Mingda Li

Efficient thermal management is essential for the reliability of modern power electronics, where increasing device density leads to severe heat dissipation challenges. However, in thin-film systems, thermal transport is often compromised by interfacial resistance and microscale defects introduced during synthesis or transfer, which are difficult to characterize using conventional techniques. Here we present a non-contact, spatiotemporal-resolved ultrafast x-ray diffraction method to extract in-plane thermal conductivity and thermal boundary conductance, using GaN thin films on silicon as a model system. By tracking the pump-induced lattice strain, we reconstruct the lateral heat flow dynamics and quantitatively probe thermal transport near a wrinkle defect. We uncover pronounced asymmetric heat dissipation across the wrinkle, with a four-fold reduction in the local thermal conductivity near the wrinkle and a 25% drop in interfacial conductance. Our work demonstrates that ultrafast x-ray diffraction can serve as a precise thermal metrology tool for characterizing heat transport in multilayered thin-film structures for next-generation microelectronic devices.

Writing magnetic bits through spin-orbit torque (SOT) switching is promising for fast and efficient magnetic random-access memory devices. While SOT switching of out-of-plane (OOP) magnetized states requires lateral symmetry breaking, in-plane (IP) magnetized states suffer from low storage density. Here, we demonstrate a field-free switching scheme using a 5-nanometer europium iron garnet film grown with a (110) orientation that shows a spin reorientation transition from OOP to IP above room temperature. This scheme combines the benefits of high-density storage in the OOP states at room temperature and the efficient field-free SOT switching in the IP states at elevated temperatures. While conventional switching of OOP bits faces the dilemma that high OOP anisotropy is required to improve bit stability and low OOP anisotropy is required to lower switching current density, this scheme disentangles this interdependence, allowing for low switching currents to be possible without sacrificing the bit stability, offering opportunities for future memory devices.

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade‐offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph‐based models, and transformer architectures, integrated with high‐throughput simulations and growing databases, effectively capture structure‐property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI‐enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

Polarons, quasiparticles from electron-phonon coupling, are crucial for material properties including high-temperature superconductivity and colossal magnetoresistance. However, scarce studies have investigated polaron formation in low-dimensional materials with phonon polarity and electronic structure transitions. In this work, we studied polarons of tellurene, composed of chiral Te chains. The frequency and linewidth of the A ₁ phonon, which becomes increasingly polar for thinner tellurene, change abruptly for thickness below 10 nanometers, where field-effect mobility drops rapidly. These phonon and transport signatures, combined with phonon polarity and band structure, suggest a crossover from large polarons in bulk tellurium to small polarons in few-layer tellurene. Effective field theory considering phonon renormalization in the small-polaron regime semiquantitatively reproduces the phonon hardening and broadening effects. This polaron crossover stems from the quasi–one-dimensional nature of tellurene, where modulation of interchain distance reduces dielectric screening and promotes electron-phonon coupling. Our work provides valuable insights into the influence of polarons on phononic, electronic, and structural properties in low-dimensional materials.

High-quality training data, efficient structure representation, and flexible feature encoding are the three key components for successful AI-powered data-driven vibrations and spectra.

Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates.

The application of optimal electron count has proven effective in predicting the existence of complex structural topologies containing electron-rich polyanionic networks, including linear and zig-zag chains, square planar structures, and simple cubic lattices of main group elements. In this study, we report the successful synthesis of a new family of magnetic compounds, RSe2−x (x ∼ 1/6) (R = Gd and Tb), using KCl flux. The resulting crystal structure of RSe2−x is tetragonal, exhibiting PbFCl-type symmetry with space group P4/nmm. Chemical composition analysis consistently reveals x ∼ 1/6, indicating chalcogen-deficient square sheets. This observation suggests that RSe2−x adheres to the 14-electron rule, as the total valence electrons per RSe2−x (x ∼ 1/6) is 14 e−. In GdSe2−x (x ∼ 1/6), the magnetic susceptibility measurement displays a lambda-shaped peak around 8 K, indicating an antiferromagnetic-type transition. However, the positive Curie–Weiss temperature (θCW) suggests the presence of an additional ferromagnetic interaction, which may result from the large magnetic moment of the Gd atoms. In contrast, TbSe2−x (x ∼ 1/6) exhibits antiferromagnetic ordering. Chemical bonding analysis also indicates that the strong Se–Se antibonding in the square net may be related to Se deficiency. Our work shed light on ellucidating the interplay between chemical rule, defects, and magnetism.

