arXiv subjects

Wei Yue

Publications and source records attributed to Wei Yue.

3 recordsLinked to original sources

arXiv2025

Mott Gap Softening Coinciding with Spin Correlations Collapse in a-RuCl3

The Mott insulator a-RuCl3, featuring the intertwined interplay of spin-orbit coupling (SOC) and Kitaev spin correlations, provides an unparalleled platform for probing quantum many-body physics. Using scanning tunneling microscopy/spectroscopy (STM/STS), we compare temperature-dependent dI/dV spectra between in-situ grown monolayers and exfoliated bulk samples. Both systems exhibit pronounced Mott gap softening near 110 K, manifested by spectral weight transfer from Hubbard bands toward the Fermi level, resulting in low-energy correlated charge delocalization. Although this gap softening coincides with Kitaev paramagnetic and structural phase transitions in bulk crystals, monolayer studies provide compelling insights. By eliminating structural phase transition in monolayer sample, we suggest that spin correlations, rather than Coulomb interactions alone, may govern charge dynamics within the Mott-Hubbard framework, challenging conventional Mott-Hubbard paradigms. These results resolve a long-standing controversy regarding the Mott gap magnitude in a-RuCl3 and experimentally confirm the critical role of spin correlations in Mott physics.

cond-mat.str-el↗

arXiv2025

An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework

Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence (AI) models. In this study, a convolutional neural network model, VIPEEGNet, was developed and validated using EEGs recorded from Massachusetts General Hospital/Harvard Medical School. The VIPEEGNet was developed and validated using two independent datasets, collected between 2006 and 2020. The development cohort included EEG recordings from 1950 patients, with 106,800 EEG segments annotated by at least one experts (ranging from 1 to 28). The online testing cohort consisted of EEG segments from a subset of an additional 1,532 patients, each annotated by at least 10 experts. For the development cohort (n=1950), the VIPEEGNet achieved high accuracy, with an AUROC for binary classification of seizure, LPD, GPD, LRDA, GRDA, and "other" categories at 0.972 (95% CI, 0.957-0.988), 0.962 (95% CI, 0.954-0.970), 0.972 (95% CI, 0.960-0.984), 0.938 (95% CI, 0.917-0.959), 0.949 (95% CI, 0.941-0.957), and 0.930 (95% CI, 0.926-0.935). For multi classification, the sensitivity of VIPEEGNET for the six categories ranges from 36.8% to 88.2% and the precision ranges from 55.6% to 80.4%, and performance similar to human experts. Notably, the external validation showed Kullback-Leibler Divergence (KLD)of 0.223 and 0.273, ranking top 2 among the existing 2,767 competing algorithms, while we only used 2.8% of the parameters of the first-ranked algorithm.

cs.LG↗

arXiv2017

Carbon nanotubes heavy metal detection with stripping voltammetry: A review paper

The challenge of heavy metal detection for environmental, industrial and medical purposes has led to the development of many analytical techniques. Stripping voltammetry is a very sensitive electrochemical method and has been widely used for heavy metal detection. Carbon nanotubes, a well-studied carbon material with physical and chemical properties suited for electrode material is commonly employed for sensitive and selective metal detection in electrochemistry. This article reviews the recent (2011-2016) applications of carbon nanotubes as an electrode or electrode surface modifier for heavy metals detection with stripping voltammetry.

physics.chem-ph↗