We find that the twisted Kitaev chain and transverse-field ferromagnetic Ising chain views are mutually compatible, although additional off-diagonal exchanges are required for a complete picture. We show that the dominant exchange interaction is a ligand-centered process—involving e g electrons -, rendered anisotropic by low-symmetry crystal fields in CoNb2O6, resulting in dominant Ising exchange. Smaller bond-dependent anisotropies are found to originate from d − d kinetic exchange processes involving t 2g electrons. We demonstrate the validity of our low-energy model by comparing its predictions to measured THz and INS spectra.

Abstract The deuterium isotope effect on the honeycomb iridate H3LiIr2O6, a quantum spin-orbit-entangled liquid, was examined by synthesizing D3LiIr2O6. The structural refinements indicate the different character of the interlayer OH and OD bonds, which results in a giant isotope effect on the magnetic interactions; the antiferromagnetic Curie-Weiss temperature |θ CW| of D3LiIr2O6 increases to ~ 170 K from ~ 100 K of H3LiIr2O6. Nevertheless, the quantum liquid state is robust against the deuterium isotope exchange in contrast to the theoretical prediction that the Kitaev spin liquid is stable only for a limited phase space of magnetic interactions. The bond- and site disorders associated with disordered OD(H) bonds, in combination with Kitaev physics, may play a role in realizing the quantum liquid state.

Abstract 2D transition metal dichalcogenide (TMD) materials have attracted interest due to their remarkable excitonic, optical, electrical, and mechanical properties, which are dependent on their crystal structure. Consequently, controlling the crystal structure of these materials is essential for fine-tuning their performance, e.g., linear and nonlinear optical, as well as charge transport properties. While various phase-switching TMD materials are available, their transitions are often irreversible. Here, we investigate the mechanism of a light-induced reversible phase transition in mono- and bilayer rhenium disulfide (ReS2). Our observations, based on transmission electron microscopy, nonlinear spectroscopy, and density functional theory, reveal a transition from the ground $${\rm{T}}^{\prime\prime}$$ T ″ (double-distorted T) to the metastable $${\rm{H}}^{\prime}$$ H ′ (distorted H) phase under femtosecond laser irradiation or influence of highly-energetic electrons. We show that the formation of sulfur vacancies facilitates this phenomenon. Our findings pave the way toward manipulating the crystal structure of ReS2 and possibly its heterostructures.

Abstract The recent discovery of the persistence of long-range magnetic order when van der Waals magnets are thinned towards monolayers provides a tunable platform for engineering of novel magnetic structures and devices. Here, we study the evolution of the electronic structure of CrGeTe3 as a function of surface electron doping. From angle-resolved photoemission, we observe spectroscopic fingerprints that this electron doping drives a marked increase in T C, reaching values more than double that of the undoped material, in agreement with recent studies using electrostatic gating. Together with density functional theory calculations and Monte Carlo simulations, we show that, surprisingly, the increased T C is mediated by the population of spin-minority Cr t 2g states, forming a half-metallic 2D electron gas. This promotes a novel variant of double exchange, and unlocks a significant influence of Ge – which was previously thought to be electronically inert in this system – in mediating Cr-Cr exchange.

Abstract Lone pairs are widely prevalent in various types of molecules and have a significant impact on the band structure, carrier transport, and dielectric response, which are the key factors for exploring the underlying mechanism of phenomena in opto-spintronics. In this work, nitrogen substitutions with nonequivalent hybridization are adopted to obtain different types of organic charge transfer crystals, where lone pairs are generated to weaken the interactions between donors and acceptors, resulting in a blueshift in photoluminescence and a weaker electron-lattice coupling. Moreover, lone pairs could further strengthen the ability to transfer energy and spin angular momentum to the lattice vibration to enhance spin resonance. Additionally, due to the effect of lone pairs, the spin density inside the crystals is redistributed to tune the transition between the singlet and triplet states. Overall, crystals with lone pairs demonstrate a more diverse set of magnetic, optical, and spin-related properties.