Second-harmonic generation (SHG) is the generation of 2ω (or half wavelength) light from incident light with frequency ω as a nonlinear optical response of the material. Three-dimensional (3D) SHG materials are widely investigated for developing laser technology to obtain shorter wavelengths in photolithography fabrication of semiconductor devices and the medical sciences, such as for imaging techniques that do not use fluorescent materials. However, to obtain the optimized SHG intensity, the 3D material is required to have no spatial-inversion symmetry (or non-centrosymmetry) and special crystal structure (or so-called phase-matched condition). Recently, engineering symmetry breaking of thin two-dimensional (2D) materials whose 3D structure has the inversion symmetry can offer a breakthrough to enhance the SHG intensity without requiring the phase-matched condition. Over the past decade, many 2D SHG materials have been synthesized to have broken inversion symmetry by stacking heterostructures, twisted moiré structures, dislocated nanoplates, spiral nanosheets, antiferromagnetic order, and strain. In this review, we focus on the recent progress in breaking inversion and rotational symmetries in out-of-plane and/or in-plane directions. The theoretical calculations and experimental setup are briefly introduced for the non-linear optical response of the 2D materials. We also present our perspectives on how these can optimize the SHG of the 2D materials.

The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to two-dimensional materials, where the Fermi level can be controlled through various techniques, the situation for bulk crystals beyond laborious chemical doping poses significant challenges. Here, we report the milli-electron-volt (meV) level ultra-fine-tuning of the Fermi level of bulk topological Weyl semimetal tantalum phosphide using accelerator-based high-energy hydrogen implantation and theory-driven planning. By calculating the desired carrier density and controlling the accelerator profiles, the Fermi level can be experimentally fine-tuned from 5 meV below, to 3.8 meV below, to 3.2 meV above the Weyl nodes. High-resolution transmission electron microscopy reveals the crystalline structure is largely maintained under irradiation, while electrical transport indicates that Weyl nodes are preserved and carrier mobility is also largely retained. Our work demonstrates the viability of this generic approach to tune the Fermi level in semimetal systems and could serve to achieve property fine-tuning for other bulk quantum materials with ultrahigh precision.

Engineering new quantum phases requires fine tuning of the electronic, orbital, spin, and lattice degrees of freedom. To this end, kagomé lattice with flat bands has garnered great attention by hosting various topological and correlated phases. However, the impact of flat bands in materials’ macroscopic properties has been elusive. Here we discover the unconventional nematicity in kagomé metal CoSn. Thermodynamic, dilatometry, resonant X-ray scattering, inelastic neutron scattering, Lamour diffraction, and thermoelectric measurements consistently hint rotational symmetry-breaking and nematic order that is pronounced only near T* = 225 K. The observations, principally the nematic's finite temperature stability--incipience-- can be explained by a phenomenological model which reveals that thermally excited flat bands become unstable and promote symmetry breaking at a characteristic temperature determined by their energy distance from the Fermi level, qualitatively and quantitatively agreeing with the experiments. Our work shows that that thermal fluctuations, which are typically detrimental for correlated electronic phases, can induce new ordered states of matter.

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Two-dimensional van der Waals (vdW) magnetic materials hold promise for the development of high-density, energy-efficient spintronic devices for memory and computation. Recent breakthroughs in material discoveries and spin-orbit torque control of vdW ferromagnets have opened a path for integration of vdW magnets in commercial spintronic devices. However, a solution for field-free electric control of perpendicular magnetic anisotropy (PMA) vdW magnets at room temperatures, essential for building compact and thermally stable spintronic devices, is still missing. Here, we report a solution for the field-free, deterministic, and nonvolatile switching of a PMA vdW ferromagnet, Fe ₃ GaTe ₂ , above room temperature (up to 320 K). We use the unconventional out-of-plane anti-damping torque from an adjacent WTe ₂ layer to enable such switching with a low current density of 2.23 × 10 ⁶ A cm ⁻² . This study exemplifies the efficacy of low-symmetry vdW materials for spin-orbit torque control of vdW ferromagnets and provides an all-vdW solution for the next generation of scalable and energy-efficient spintronic devices.

