arXiv subjects

Xiaoying Qin

Publications and source records attributed to Xiaoying Qin.

4 recordsLinked to original sources

arXiv2025

Synergistic modulation of band structure and phonon transport for higher thermoelectric performance of WSe2

Tungsten diselenide (WSe2) emerges as a promising thermoelectric (TE) candidate due to its high thermopower (S), cost-effectiveness, and environmentally friendly characteristics. However, pristine WSe2 exhibits limited electrical conductivity (sigma), a low power factor (PF), and high lattice thermal conductivity (k_L), which restrict its overall TE performance. Here, we show that through co-doping of Nb for W and Te for Se in WSe2, its power factor increases 17-fold, reaching 8.91 microW cm^-1 K^-2 at 850 K. Simultaneously, its lattice thermal conductivity (k_L) decreases from 1.70 W m^-1 K^-1 to 0.48 W m^-1 K^-1. Experiments and density functional theory (DFT) analysis demonstrate that the enhancement of PF is linked to an increased density of states, higher effective mass (md*), improved mobility (mu), and elevated electrical conductivity (sigma) owing to the replacement of Se2- with Te2-; while the observed 72% reduction in k_L results primarily from phonon scattering at Te-Se and Nb-W defects. As a result, a remarkable ZT_max ~ 1 is obtained at 850 K for the sample W0.95Nb0.05Se2-yTey with y = 0.3, which is about a 30-fold increase compared to WSe2, proving that Nb and Te co-doping in WSe2 can significantly boost its TE performance.

cond-mat.mtrl-sci↗

arXiv2023

Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification

Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification.

cs.CV↗

arXiv2022

Topological superconductivity in a topological insulator

Topological superconductivity is an exotic quantum phenomenon with coupled nontrivial topological order and superconductivity together. A direct idea for producing topological superconductors is to create superconductivity based on the well recognized topological insulators. The topological insulating states in highly efficient thermoelectric materials Bi$_2$Te$_3$ and Bi$_2$Se$_3$ and their alloy Bi$_{2}$Te$_{3-x}$Se$_{x}$ have been established from angle-resolved photoemission and transport experiments. Superconductivity was also observed based on these popular topological insulators by the application of pressure chemical dopant and heterostructures. However, the experiments mainly focusing on Bi$_{2}$Se$_3$ doped by metals have not provided the consistent evidence to support the topological superconductivity. Here we carry out a systematic high-pressure study on a topological insulator Bi$_{2}$Te$_{2.7}$Se$_{0.3}$ to provide the convincing evidence for the expected topological superconductivity. Four phases with different structures are found upon compression. The topological surface state is identified in the entire initial phase, while superconductivity is found to coexist with such a state of the compressed material after its passing the electronic topological transition, followed by three other superconducting phases without topological character. For these superconducting phases, we observe that the upper critical field follows with the temperature in the critical exponent ${2/3}$ for the first one with the topological surface state and $1$ for the left. These observations support the realization of the topological superconductivity in the initial phase according to the theoretically proposed critical field measure. This work also points out a big pool and new direction for finding topological superconductors from topological thermoelectric materials.

cond-mat.supr-con↗

arXiv2017

Chemical reaction directed oriented attachment: from precursor particles to new substances

The oriented attachment (OA) of nanoparticles is an important mechanism for the synthesis of the crystals of inorganic functional materials, and the formation of natural minerals. For years it has been generally acknowledged that OA is a physical process, i.e., particle alignments and interface fusion via mass diffusion, not involving the formation of new substances. Hence, the obtained crystals maintain identical crystallographic structures and chemical constituents to those of the precursor particles. Here we report a chemical reaction directed OA growth, through which Y2(CO3)3.2H2O nanoparticles are converted to single-crystalline double-carbonates (e.g., NaY(CO3)2.6H2O). The dominant role of OA growth is supported by our first-principles calculations. Such a new OA mechanism enriches the aggregation-based crystal growth theory.

cond-mat.mtrl-sci↗