arXiv subjects

Guanping Xu

Publications and source records attributed to Guanping Xu.

8 recordsLinked to original sources

arXiv2026

Synergistic Interface Stability and High Room-Temperature Ionic Conductivity for Wide-Temperature All-Solid-State Batteries Based on Li6+xSixSb1-xS5I Electrolytes

Solid-state lithium-ion batteries (LIBs) are increasingly recognized for their exceptional energy density and safety. However, their widespread adoption is challenged by persistent issues such as thermal and electrochemical instability, dendrite formation, and limited compatibility with high-voltage cathodes. Sulfide-based solid electrolytes (SEs), particularly iodide argyrodites, offer outstanding ionic conductivity and stability; however, their practical application is constrained by the formation of space-charge layers, slow ion transport, and susceptibility to dendrite penetration. To address these challenges, we synthesized a novel Li6.6Si0.6Sb0.4S5I argyrodite electrolyte via ball milling and heat treatment, achieving a remarkable room-temperature ionic conductivity of 9.9 mS cm^-1. The electrolyte was integrated with a LiNbO3-coated LiNi0.7Co0.1Mn0.2O2 cathode to form an all-solid-state battery, which demonstrated an initial discharge capacity of 171.2 mAh g^-1, retained 84.2% of its capacity after 200 cycles at 0.5C, and maintained stable cycling across a broad temperature range from -20 degrees C to 60 degrees C. Our study shows that tailored electrolyte composition and a composite cathode configuration significantly enhance cycling stability and improve interfacial protection. These findings highlight the potential of Si-doped antimony-type iodide argyrodites for next-generation high-performance all-solid-state batteries, offering durable operation under diverse thermal conditions.

cond-mat.mtrl-sci↗

arXiv2025

Synergistic effects of rare-earth doping on the magnetic properties of orthochromates: A machine learning approach

Multiferroic materials, particularly rare-earth orthochromates (RECrO$_3$), have garnered significant interest due to their unique magnetic and electric-polar properties, making them promising candidates for multifunctional devices. Although extensive research has been conducted on their antiferromagnetic (AFM) transition temperature (N$\acute{\textrm{e}}$el temperature, $T_\textrm{N}$), ferroelectricity, and piezoelectricity, the effects of doping and substitution of rare-earth (RE) elements on these properties remain insufficiently explored. In this study, convolutional neural networks (CNNs) were employed to predict and analyze the physical properties of RECrO$_3$ compounds under various doping scenarios. Experimental and literature data were integrated to train machine learning models, enabling accurate predictions of $T_\textrm{N}$, besides remanent polarization ($P_\textrm{r}$) and piezoelectric coefficients ($d_{33}$). The results indicate that doping with specific RE elements significantly impacts $T_\textrm{N}$, with optimal doping levels identified for enhanced performance. Furthermore, high-entropy RECrO$_3$ compounds were systematically analyzed, demonstrating how the inclusion of multiple RE elements influences magnetic properties. This work establishes a robust framework for predicting and optimizing the properties of RECrO$_3$ materials, offering valuable insights into their potential applications in energy storage and sensor technologies.

cond-mat.mtrl-sci↗

arXiv2025

Integrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves

The integration of machine learning techniques with triboelectric nanogenerators (TENGs) offers a transformative pathway for optimizing energy harvesting technologies. In this study, we propose a comprehensive framework that utilizes graph neural networks to predict and enhance the performance of TENG electrode materials and doping strategies. By leveraging an extensive dataset of experimental and computational results, the model effectively classifies electrode materials, predicts optimal doping ratios, and establishes robust structure-property relationships. Key findings include a 65.7% increase in energy density for aluminum-doped PTFE and an 85.7% improvement for fluorine-doped PTFE, highlighting the critical influence of doping materials and their concentrations. The model further identifies PTFE as a highly effective negative electrode material, achieving a maximum energy density of 1.12 J/cm$^2$ with 7% silver (Ag) doping when copper (Cu) is used as the positive electrode. This data-driven approach not only accelerates material discovery but also significantly reduces experimental costs, providing novel insights into the fundamental factors influencing TENG performance. The proposed methodology establishes a robust platform for intelligent material design, advancing the development of sustainable energy technologies and self-powered systems.

cond-mat.mtrl-sci↗

arXiv2025

Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study

In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. However, the issue of dendrite growth has emerged as a significant obstacle in battery development. Excessive dendrite growth during charging and discharging processes can lead to battery short-circuiting, degradation of electrochemical performance, reduced cycle life, and abnormal exothermic events. Consequently, understanding the dendrite growth process has become a key challenge for researchers. In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods. We developed two distinct computer models to predict dendrite growth in batteries. The CNN-1 model employs standard convolutional neural network techniques for dendritic growth prediction, while CNN-2 integrates additional physical parameters to enhance model robustness. Our results demonstrate that CNN-2 significantly enhances prediction accuracy, offering deeper insights into the impact of physical factors on dendritic growth. This improved model effectively captures the dynamic nature of dendrite formation, exhibiting high accuracy and sensitivity. These findings contribute to the advancement of safer and more reliable energy storage systems.