Recent discovery of emergent magnetism in van der Waals magnetic materials (vdWMM) has broadened the material space for developing spintronic devices for energy-efficient computation. While there has been appreciable progress in vdWMM discovery, a solution for non-volatile, deterministic switching of vdWMMs at room temperature has been missing, limiting the prospects of their adoption into commercial spintronic devices. Here, we report the first demonstration of current-controlled non-volatile, deterministic magnetization switching in a vdW magnetic material at room temperature. We have achieved spin-orbit torque (SOT) switching of the PMA vdW ferromagnet Fe₃GaTe₂ using a Pt spin-Hall layer up to 320 K, with a threshold switching current density as low as $${J}_{{{{{{\rm{sw}}}}}}}=$$ J sw = 1.69 $$\times$$ × 10⁶ A cm⁻² at room temperature. We have also quantitatively estimated the anti-damping-like SOT efficiency of our Fe₃GaTe₂/Pt bilayer system to be $${\xi }_{{{{{{\rm{DL}}}}}}}=0.093$$ ξ DL = 0.093 , using the second harmonic Hall voltage measurement technique. These results mark a crucial step in making vdW magnetic materials a viable choice for the development of scalable, energy-efficient spintronic devices.

Characterizing and controlling entanglement in quantum materials is crucial for the development of next-generation quantum technologies. However, defining a quantifiable figure of merit for entanglement in macroscopic solids is theoretically and experimentally challenging. At equilibrium the presence of entanglement can be diagnosed by extracting entanglement witnesses from spectroscopic observables and a nonequilibrium extension of this method could lead to the discovery of novel dynamical phenomena. Here, we propose a systematic approach to quantify the time-dependent quantum Fisher information and entanglement depth of transient states of quantum materials with time-resolved resonant inelastic x-ray scattering. Using a quarter-filled extended Hubbard model as an example, we benchmark the efficiency of this approach and predict a light-enhanced many-body entanglement due to the proximity to a phase boundary. Our work sets the stage for experimentally witnessing and controlling entanglement in light-driven quantum materials via ultrafast spectroscopic measurements.

Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.

Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials’ topology often poses significant technical challenges. X‐ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms’ local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X‐ray absorption near‐edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F₁ scores of 89% and 93% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine‐learning‐augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non‐cleavable compounds and amorphous materials, and may further inform field‐driven phenomena in situ, such as magnetic field‐driven topological phase transitions.

M-STAR is a next generation polarized neutron reflectometer with advanced capabilities. A new focusing guide concept is optimized for samples with dimensions down to a millimeter range. A proposed hybrid pulse-skipping chopper will enable experiments at constant geometry at one incident angle in a broad range of wavevector transfer Q up to 0.3 A−1 for specular, off-specular, and GISANS measurements. M-STAR will empower nanoscience and spintronics studies routinely on small samples (∼2 × 2 mm2) and of atomic-scale thickness using versatile experimental conditions of magnetic and/or electric fields, light, and temperature applied in situ to novel complex device-like nanosystems with multiple buried interfaces. M-STAR will enable improved grazing incidence diffraction measurements, as a surface-sensitive depth-resolved probe of, e.g., the out-of-plane component of atomic magnetic moments in ferromagnetic, antiferromagnetic, and more complex structures as well as in-plane atomic-scale structures inaccessible with contemporary diffractometry and reflectometry. New horizons will be opened by the development of an option to probe near-surface dynamics with inelastic grazing incidence scattering in the time-of-flight mode. These novel options in combination with ideally matched parameters of the second target station will place M-STAR in the world’s leading position for high resolution polarized reflectometry.

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

Following the discovery of a new family of kagomé prototypical materials with structure AV3Sb5 (A=K, Rb, and Cs), there has been a heightened interest in studying the correlation-driven electronic phenomena in these kagomé lattice systems. The study of these materials has gone beyond magneto-transport measurements to reveal exciting features such as Dirac bands, anomalous Hall effect, bulk superconductivity with Tc∼0.9−2.5K, and the observation of charge density wave instabilities, suggesting an intertwining of topological physics and new quantum orders. Moreover, very recent works on numerous types of experiments have appeared further examining the unconventional superconductivity and the exotic electronic states found within these kagomé materials. Theories on the strong interactions that play a role in these systems have been proposed to shed light on the nature of these topological charge density waves. In this brief review, we summarize these recent experimental findings and theoretical proposals and envision the materials as new platforms to study the interplay between topological physics and strongly correlated electronic systems.

