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Zhenping Wu

Publications and source records attributed to Zhenping Wu.

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Atomic-scale order enables high thermal boundary conductance at $\beta$-Ga$_2$O$_3$/4H-SiC interfaces

Thermal boundary conductance (TBC) at dissimilar interfaces imposes a fundamental limit on electronic device performance, yet predicting and understanding heat transport across realistic, disordered boundaries remains elusive. Here, we develop a computational framework that combines machine-learned interatomic potentials with lattice dynamics to address the long-standing problem of how interfacial structure, from disordered to atomically sharp, affects thermal transport in the technologically important $\beta$-Ga$_2$O$_3$/4H-SiC heterostructure. By explicitly accounting for phonon wave-particle duality, we show that interfacial disorder introduces additional interfacial phonon modes that facilitate vibrational impedance matching between the two highly dissimilar crystals, yet it simultaneously disrupts interfacial phonon coherence and limits the potential heat-transport benefit. Our atomistic simulations further indicate that restoring atomic-scale order preserves coherence and yields markedly higher conductance. These insights motivate the controlled epitaxial growth of $\beta$-Ga$_2$O$_3$/4H-SiC heterostructures with systematically tuned interfacial order. Experimental measurements validate our predictions, achieving a record-high TBC of 231 MW m$^{-2}$ K$^{-1}$ at atomically sharp junctions. Beyond the immediate implications for $\beta$-Ga$_2$O$_3$-based power electronics, our results establish the preservation of interfacial phonon coherence as an effective strategy for mitigating thermal bottlenecks in mismatched systems.

cond-mat.mtrl-sci

Small-Data Machine Learning Uncovers Decoupled Control Mechanisms of Crystallinity and Surface Morphology in $\beta$-Ga2O3 Epitaxy

The ultrawide-bandgap semiconductor $\beta$-Ga2O3 holds exceptional promise for next-generation power electronics and deep-ultraviolet optoelectronics, yet its widespread application is hindered by the lack of cost-effective, high-quality heteroepitaxial thin films. Here, we demonstrate an interpretable machine learning framework that efficiently navigates the complex, multiparameter process space of pulsed laser deposition (PLD) to achieve high-crystallinity $\beta$-Ga2O3 epitaxy on c-plane sapphire. By systematically benchmarking nine regression algorithms under limited experimental data conditions, we identify quadratic polynomial ridge regression as the optimal surrogate model, which combines predictive accuracy (R$^2$ $\approx$ 0.86) with full physical transparency through explicit analytical coefficients. Coupling this model with SHAP (SHapley Additive exPlanations) analysis and iterative experimental design, we construct a closed-loop optimization workflow that progressively refines the process-performance landscape over only three experimental rounds. This data-efficient strategy reduces the X-ray rocking curve (RC) full-width at half-maximum (FWHM) by 70$\%$ from > 3$^{\circ}$ to 0.92$^{\circ}$, which is the best reported value for PLD-grown $\beta$-Ga2O3 on sapphire. Intriguingly, concurrent modeling of surface roughness reveals that crystalline quality and surface morphology are governed by distinct dominant factors: temperature primarily controls bulk crystallinity, whereas oxygen pressure dictates surface kinetics. This decoupled mechanism, quantitatively captured for the first time via feature importance analysis, provides actionable physical insight for independent optimization of structural and morphological properties. Our work establishes a generalizable, resource-efficient paradigm for intelligent process development in oxide epitaxy and beyond.

cond-mat.mtrl-sci

Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis.

physics.app-ph

Applications of Raman Spectroscopy in Clinical Medicine

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research.

physics.app-ph

Metal-insulator phase separation in KTaO3-based two-dimensional electron gas

Electronic phase separation (EPS) originates from an incomplete transformation between electronic phases, causing the inhomogeneous spatial distribution of electronic properties. In the system of two-dimensional electron gas (2DEG), the EPS is usually identified based on a percolative metal-to-superconductor transition. Here, we report a metal-insulator transition (MIT) in KTaO3-based 2DEG with the width of conductive channel decreasing into micrometer scale. Hysteretic resistance-temperature relations are observed due to the competition between metallic and insulating phases, which is tunable by magnetic field. Such a size-dependent MIT effect is attributed to the coexistence and separation of metallic and insulating phases. Combining density functional theory calculation, we propose a theoretical model to simulate the dynamic process of the EPS using the percolation theory, demonstrating the mechanism of size-dependent MIT. Our work suggests a clear and simple 2DEG platform to achieve the spatial coexistence of metallic and insulating phases.

cond-mat.mtrl-sci

Observation of room-temperature ferroelectricity in elemental Te nanowires

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices.

cond-mat.mtrl-sci

Asymmetric Ground States in La$_{0.67}$Sr$_{0.33}$MnO$_3$/BaTiO$_3$ heterostructures Induced by Flexoelectric Bending

