arXiv subjects

Zhuohang Yu

Publications and source records attributed to Zhuohang Yu.

13 recordsLinked to original sources

arXiv2026

Anisotropic Dopant and Strain Architectures in WS$_2$ Nanocrystals Driven by Growth Kinetics

Dopant distribution in two-dimensional semiconductors is typically assumed to be stochastic, limiting deterministic defect engineering. Here, we show that non-equilibrium growth kinetics can be harnessed to define dopant-driven strain architectures in vanadium-doped WS$_2$ monolayers. Using synchrotron X-ray fluorescence, we identify preferential vanadium incorporation, anti-correlated with tungsten content, along crystallographic bisectors. An adsorption-growth-diffusion model with a single kinetic parameter quantitatively captures the dopant segregation arising from preferential corner adsorption and limited diffusion during chemical vapor deposition growth. Hyperspectral Raman imaging demonstrates mechanically induced vibrational responses, revealing localized tensile strain ($\varepsilon \approx0.70\%$) channels associated with the anisotropic dopant distribution. This regime is marked by the depletion of W-site-sensitive in-plane modes and the emergence of a localized $J2$ mode (210~cm$^{-1}$), which our ab-initio calculations attribute to antiphase V$-$V oscillations. These findings establish kinetic segregation as a route to deterministic chemical and strain architectures in 2D semiconductors, enabling programmable defect landscapes and strain engineering during synthesis.

cond-mat.mtrl-sci↗

arXiv2025

First principles and scanning tunneling spectroscopical evidences for thermodynamically stable "on-top" sulfur divacancy in monolayer WS$_{2}$

Chalcogen vacancies in monolayer transition metal dichalcogenides (TMDs), such as WS$_{2}$, play a crucial role in various applications ranging from optoelectronics and catalysis to quantum information science (QIS), making their identification and control essential. This study focuses on WS$_{2}$ single vacancy and vacancy pairs. Using first principles computations, we investigate their thermodynamic stabilities and electronic structures. We identify an "on-top" divacancy configuration where two vacancies sit on top of each other to be the only energetically stable complex with a binding energy of 160 meV. We compute a small difference in electronic structure with a shift of the unoccupied state by 140 meV for the divacancy complex and observe electronic state shift during Scanning Tunneling Spectroscopy of a series of vacancy in WS$_2$ providing spectroscopical evidence for the presence of this defect.

cond-mat.mtrl-sci↗

arXiv2025

Understanding and Controlling V-Doping and S-Vacancy Behavior in Two-Dimensional Semiconductors- Toward Predictive Design

Doping in transition metal dichalcogenide (TMD) monolayers provides a powerful method to precisely tailor their electronic, optical, and catalytic properties for advanced technological applications, including optoelectronics, catalysis, and quantum technologies. However, doping efficiency and outcomes in these materials are strongly influenced by the complex interactions between introduced dopants and intrinsic defects, particularly sulfur vacancies. This coupling between dopants and defects can lead to distinctly different behaviors depending on doping concentration, presenting significant challenges in the predictable and controlled design of TMD properties. For example, in this work we systematically varied the p-type vanadium (V) doping density in tungsten disulfide (WS2) monolayers and observed a transition in doping behavior. At low concentrations, V-dopants enhance the native optical properties of WS2, as evidenced by increased photoluminescence, without introducing new electronic states. However, at higher concentrations, V-dopants promote the formation of vanadium-sulfur vacancy complexes that generate mid-gap states, with energies that can be precisely tuned by controlling the vanadium concentration. Using a combination of excitation- and temperature-dependent photoluminescence microscopy, atomic-resolution scanning transmission electron microscopy, and first-principles calculations, we identify attractive interactions between p-type V-dopants and n-type monosulfur vacancies. Our results provide mechanistic understanding of how enthalpic dopant-defect interactions versus entropic effects govern the balance between property enhancement versus perturbation of transition metal dichalcogenides and suggest a pathway toward the rational design of doping strategies for next-generation optoelectronic, catalytic, and quantum devices.

cond-mat.mtrl-sci↗

arXiv2025

Inorganic Catalyst Efficiency Prediction Based on EAPCR Model: A Deep Learning Solution for Multi-Source Heterogeneous Data