Abstract It is well-known that exciton effects are determinant to understanding the optical absorption spectrum of low-dimensional materials. However, the role of excitons in nonlinear optical responses has been much less investigated at the experimental level. Additionally, computational methods to calculate nonlinear conductivities in real materials are still not widespread, particularly taking into account excitonic interactions. We present a methodology to calculate the excitonic second-order optical responses in 2D materials relying on: (i) ab initio tight-binding Hamiltonians obtained by Wannier interpolation and (ii) solving the Bethe-Salpeter equation with effective electron-hole interactions. Here, in particular, we explore the role of excitons in the shift current of monolayer materials. Focusing on MoS2 and GeS monolayer systems, our results show that 2p-like excitons, which are dark in the linear response regime, yield a contribution to the photocurrent comparable to that of 1s-like excitons. Under radiation with intensity ~104W/cm2, the excitonic theory predicts in-gap photogalvanic currents of almost ~10 nA in sufficiently clean samples, which is typically one order of magnitude higher than the value predicted by independent-particle theory near the band edge.

Abstract Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10−12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

Abstract Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.g., total energy, energy above the convex hull, energy band gap) and data-scarce mechanical properties (e.g., bulk and shear modulus). Our model incorporates four-body interactions, capturing periodicity and structural characteristics. It outperforms state-of-the-art models in 8 materials property regression tasks. Also, this model predicts local atomic environments and global structural features better than several models. Transfer learning addresses mechanical property data scarcity, while separate architecture analysis allows application to materials lacking crystal structure information. Our framework’s interpretability aids in understanding elemental contributions, enhancing material design and discovery. Continuous advancements promise further performance improvements, driving efficient and accurate materials property prediction.

Abstract Perovskites at the crossover between ferroelectric and relaxor are often used to realize dielectric capacitors with high energy and power density and simultaneously good efficiency. Lead-free Bi0.5Na0.5TiO3 is gaining importance in showing an alternative to lead-based devices. Here we show that (1-x)Bi0.5Na0.5TiO3 – xBaZr y Ti 1-y O3 (best: 0.94Bi0.5Na0.5TiO3 -0.06BaZr0.4Ti0.6O3) shows an increase of recoverable energy density and electric breakdown upon chemical substitution. In thin films derived from Chemical Solution Deposition, we observed that polarization peaks at the morphotropic phase boundary at x = 0.06. While Zr substitution results in reduced polarization, it enhances both efficiency and electric breakdown strength, ultimately doubling the recoverable energy density and the metallization interface by lowering surface roughness. Our dielectric capacitor shows

Abstract Multiferroic materials can host a plethora of intriguing phenomena due to the presence of multiple ferroic properties that break both spatial inversion symmetry and time reversal symmetry at an observable scale. Hexagonal manganite multiferroics are of particular interest as the properties of their symmetry-lowering phase transition can be described by a Mexican-hat-like potential energy surface. The early universe is proposed to have undergone a symmetry-lowering phase transition that is described by a similar Mexican-hat-like potential that gives rise to the formation of one-dimensional topologically protected defects known as cosmic strings. According to the Kibble-Zurek mechanism, hexagonal manganite multiferroics can host the crystallographic equivalent of cosmic strings and can therefore serve as a testing ground for exploration of concepts in cosmology. To date, however, direct imaging of 1D topological defects in a condensed matter material system has not been achieved. Here we report on robust three-dimensional imaging of topologically protected strings in a single hexagonal manganite nanocrystal, enabled by advances in experimental techniques. Our findings reveal multiferroic strings with a preferred phase vortex winding direction and average separation of ~93 nm.

Abstract Supported nanocatalysts are crucial for hydrogen production, yet their activity and stability are challenging to manage due to complex metal-support interfaces. Herein, we design Pt@ anatase&rutile-TiO2 with a strong-weak dual interface by modifying TiO2 using high-energy ball milling and in-situ reduction to vary surface energies. Experiments and density functional theory calculations reveal that the strong Pt-anatase TiO2 interface enhances hydrogen adsorption. In contrast, the weak Pt-rutile TiO2 interface facilitates hydrogen desorption, simultaneously preventing Pt agglomeration and increasing reaction rate. As a result, the tailored catalyst has a 529.3 mV overpotential at 1000 mA cm−2 in 0.5 M H2SO4, 0.69 times less than commercial Pt/C. It also possesses 8.8 times the mass activity of commercial Pt/C and maintains a low overpotential after 2000 cyclic voltammetry cycles, suggesting high activity and stability. This strong-weak dual interface engineering strategy shows potential for overall water splitting and proton exchange membrane water electrolyzer, advancing the design of efficient supported nanocatalysts.

