arXiv subjects

Cheng-Yu Chen

Publications and source records attributed to Cheng-Yu Chen.

6 recordsLinked to original sources

arXiv2026

Crystallizing Substrates Drag Supported Nanoparticles

When a solid support undergoes crystallization, the advancing amorphous-to-crystalline transformation front separates regions of distinct surface energy, creating a moving interfacial energy boundary. A supported nanoparticle straddling such a boundary experiences an asymmetric particle-substrate interfacial energy environment that constitutes a lateral thermodynamic driving force for migration. Here, using in situ transmission electron microscopy to track Pt nanoparticle motion statistically, paired with time-resolved diffraction and 4D-STEM analysis to characterize support crystallization, we demonstrate that propagating crystallization fronts in amorphous AlO$_x$ thin films actively drag supported Pt nanoparticles over long distances. Temporal correlation between the onsets of support crystallization and rapid particle migration, together with 4D-STEM virtual crystallinity maps, establishes that the front drives particle motion. Phase-field simulations confirm that particle-substrate interfacial energy contrast alone sustains particle drag, and identify curvature gradients along the particle surface as the mechanism by which the advancing front redistributes mass and displaces the particle. These results establish a general mechanism by which any propagating surface-energy boundary on a substrate can act as a deterministic driver of supported nanoparticle transport.

cond-mat.mtrl-sci↗

arXiv2025

Interfacial Energy Gradients Drive Coalescence of Supported Nanoparticles

Understanding and controlling nanoparticle coalescence is crucial for applications ranging from catalysis to nanodevice fabrication, yet the behavior of nanoparticles on dynamically evolving, heterogeneous substrates remains poorly understood. Here, we employ in situ transmission electron microscopy to investigate platinum (Pt) nanoparticle dynamics on silicon nitride (SiN$_x$) substrates where localized crystalline silicon (Si) nanodomains are deliberately formed via electron beam irradiation at $800^\circ$C. We observe that Pt nanoparticles in contact with these Si pads transform into a more mobile platinum silicide (Pt$_3$Si) phase. Strikingly, these Pt$_3$Si nanoparticles exhibit pronounced directional migration away from the Si pads, driven by interfacial energy gradients, rather than undergoing stochastic Brownian motion. This directed movement fundamentally dictates coalescence pathways, leading to either enhanced sintering when particles are channeled together or inhibited coalescence when Si pads act as repulsive barriers. Our findings reveal that local substrate chemistry and the resulting interfacial energy landscapes can dominate over initial particle size or proximity in controlling solid-state nanoparticle migration and assembly. This work provides insights into how substrate heterogeneity can be used to direct nanoparticle behavior, challenging conventional coalescence models and offering pathways for the rational design of supported nanomaterials.

cond-mat.mtrl-sci↗

arXiv2024

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Neuroimaging in Frontotemporal Dementia (NIFD), present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical high-performance computing (HPC) nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ (Noisy Intermediate-Scale Quantum) era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline's superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

quant-ph↗

arXiv2023

Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.

cs.CL↗

arXiv2023

Discovery of compact disc Galaxies with High Surface Brightness in the Sloan Digital Sky Survey

Compact disc galaxies (CDGs) with high surface brightness were identified in the Sloan Digital Sky Survey (SDSS) data. We determined the surface profiles of the CDGs and compared them to those of normal-sized disk galaxies (NDGs). The CDGs have higher central brightness and older stellar age than the NDGs. Furthermore, the brightness profiles of the CDGs fit a S{\'e}rsic model with $n \approx 2.11$ and have a zero $g^{\prime}-r^{\prime}$ color gradient on average. By contrast, the NDGs fit an exponential profile and have a negative color gradient on average. These results indicate that the structure and stellar population of the CDGs and NDGs differ. We suggest that the CDGs are ancient galaxies in the quenching phase following the initial central starburst.

astro-ph.GA↗

arXiv2020

Dynamics of Companion Galaxies of Early-Type Galaxies

We estimated the dynamical masses of 115 early-type galaxies (ETGs) by analyzing the dynamics of satellite and companion galaxies of these ETGs. We selected galaxies with absolute magnitudes between -22 and -25 in the $K_s$-band from the Extragalactic Distance Database (EDD). We also selected 216 spiral galaxies for comparison. We employed a simple model to simulate the observed dynamical mass from satellite galaxies at various distances. Our simulations showed that the dynamical masses derived from satellite galaxies with elliptical orbits would be smaller than those with circular orbits even they contain the same dark mass halos. Therefore, relationships between the observed $M_\mathrm{dyn}/M_\mathrm{b}$ distributions and distances would depend on orbital shapes. From the relationships between our observed $M_\mathrm{dyn}/M_\mathrm{b}$ distributions and distances, we suggest that the satellite galaxies of the ETGs have relatively more elliptical orbits than those of the spiral galaxies have and the $M_\mathrm{dyn}/M_\mathrm{b}$ of the ETGs are greater than that of the spiral galaxies.

astro-ph.GA↗