The design of inorganic catalysts and the prediction of their catalytic efficiency are fundamental challenges in chemistry and materials science. Traditional catalyst evaluation methods primarily rely on machine learning techniques; however, these methods often struggle to process multi-source heterogeneous data, limiting both predictive accuracy and generalization. To address these limitations, this study introduces the Embedding-Attention-Permutated CNN-Residual (EAPCR) deep learning model. EAPCR constructs a feature association matrix using embedding and attention mechanisms and enhances predictive performance through permutated CNN architectures and residual connections. This approach enables the model to accurately capture complex feature interactions across various catalytic conditions, leading to precise efficiency predictions. EAPCR serves as a powerful tool for computational researchers while also assisting domain experts in optimizing catalyst design, effectively bridging the gap between data-driven modeling and experimental applications. We evaluate EAPCR on datasets from TiO2 photocatalysis, thermal catalysis, and electrocatalysis, demonstrating its superiority over traditional machine learning methods (e.g., linear regression, random forest) as well as conventional deep learning models (e.g., ANN, NNs). Across multiple evaluation metrics (MAE, MSE, R2, and RMSE), EAPCR consistently outperforms existing approaches. These findings highlight the strong potential of EAPCR in inorganic catalytic efficiency prediction. As a versatile deep learning framework, EAPCR not only improves predictive accuracy but also establishes a solid foundation for future large-scale model development in inorganic catalysis.

cs.LG↗

arXiv2025

Matching-Free Depth Recovery from Structured Light

We introduce a novel approach for depth estimation using images obtained from monocular structured light systems. In contrast to many existing methods that depend on image matching, our technique employs a density voxel grid to represent scene geometry. This grid is trained through self-supervised differentiable volume rendering. Our method leverages color fields derived from the projected patterns in structured light systems during the rendering process, facilitating the isolated optimization of the geometry field. This innovative approach leads to faster convergence and high-quality results. Additionally, we integrate normalized device coordinates (NDC), a distortion loss, and a distinctive surface-based color loss to enhance geometric fidelity. Experimental results demonstrate that our method outperforms current matching-based techniques in terms of geometric performance in few-shot scenarios, achieving an approximately 30% reduction in average estimated depth errors for both synthetic scenes and real-world captured scenes. Moreover, our approach allows for rapid training, being approximately three times faster than previous matching-free methods that utilize implicit representations.

cs.CV↗

arXiv2024

EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns

Conventional methods, including Decision Tree (DT)-based methods, have been effective in scientific tasks, such as non-image medical diagnostics, system anomaly detection, and inorganic catalysis efficiency prediction. However, most deep-learning techniques have struggled to surpass or even match this level of success as traditional machine-learning methods. The primary reason is that these applications involve multi-source, heterogeneous data where features lack explicit relationships. This contrasts with image data, where pixels exhibit spatial relationships; textual data, where words have sequential dependencies; and graph data, where nodes are connected through established associations. The absence of explicit Feature Relation Patterns (FRPs) presents a significant challenge for deep learning techniques in scientific applications that are not image, text, and graph-based. In this paper, we introduce EAPCR, a universal feature extractor designed for data without explicit FRPs. Tested across various scientific tasks, EAPCR consistently outperforms traditional methods and bridges the gap where deep learning models fall short. To further demonstrate its robustness, we synthesize a dataset without explicit FRPs. While Kolmogorov-Arnold Network (KAN) and feature extractors like Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and Transformers struggle, EAPCR excels, demonstrating its robustness and superior performance in scientific tasks without FRPs.

cs.LG↗

arXiv2024

Phonon and exciton temperature-dependent properties of twisted MoS$_2$

In the present work, Raman and photoluminescence spectroscopies were used to study the dynamics of phonons and different excitons of MoS$_2$ bilayer under a rotation of 29$^{\circ}$ dependent of the temperature. The twisted bilayer (T-2L) of MoS$_2$ was obtained through mechanical exfoliation, and subsequently rotated using a dimethyl polysiloxane (PDMS) substrate and deterministic transferred to a SiO$_2$ substrate. The Raman spectrum of the twisted bilayer presents three peaks E$'$ (386 cm$^{-1}$), A$'_{1}$ (405 cm$^{-1}$) and an FA$'$ peak at approximately 409 cm$^{-1}$ linked to the A$'_{1}$ mode, which is attributed to a Moir\'e pattern phonon. Both modes (A$'_{1}$ and FA$'$) are dependent of light polarization in a way that demonstrates an effective coupling between the layers. It was also verified through the Gr\"uneisen parameter, an increase in the anharmonicity of the mode in the E$'$ plane and a decrease the same for A$'_{1}$. In the PL measurements, the appearance of an exciton in T-2L was verified, which generated a second shoulder measured at $\approx$1.58 eV attributed to an indirect transition of an I trion. The interaction between the rotated monolayers of MoS$_{2}$ proved to be an important parameter for possible fine-tuning of the properties of bilayer samples.

cond-mat.mtrl-sci↗

arXiv2023

A substitutional quantum defect in WS$_2$ discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the space of possible defects is immense, making the identification of high-performance quantum defects extremely challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS$_2$, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$_{\rm S}^{0}$) as very promising and fabricate it with scanning tunneling microscopy (STM). The Co$_{\rm S}^{0}$ electronic structure measured by STM agrees with first principles and showcases an attractive new quantum defect. Our work shows how HT computational screening and novel defect synthesis routes can be combined to design new quantum defects.