Abstract Recent advances in additive manufacturing have enabled the creation of three-dimensional (3D) architectured pyrolytic carbon (PyC) structures with ultrahigh specific strength and energy absorption capabilities. However, their scalability is limited by reduced strength at larger sizes. Here we introduce a modular assembling approach to scale up PyC lattice structures while retaining strength. Three assembling mechanisms—adhesive, Lego-adhesive, and mechanical interlocking—are explored, demonstrating notably increased specific compressive strength and modulus as size increases, driven by energy release from assembly joint fractures. Practical application is demonstrated by using assembled PyC lattices as the core of an aerospace sandwich structure, significantly enhancing indentation resistance compared to conventional aramid paper honeycomb core. The method also enables versatile designs, including curved structures for space debris protection and bio-scaffold applications. This scalable approach offers a promising pathway for integrating PyC structures into large-scale engineering applications requiring superior mechanical properties and programmability in complicated shape design.

Abstract Photonic solutions are potentially highly competitive for energy-efficient neuromorphic computing. However, a combination of specialized nanostructures is needed to implement all neuro-biological functionality. Here, we show that donor-acceptor Stenhouse adduct dyes integrated with III-V semiconductor nano-optoelectronics have combined excellent functionality for bio-inspired neural networks. The dye acts as synaptic weights in the optical interconnects, while the nano-optoelectronics provide neuron reception, interpretation and emission of light signals. These dyes can reversibly switch from absorbing to non-absorbing states, using specific wavelength ranges. Together, they show robust and predictable switching, low energy thermal reset and a memory dynamic range from days to sub-seconds that allows both short- and long-term memory operation at natural timescales. Furthermore, as the dyes do not need electrical connections, on-chip integration is simple. We illustrate the functionality using individual nanowire photodiodes as well as arrays. Based on the experimental performance metrics, our on-chip solution is capable of operating an anatomically validated model of the insect brain navigation complex.

Abstract Selecting appropriate buffers is crucial for evaluating the chemical durability of glass under controlled conditions such as in the EPA 1313 test designed to measure elemental release as a function of pH. The efficacy of two alkali-metal free buffers, TRIS (NH2C(CH2OH)3) and ammonium chloride—ammonia (NH3/NH4Cl), was investigated during EPA 1313 testing of a simulated Hanford low-activity waste borosilicate glass in the alkaline regime (pH 8.5–10.5) at varying temperatures (RT, 40 °C, and 60 °C). While both buffers maintained the desired pH at room temperature, and up to 40 °C, the effectiveness of TRIS decreased at elevated temperatures, particularly at pH 10.5. Although 11B NMR showed evidence of TRIS-B complexation, its effect on the rate of elemental release was found to be negligible under the test conditions. With ammonium buffer, the release of alkali cations was slightly elevated when compared to the same conditions with TRIS at early time points.

Abstract Simplified borosilicate glass powders were irradiated by 952 MeV 136Xe ions and then altered in a solution at a high S/V ratio at pH 9 and 90 °C for 33 days. Compared to the alteration of a non-irradiated sample, the irradiated sample altered 3–5 times more. Overall, both the gels had a similar structure as indicated by 29Si, 27Al, 23Na, and 17O NMR experiments. Nevertheless, according to 11B and 1H NMR experiments, differences were observed in the quantity and speciation of B retained in the gels. The results suggest that the glass alteration mechanisms responsible for passivation are not changed because of the irradiation-induced structural damages. However, the alteration kinetics, gel morphology related to porosity, and the degree of maturation are different. It seems that the gel formed on irradiated glass matures faster and retains B, which in turn influences the glass dissolution rate.

Abstract A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output. The results show that the two algorithms based on multiple learners, gradient boosting decision tree (GBDT) and eXtreme Gradient Boosting (XGBoost), show better performance. The coefficient of determination R 2 of the models are 0.98 and the maximum error is 6.57 and 6.40%, respectively. The importance and interpretability of the input features were analyzed. The trend of the model analysis results was the same as that of the experimental conclusions, which further revealed the mechanism of the influence of element content and temperature changes on the oxidation resistance of Ti-V-Cr alloys and verified the effectiveness of the model. This study is of great significance for the discovery, prediction, and quantification of new high-temperature oxidation-resistant Ti-V-Cr alloys.

Abstract The accumulation of non-degradable plastics poses a significant environmental challenge. Photoreforming presents a promising solution through plastic upcycling. This review explores the mechanisms of photoreforming that determine product selectivity, emphasizing the role of polymer molecular structure and pre-treatment techniques. By identifying key factors affecting reaction dynamics, we aim to establish optimized frameworks for plastic photoreforming and enhance sustainable waste management, highlighting opportunities for improvement and innovation.