Topological nodal semimetals are known to host a variety of fascinating electronic properties due to the topological protection of the band-touching nodes. Neutron scattering, despite its power in probing elementary excitations, has not been routinely applied to topological semimetals, mainly due to the lack of an explicit connection between the neutron response and the signature of topology. In this work, we theoretically investigate the role that neutron scattering can play to unveil the topological nodal features: a large magnetic neutron response with spectral non-analyticity can be generated solely from the nodal bands. A new formula for the dynamical structure factor for generic topological nodal metals is derived. For Weyl semimetals, we show that the locations of Weyl nodes, the Fermi velocities and the signature of chiral anomaly can all leave hallmark neutron spectral responses. Our work offers a neutron-based avenue toward probing bulk topological materials.

Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

Thermoelectrics are promising by directly generating electricity from waste heat. However, (sub-)room-temperature thermoelectrics have been a long-standing challenge due to vanishing electronic entropy at low temperatures. Topological materials offer a new avenue for energy harvesting applications. Recent theories predicted that topological semimetals at the quantum limit can lead to a large, non-saturating thermopower and a quantized thermoelectric Hall conductivity approaching a universal value. Here, we experimentally demonstrate the non-saturating thermopower and quantized thermoelectric Hall effect in the topological Weyl semimetal (WSM) tantalum phosphide (TaP). An ultrahigh longitudinal thermopower $$S_{xx} \sim 1.1 \times 10^3 \, \mu \, {\mathrm{V}} \, {\mathrm{K}}^{ - 1}$$ S x x ~ 1.1 × 1 0 3 μ V K − 1 and giant power factor $$\sim 525 \, \mu \, {\mathrm{W}} \, {\mathrm{cm}}^{ - 1} \, {\mathrm{K}}^{ - 2}$$ ~ 525 μ W cm − 1 K − 2 are observed at ~40 K, which is largely attributed to the quantized thermoelectric Hall effect. Our work highlights the unique quantized thermoelectric Hall effect realized in a WSM toward low-temperature energy harvesting applications.

Due to their unique properties, polymers – typically thermal insulators – can open up opportunities for advanced thermal management when they are transformed into thermal conductors. Recent studies have shown polymers can achieve high thermal conductivity, but the transport mechanisms have yet to be elucidated. Here we report polyethylene films with a high thermal conductivity of 62 Wm⁻¹ K⁻¹, over two orders-of-magnitude greater than that of typical polymers (~0.1 Wm⁻¹ K⁻¹) and exceeding that of many metals and ceramics. Structural studies and thermal modeling reveal that the film consists of nanofibers with crystalline and amorphous regions, and the amorphous region has a remarkably high thermal conductivity, over ~16 Wm⁻¹ K⁻¹. This work lays the foundation for rational design and synthesis of thermally conductive polymers for thermal management, particularly when flexible, lightweight, chemically inert, and electrically insulating thermal conductors are required.

The charge and spin of the electrons in solids have been extensively exploited in electronic devices and in the development of spintronics. Another attribute of electrons—their orbital nature—is attracting growing interest for understanding exotic phenomena and in creating the next-generation of quantum devices such as orbital qubits. Here, we report on orbital-flop induced magnetoresistance anisotropy in CeSb. In the low temperature high magnetic-field driven ferromagnetic state, a series of additional minima appear in the angle-dependent magnetoresistance. These minima arise from the anisotropic magnetization originating from orbital-flops and from the enhanced electron scattering from magnetic multidomains formed around the first-order orbital-flop transition. The measured magnetization anisotropy can be accounted for with a phenomenological model involving orbital-flops and a spin-valve-like structure is used to demonstrate the viable utilization of orbital-flop phenomenon. Our results showcase a contribution of orbital behavior in the emergence of intriguing phenomena.

Anderson localization in phonon heat conduction is observed in GaAs/AlAs superlattices with ErAs nanodots.

Manganese oxide (MnO₂) has long been investigated as a pseudo-capacitive material for fabricating fiber-shaped supercapacitors but its poor electrical conductivity and its brittleness are clear drawbacks. Here we electrochemically insert nanostructured MnO₂domains into continuously interconnected carbon nanotube (CNT) networks, thus imparting both electrical conductivity and mechanical durability to MnO₂. In particular, we synthesize a fiber-shaped coaxial electrode with a nickel fiber as the current collector (Ni/CNT/MnO₂); the thickness of the CNT/MnO₂hybrid nanostructured shell is approximately 150 μm and the electrode displays specific capacitances of 231 mF cm⁻¹. When assembling symmetric devices featuring Ni/CNT/MnO₂coaxial electrodes as cathode and anode together with a 1.0 M Na₂SO₄aqueous solution as electrolyte, we find energy densities of 10.97 μWh cm⁻¹. These values indicate that our hybrid systems have clear potential as wearable energy storage and harvesting devices.