cond-mat.mtrl-sci↗

arXiv2023

Graphene-driven correlated electronic states in one dimensional defects within WS$_2$

Tomonaga-Luttinger liquid (TLL) behavior in one-dimensional systems has been predicted and shown to occur at semiconductor-to-metal transitions within two-dimensional materials. Reports of one-dimensional defects hosting a Fermi liquid or a TLL have suggested a dependence on the underlying substrate, however, unveiling the physical details of electronic contributions from the substrate require cross-correlative investigation. Here, we study TLL formation within defectively engineered WS$_2$ atop graphene, where band structure and the atomic environment is visualized with nano angle-resolved photoelectron spectroscopy, scanning tunneling microscopy and spectroscopy, and non-contact atomic force microscopy. Correlations between the local density of states and electronic band dispersion elucidated the electron transfer from graphene into a TLL hosted by one-dimensional metal (1DM) defects. It appears that the vertical heterostructure with graphene and the induced charge transfer from graphene into the 1DM is critical for the formation of a TLL.

cond-mat.mtrl-sci↗

arXiv2022

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (for example, amide, amino acid, carboxylic acid), we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

q-bio.QM↗

arXiv2021

Autonomous Investigations over WS$_2$ and Au{111} with Scanning Probe Microscopy

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS$_2$ sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS$_2$, Au face-centered cubic, and Au hexagonal close packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

cond-mat.mtrl-sci↗

arXiv2021

Trends on 3d Transition Metal Coordination on Monolayer MoS$_2$

Two-dimensional materials (2DM) have attracted much interest due to their distinct optical, electronic, and catalytic properties. These properties can be by tuned a range of methods including substitutional doping or, as recently demonstrated, by surface functionalization with single atoms, increasing even further 2DM portfolio. Here we theoretically and experimentally describe the coordination reaction between MoS$_2$ monolayers with 3d transition metals (TMs), exploring the nature and the trend of MoS$_2$-TMs interaction. Density Functional Theory calculations, X-Ray Photoelectron Spectroscopy (XPS), and Photoluminescence (PL) point to the formation of MoS$_2$-TM coordination complexes, where the adsorption energy trend for 3d TM resembles the crystal-field (CF) stabilization energy for weak-field complexes. Pearson's theory for hard-soft acid-base and Ligand-field theory were applied to discuss the periodic trends on 3d TM coordination on the MoS$_2$ surface. We found that softer acids with higher ligand field stabilization energy, such as Ni$^{2+}$, tend to form bonds with more covalent character with MoS$_2$, which can be considered a soft base. On the other hand, harder acids, such as Cr$^{3+}$, tend to form bonds with more ionic character. Additionally, we studied the trends in charge transfer and doping observed in the XPS and PL results, where metals such as Ni led to an n-type of doping, while Cu functionalization results in p-type doping. Therefore, the formation of coordination complexes on TMD's surface is demonstrated to be a promising and effective way to control and to understand the nature of the single-atom functionalization of TMD.

cond-mat.mtrl-sci↗

arXiv2020

Tunable Ferromagnetism and Thermally Induced Spin Flip in Vanadium-doped Tungsten Diselenide Monolayers at Room Temperature

The outstanding optoelectronic and valleytronic properties of transition metal dichalcogenides (TMDs) have triggered intense research efforts by the scientific community. An alternative to induce long-range ferromagnetism (FM) in TMDs is by introducing magnetic dopants to form a dilute magnetic semiconductor. Enhancing ferromagnetism in these semiconductors not only represents a key step towards modern TMD-based spintronics, but also enables exploration of new and exciting dimensionality-driven magnetic phenomena. To this end, we show tunable ferromagnetism at room temperature and a thermally induced spin flip (TISF) in monolayers of V-doped WSe2. As vanadium concentrations increase within the WSe2 monolayers the saturation magnetization increases, and it is optimal at ~4at.% vanadium; the highest doping/alloying level ever achieved for V-doped WSe2 monolayers. The TISF occurs at ~175 K and becomes more pronounced upon increasing the temperature towards room temperature. We demonstrate that TISF can be manipulated by changing the vanadium concentration within the WSe2 monolayers. We attribute TISF to the magnetic field and temperature dependent flipping of the nearest W-site magnetic moments that are antiferromagnetically coupled to the V magnetic moments in the ground state. This is fully supported by a recent spin-polarized density functional theory calculation. Our findings pave the way for the development of novel spintronic and valleytronic nanodevices based on atomically thin magnetic semiconductors and stimulate further studies in this rapidly expanding research field of 2D magnetism.

cond-mat.mtrl-sci↗