Abstract Electro-Spun nanofibers (ESNs), with their design flexibility, tailorable morphologies, and high surface area, are well-favored as triboelectric nanogenerator (TENG) materials for wearable electronics. Here, various aspects of ESNs-based wearable TENGs were examined. After introducing the most common TENG operating modes, an insightful overview of wearable TENG applications based on ESNs was presented. In this survey, a special attention is paid to wearable sensing, human-machine interaction, self-powered devices, and amplified energy harvesting. Efforts towards improving energy conversion efficiency, material durability, and compatibility with diverse wearable platforms were visited. Finally, a perspective based on particularly material aspect of ESNs is given, which could be insightful in tackling prevailing challenges and giving birth to new directions.

Abstract Flexible integrated circuits (FlexICs) have drawn increasing attention, particularly in remote sensors and wearables operating in a limited power budget. Here, we present an ultra-low power timer designed to wake-up an external circuit periodically, from a deep-sleep state into an active state, thereby largely reducing the system power consumption. We achieved this with a circuit topology that exploits the transistor’s leakage current to generate a low frequency wake-up signal. This topology is compatible with IC technologies where only n-type transistors are available. The design was implemented with the sustainable FlexIC process of PragmatIC, that is based on Indium Gallium Zinc Oxide (IGZO) thin-film transistors. Our timer generates mean wake-up frequency of 0.24 ± 0.15 Hz, with a mean power consumption of 26.7 ± 14.1 nW. In this paper, we provide details of the Wake-Up timer’s design and performance at different supply voltages, under temperature variations and different light conditions.

Abstract The observation of high-T c superconductivity (HTSC) in concomitant with pressure-induced orthorhombic-tetragonal structural transition in bilayer La3Ni2O7 has sparked hopes of achieving HTSC by stabilizing the tetragonal phase at ambient pressure. Chemical pressure, introduced by replacing La3+ with smaller rare-earth R 3+ has been considered as a potential route. However, our experimental and theoretical investigation reveals that such substitutions, despite causing lattice contraction, actually produce stronger orthorhombic distortions, requiring higher pressures for the structural transition. A linear extrapolation of P c versus the average size of A-site cations (), yields a putative critical value of c ≈ 1.23 Å for P c ≈ 1 bar. The negative correlation between P c and indicates that replacing La3+ with smaller R 3+ ions is unlikely to reduce P c to ambient pressure. Instead, substituting La3+ with larger cations like Sr2+ or Ba2+ might be a feasible approach. Our results provide guidance for realizing ambient-pressure HTSC in bilayer nickelates.

Abstract The detailed anisotropic dispersion of the low-temperature, low-energy magnetic excitations of the candidate spin-triplet superconductor UTe2 is revealed using inelastic neutron scattering. The magnetic excitations emerge from the Brillouin zone boundary at the high symmetry Y and T points and disperse along the crystallographic $$\hat{b}$$ b ̂ -axis. In applied magnetic fields to at least μ 0 H = 11 T along the $$\hat{c}-{\rm{axis}}$$ c ̂ − axis , the magnetism is found to be field-independent in the (h k0) plane. The scattering intensity is consistent with that expected from U3+/U4+ f-electron spins with preferential orientation along the crystallographic $$\hat{a}$$ a ̂ -axis, and a fluctuating magnetic moment of μ e f f =1.7(5) μ B . We propose interband spin excitons arising from f-electron hybridization as a possible origin of the magnetic excitations in UTe2.

Abstract A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~108 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤102 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

Abstract Design of micro/nanotextured consumer product surfaces presents the opportunity to enrich tactile experiences and enhance the capabilities of haptic devices, enabling rich human-object interactions through the passive or active control of finger friction. The absence of a comprehensive model that can holistically represent the underlying physics at finger-material interface, however, inhibits reliable prediction of finger friction. Here, we develop a model for micro/nanostructured touch interfaces, accounting for contact mechanics, capillaries, electrostatic fields, and their mutual interactions. We experimentally validate this model and apply it to predicting the friction and adhesion of microparticle-coated plastic films for food packaging, and designing surface structures for electroadhesive surfaces to achieve both stronger effects and lower variability — essential features for high-volume consumer electronics. Our model has wide applicability in predictive design of micro/nanostructured surfaces with diverse haptic functionalities.