Misfit strain delivered from single-crystal substrates typically modifies the ground states of transition metal oxides, generating increasing interests in designing modern transducers and sensors. Here, we demonstrate that magnetotransport properties of La$_{0.67}$Sr$_{0.33}$MnO$_3$ (LSMO) films were continuously tuned by uniaxial strain produced by a home-designed bending jig. The electrical conductivity and Curie temperature of LSMO films are enhanced by bending stresses. The resistivity of a u-shape bended LSMO decays three times faster than that of a n-shape bended LSMO as a response to the same magnitude of strain. The asymmetric magnetic states in uniaxially strained LSMO are attributed to the dual actions of Jahn-Teller distortion and strain gradient mediated flexoelectric fields in an adjacent ferroelectric layer. These findings of multi-field regulation in a single material provide a feasible means for developing flexible electronic and spintronic devices.

cond-mat.mtrl-sci

Strong Ferromagnetism Achieved via Breathing Lattices in Atomically Thin Cobaltites

Low-dimensional quantum materials that remain strongly ferromagnetic down to mono layer thickness are highly desired for spintronic applications. Although oxide materials are important candidates for next generation of spintronic, ferromagnetism decays severely when the thickness is scaled to the nano meter regime, leading to deterioration of device performance. Here we report a methodology for maintaining strong ferromagnetism in insulating LaCoO3 (LCO) layers down to the thickness of a single unit cell. We find that the magnetic and electronic states of LCO are linked intimately to the structural parameters of adjacent "breathing lattice" SrCuO2 (SCO). As the dimensionality of SCO is reduced, the lattice constant elongates over 10% along the growth direction, leading to a significant distortion of the CoO6 octahedra, and promoting a higher spin state and long-range spin ordering. For atomically thin LCO layers, we observe surprisingly large magnetic moment (0.5 uB/Co) and Curie temperature (75 K), values larger than previously reported for any mono layer oxide. Our results demonstrate a strategy for creating ultra thin ferromagnetic oxides by exploiting atomic hetero interface engineering,confinement-driven structural transformation, and spin-lattice entanglement in strongly correlated materials.

cond-mat.mtrl-sci

Modulation of superconductivity by quantum confinement in doped strontium titanate

Quantum confinement in a thin-film geometry offers viable routes for tuning the critical properties of superconductors through modification of both density of states and pairing interaction. Low-density systems like doped strontium titanate are especially susceptible to these confinement-induced effects. In this paper, we show that the superconducting critical temperature $T_c$ is enhanced through quantum confinement in SrTiO$_3$/SrTi$_{1-x}$Nb$_x$O$_3$/SrTiO$_3$ heterostructures at $x=1\%$ concentration, by measuring resistivity transitions and the Hall carrier density for different thicknesses of the doped layer. We observe a nonmonotonic raise of $T_c$ with decreasing layer thickness at constant carrier density as estimated from the Hall effect. We analyze the results by solving a two-band model with a pairing interaction reproducing the density-dependent $T_c$ of doped SrTiO$_3$ in the bulk, that we confine to a potential well established self-consistently by the charged Nb dopants. The evolution of the theoretical $T_c$ with thickness agrees well with experiments. We point out the possible role of density inhomogeneities and suggest novel methods for engineering superconductivity in epitaxial thin films.

cond-mat.supr-con

Probing Quantum Confinement and Electronic Structure at Polar Oxide Interfaces

Polar discontinuities occurring at interfaces between two different materials constitute both a challenge and an opportunity in the study and application of a variety of devices. In order to cure the large electric field occurring in such structures, a reconfiguration of the charge landscape sets in at the interface via chemical modifications, adsorbates or charge transfer. In the latter case, one may expect a local electronic doping of one material: one sparkling example is the two-dimensional electron liquid (2DEL) appearing in SrTiO$_3$ once covered by a polar LaAlO$_3$ layer. Here we show that tuning the formal polarisation of a (La,Al)$_{1-x}$(Sr,Ti)$_x$O$_3$ (LASTO:$x$) overlayer through chemical composition modifies the quantum confinement of the 2DEL in SrTiO$_3$ and its electronic band structure. The analysis of the behaviour in magnetic field of superconducting field-effect devices reveals, in agreement with $ab\ initio$ calculations and self-consistent Poisson-Schrödinger modelling, that quantum confinement and energy splitting between electronic bands of different symmetries strongly depend on interface charge densities. These results not only strongly support the polar discontinuity mechanisms with a full charge transfer to explain the origin of the 2DEL at the celebrated LaAlO$_3$/SrTiO$_3$ interface, but also demonstrate an effective tool for tailoring the electronic structure at oxide interfaces.

cond-mat.mtrl-sci