physics.comp-ph↗

arXiv2024

Ultrasonic-assisted liquid phase exfoliation for high-yield monolayer graphene with enhanced crystallinity

Graphene stands as a promising material with vast potential across energy storage, electronics, etc. Here, we present a novel mechanical approach utilizing ultrasonic high-energy intercalation exfoliation to extract monolayer graphene from graphite, offering a simple yet efficient alternative to conventional methods. Through a comprehensive series of characterizations involving atomic force microscopy, scanning electron microscopy, Raman spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, the resulting graphene nanosheets demonstrate superior crystallinity compared to those obtained via the conventional method. The high-crystalline freestanding graphene nanosheets derived from this method not only facilitate easier separation but also significantly enhance the physical performance of the original materials. This method showcases the potential for scalable production of layered materials with increased yield and crystallinity, paving the way for their utilization in various applications.

cond-mat.mtrl-sci↗

arXiv2024

Unraveling the magnetic and electronic complexity of intermetallic ErPd$_2$Si$_2$: Anisotropic thermal expansion, phase transitions, and twofold magnetotransport behavior

We present a comprehensive investigation into the physical properties of intermetallic ErPd$_2$Si$_2$, a compound renowned for its intriguing magnetic and electronic characteristics. We confirm the tetragonal crystal structure of ErPd$_2$Si$_2$ within the $I4/mmm$ space group. Notably, we observed anisotropic thermal expansion, with the lattice constant $a$ expanding and $c$ contracting between 15 K and 300 K. This behavior is attributed to lattice vibrations and electronic contributions. Heat capacity measurements revealed three distinct temperature regimes: $T_1 \sim 3.0$ K, $T_\textrm{N} \sim 4.20$ K, and $T_2 \sim 15.31$ K. These correspond to the disappearance of spin-density waves, the onset of an incommensurate antiferromagnetic (AFM) structure, and the crystal-field splitting and/or the presence of short-range spin fluctuations, respectively. Remarkably, the AFM phase transition anomaly was observed exclusively in low-field magnetization data (120 Oe) at $T_\textrm{N}$. A high magnetic field ($B =$ 3 T) effectively suppressed this anomaly, likely due to spin-flop and spin-flip transitions. Furthermore, the extracted effective PM moments closely matched the expected theoretical value, suggesting a dominant magnetic contribution from localized 4$f$ spins of Er. Additionally, significant differences in resistance ($R$) values at low temperatures under applied $B$ indicated a magnetoresistance (MR) effect with a minimum value of -4.36\%. Notably, the measured MR effect exhibited anisotropic behavior, where changes in the strength or direction of the applied $B$ induced variations in the MR effect. A twofold symmetry of $R$ was discerned at 3 T and 9 T, originating from the orientation of spin moments relative to the applied $B$. Intriguingly, above $T_\textrm{N}$, short-range spin fluctuations also displayed a preferred orientation along the $c$-axis due to single-ion anisotropy.

cond-mat.str-el↗

arXiv2024

Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance

We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation.The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations.

cond-mat.mtrl-sci↗

arXiv2024

Structure and magnetic properties of a La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ single crystal

We have successfully grown large and good-quality single crystals of the La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ compound using the floating-zone method with laser diodes. We investigated the crystal quality, crystallography, chemical composition, magnetic properties and the oxidation state of Cr in the grown single crystals by employing a combination of techniques, including X-ray Laue and powder diffraction, scanning electron microscopy, magnetization measurements, X-ray photoelectron spectroscopy and light absorption. The La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ single crystal exhibits a single-phase composition, crystallizing in a trigonal structure with the space group $R\bar{3}c$ at room temperature. The chemical composition was determined as La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$, indicating a significant chromium deficiency. Upon warming, we observed five distinctive characteristic temperatures, namely $T_1 =$ 21.50(1) K, $T_2 =$ 34.98(1) K, $T_3 =$ 117.94(1) K, $T_4 =$ 155.01(1) K, and $T_{\textrm{N}} =$ 271.80(1) K, revealing five distinct magnetic anomalies. Our magnetization study allows us to explore the nature of these anomalies. Remarkably, the oxidation state of chromium in the single-crystal La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$, characterized by a band gap of 1.630(8) eV, is exclusively attributed to Cr$^{3+}$ ions, making a departure from the findings of previous studies on polycrystalline materials.

cond-mat.mtrl-sci↗