Abstract The effect of TiC and VC nano-precipitate size on the hydrogen embrittlement of ferritic steels was studied in this work. Steels containing two size distributions (10 nm or less and 10 - 100 nm) of TiC and VC carbides are subjected to tensile tests in-situ in an electrochemical hydrogen charging environment. Hydrogen is found to be trapped in interstitial matrix sites on the precipitate/matrix interface with activation energies of 14 - 20 kJ/mol and inside misfit dislocation cores with energies of 27 - 37 kJ/mol. All steels are embrittled by 15 to 20%, except the TiC steel with semi-coherent carbides up to 100 nm, which is embrittled by 37%. This is caused by accelerated intergranular fracture as a result of hydrogen trapped in dislocation pile-ups around grain boundary precipitates. The steel with coherent VC nano-carbides retained the highest strength and ductility during in-situ testing. This is therefore the optimal carbide configuration for use in hydrogen environments.

Abstract This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects on damage evolution in TPBAR components, recommending suitable potentials and discussing advancements for materials in extreme radiation environments.

Abstract Understanding the bending behaviour of a crystal onto a flexible platform is crucial for flexible electronics. The Young’s modulus, a measure of how easily a material deforms, plays a critical role in the coupled deformation of a crystal on a flexible substrate, as well as the transfer of strain from the substrate onto the layer. Here, we report on the bending behaviour of gallium selenide (GaSe), a van der Waals semiconductor with a small Young’s modulus and strain-dependent electronic band structure. A controllable, reproducible uniaxial strain, ϵ, is applied to nanometer-thick GaSe layers via their bending on a mica substrate. The spectral shift ΔE of the room temperature photoluminescence emission corresponds to a strain coefficient ΔE/ϵ of up to ~100 eV, the largest value reported in the literature to date. This is accompanied by coupled electronic and vibrational states under strain-induced resonant excitation conditions, as probed by Raman spectroscopy.

Abstract Realizing the quantum anomalous Hall effect (QAHE) at high temperatures remains a significant challenge in condensed matter physics. MnBi2Te4, an intrinsic magnetic topological insulator, presents a promising platform for QAHE. However, its inherent interlayer antiferromagnetic coupling hinders practical realization at high temperatures. In this study, we propose a novel approach to achieve interlayer ferromagnetic (FM) coupling in MBT bilayer by intercalating the septuple-layer of topological insulators XBi2Te4 (X=Ge, Sn, Pb). Using first-principles calculations, we demonstrate that the p z orbital of the X atom mediates interactions between interlayer Mn atoms, enabling FM coupling. Monte Carlo simulations predict a magnetic transition temperature of 38 K for the MnBi2Te4/PbBi2Te4/MnBi2Te4 heterostructure. Our band structure and topological analyses confirm the preservation of QAHE in all MnBi2Te4/XBi2Te4/MnBi2Te4 heterostructures, while the MnBi2Te4/PbBi2Te4/MnBi2Te4 heterostructure exhibits a topological band gap of 72 meV, significantly exceeding that of the pure MnBi2Te4 bilayer. Furthermore, a continuum model is developed to elucidate the underlying mechanism of the nontrivial topological states. Our work provides a practical pathway to achieving interlayer FM coupling in MnBi2Te4 bilayers, paving the way for high-temperature QAHE and advancing the development of magnetic topological insulators for quantum and spintronic applications.

Abstract Most Rashba spin splitting experimentally studied so far has ideal lattice with inversion symmetry broken, which limits the possibility to minimize the presence of spin-degenerate carriers near the Fermi level. Here, we report a novel 2D Au2Sb surface alloy decorated with periodic structural defects that exhibits modulation on the Rashba spin-orbit coupling band. Spin- and angle-resolved photoemission spectroscopy reveals a Rashba spin-split band with antiparallel spin polarization, significantly broadened compared to the Au₂Sn surface alloy. From the good agreement between the experimental results and DFT calculations, we identify that the broadening of the Rashba bands comes from variations in Sb atom corrugation induced by the periodic three-pointed star-shaped defects. These periodic defects can shift the energy position of the Rashba bands without breaking the in-plane rotational and mirror symmetries. Our findings highlight the potential to tune spin-dependent properties in 2D materials for spintronic applications.