We demonstrated electron transfer across the FeSe/STO interface and showed its dominant role in T _c enhancement in this system.

Using ab initio simulations, we uncover the electron mean-free-path (MFP) spectrum in Dirac material and specifically show how the thermoelectric efficiency can greatly benefit from a distinct, monotonically decreasing trend of electron MFPs arising from the linear energy-momentum dispersion implied by the Dirac band topology. In the past, it was generally assumed that for the nanostructuring approach to be effective, one should design nanostructures to have characteristic length larger than the electron MFP but smaller than the phonon MFP to reduce thermal conductivity. Our results show that enhancement in thermoelectric performance can be achieved in Dirac materials even when they are smaller than the electron MFP by selectively filtering out long-MFP electrons that are harmful to the Seebeck coefficient.

On 11 March 2011, the day of the unforgettable disaster of the 9 magnitude Tohoku earthquake and quickly followed by the devastating Tsunami, a damageable amount of radionuclides had dispersed from the Fukushima Daiichi’s damaged nuclear reactors. Decontamination of the dispersed radionuclides from seawater and soil, due to the huge amounts of coexisting ions with competitive functionalities, has been the topmost difficulty. Ferric hexacyanoferrate, also known as Prussian blue (PB), has been the most powerful material for selectively trapping the radioactive cesium ions; its high tendency to form stable colloids in water, however, has made PB to be impossible for the open-field radioactive cesium decontamination applications. A nano/nano combinatorial approach, as is described in this study, has provided an ultimate solution to this intrinsic colloid formation difficulty of PB. Cellulose nanofibers (CNF) were used to immobilize PB via the creation of CNF-backboned PB. The CNF-backboned PB (CNF/PB) was found to be highly tolerant to water and moreover, it gave a 139 mg/g capability and a million (10⁶) order of magnitude distribution coefficient (K_d) for absorbing of the radioactive cesium ion. Field studies on soil and seawater decontaminations in Fukushima gave satisfactory results, demonstrating high capabilities of CNF/PB for practical applications.

We present a theoretical investigation of the surface plasmon (SP) at the interface between a topologically nontrivial cylindrical core and a topologically trivial surrounding material, from the axion electrodynamics and modified constitutive relations. We find that the topological effect always leads to a red-shift of SP energy, while the energy red-shift decreases monotonically as core diameter decreases. A qualitative picture based on classical perturbation theory is given to explain these phenomena, from which we also infer that to enhance the shift, the difference between the inverse of dielectric constants of two materials must be increased. We also find that the surrounding magnetic environment suppresses the topological effect. All these features can be well described by a simple ansatz surface wave, which is in good agreement with full electromagnetic eigenmodes. In addition, bulk plasmon energy at ω_p = 17.5 ± 0.2 eV for a semiconducting Bi₂Se₃ nanoparticle is observed from high-resolution electron energy loss spectrum measurements.

We provide an analytical solution for studying the near-field optical effect of a core-shell nanostructure in proximity to a flat surface, within quasi-static approximation. The distribution of electrostatic potential and the field enhancement in this complex geometry are obtained by solving a set of linear equations. This analytical result can be applied to a wide range of systems associated with near-field optics and surface plasmon polaritons. To illustrate the power of this technique, we study the field-attenuation effect of an oxidized shell in a silver tip in a near-field scanning microscope. The thickness of oxidized layer can be monitored by measuring the intensity of light. We also find a linear relation between resonant frequency and temperature in an Ag–Au core-shell structure, which provides insight for local temperature detection with nm scale resolution. Our results also show good agreement with recent finite element method results.

We use magneto-resistance measurements to investigate the effect of texturing in polycrystalline bismuth thin films. Electrical current in bismuth films with texturing such that all grains are oriented with the trigonal axis normal to the film plane is found to flow in an isotropic manner. By contrast, bismuth films with no texture such that not all grains have the same crystallographic orientation exhibit anisotropic current flow, giving rise to preferential current flow pathways in each grain depending on its orientation. Extraction of the mobility and the phase coherence length in both types of films indicates that carrier scattering is not responsible for the observed anisotropic conduction. Evidence from control experiments on antimony thin films suggests that the anisotropy is a result of bismuth's large electron effective mass anisotropy.

Here we report a liquid-based, low-cost and reliable synthesis method of a lithium–sulfur composite cathode with improved cyclability.