Abstract Metallic strontium titanate (SrTiO3) is known to have both normal-state and superconducting properties that strongly vary over a wide range of charge carrier densities, but the complex interplay between lattice and electronic degrees of freedom has hindered the development of a clear qualitative description of the observed behavior. A major challenge is to understand how the charge carriers themselves evolve with doping and temperature, with possible polaronic effects and evidence of an effective mass that strongly increases with temperature. Here we use 47,49Ti nuclear magnetic resonance (NMR) to perform a comprehensive study of the electronic spin susceptibility in the metallic state of strontium titanate across the doping-temperature phase diagram. We find a temperature-dependent Knight shift that can be quantitatively understood within a nondegenerate Fermi gas model that fully takes into account the complex band structure of SrTiO3. Our data are consistent with a temperature-independent effective mass, and we show that the behavior of the spin susceptibility is universal in a wide range of temperatures and carrier concentrations. These results provide a microscopic foundation for the understanding of the properties of the unconventional low-density metallic state in strontium titanate and related materials.

Abstract Thin films fabricated from solution-processed graphene nanosheets are of considerable technological interest for a wide variety of applications, such as transparent conductors, supercapacitors, and memristors. However, very thin printed films tend to have low conductivity compared to thicker ones. In this work, we demonstrate a simple layer-by-layer deposition method which yields thin films of highly-aligned, electrochemically-exfoliated graphene which have low roughness and nanometer-scale thickness control. By optimising the deposition parameters, we demonstrate films with high conductivity (1.3 × 105 S/m) at very low thickness (11 nm). Finally, we connect our high conductivities to low inter-nanosheet junction resistances (RJ), which we estimate at RJ ~ 1kΩ.

Abstract We present the first investigation of unusual nonlinear Hall effects in twisted multilayer 2D materials. Contrary to expectations, our study shows that these nonlinear effects are not merely extensions of their monolayer counterparts. Instead, we find that stacking order and pairwise interactions between neighboring layers, mediated by Berry curvatures, play a pivotal role in shaping their collective nonlinear optical response. By combining large-scale Real-Time Time-Dependent Density Functional Theory (RT-TDDFT) simulations with model Hamiltonian analyses, we demonstrate a remarkable second-harmonic transverse response in hexagonal boron nitride four-layers, even in cases where the total Berry curvature cancels out. Furthermore, our symmetry analysis of the layered structures provides a simplified framework for predicting nonlinear responses in multilayer materials in general. Our investigation challenges the prevailing understanding of nonlinear optical responses in layered materials and opens new avenues for the design and development of advanced materials with tailored optical properties.

Abstract In recent years, with the development of interfacial polymerization methodologies, the successful preparation of covalent organic framework (COF) monolayers featuring ultrahigh pore density and molecule-scale thickness has become a reality. This advancement has enabled ion transport with ultrahigh flux across extremely short paths. Owing to these processes, the COF monolayers have shown wide potential applications ranging from energy conversion and artificial mechanosensing to ion sieving. To date, nanofluidic systems based on COF monolayers are homogeneous, leading to nearly symmetric ion transport properties. Studies on the ionic transport behavior within ultrathin COF heterojunctions are limited and remain challenging. Herein, using a two-step sequential self-assembly and subsequent interfacial polymerization strategy, bi-layered heterogeneous tetraphenylporphyrin-based COF membranes composed of negatively charged and positively charged monolayers were prepared. Using an electric field as a driving force, rectified ion transport was observed for the first time. Notably, this ionic current rectification property could be modulated by the salt concentration and the structural composition of the membrane, and it was universal for various COF heterojunctions with different chemical ingredients. These findings are beneficial for promoting the development of innovative nanofluidic materials and devices.

Abstract We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.

Abstract A comprehensive understanding of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials. We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the overall characteristics of their piezoelectricity and the interplay between lattice, displacement, polarization, and elasticity. Among the screened 123 types of perovskites, the structural tetragonality is naturally divided into two categories: normal tetragonal (c/a ratio 1.17), exhibiting distinct chemical features, ferroelectric, elastic, and piezoelectric properties. Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region, which also produces an inherent contradiction between high piezoelectric coefficient d 33 and large piezoelectric anisotropy ratio |d 33/d 31|. Both the polarization axis and elastic softness direction are strongly correlated to piezoelectric anisotropy, which jointly determines the direction of maximum longitudinal piezoelectric response d 33. The validity and deficiencies of the widely utilized |d 33/d 31| ratio for representing piezoelectric anisotropy were reevaluated.