arXiv subjects

Guangcun Shan

Publications and source records attributed to Guangcun Shan.

At least 19 recordsLinked to original sources

arXiv2025

Molecular Determinants of Orthosteric-allosteric Dual Inhibition of PfHT1 by Computational Assessment

To overcome antimalarial drug resistance, carbohydrate derivatives as selective PfHT1 inhibitor have been suggested in recent experimental work with orthosteric and allosteric dual binding pockets. Inspired by this promising therapeutic strategy, herein, molecular dynamics simulations are performed to investigate the molecular determinants of co-administration on orthosteric and allosteric inhibitors targeting PfHT1. Our binding free energy analysis capture the essential trend of inhibitor binding affinity to protein from published experimental IC50 data in three sets of distinct characteristics. In particular, we rank the contribution of key residues as binding sites which categorized into three groups based on linker length, size of tail group, and sugar moiety of inhibitors. The pivotal roles of these key residues are further validated by mutant analysis where mutated to nonpolar alanine leading to reduced affinities to different degrees. The exception was fructose derivative, which exhibited a significant enhanced affinity to mutation on orthosteric sites due to strong changed binding poses. This study may provide useful information for optimized design of precision medicine to circumvent drug-resistant Plasmodium parasites with high efficacy.

physics.bio-ph↗

arXiv2025

High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture

The removal of leaked radioactive iodine isotopes in humid environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and machine learning were combined to reveal the iodine capture performance of 1816 metal-organic framework (MOF) materials under humid air conditions. Firstly, the relationship between the structural characteristics of MOFs and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. Subsequently, two machine learning regression algorithms - Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOFs. In addition to 6 structural features, 25 molecular features and 8 chemical features were incorporated to enhance the prediction accuracy of the machine learning algorithms. Feature importance was assessed to determine the relative influence of various features on iodine adsorption performance, in which the Henry's coefficient and heat of adsorption to iodine were found the two most crucial chemical factors. Furthermore, four types of molecular fingerprints were introduced for providing comprehensive and detailed structural information of MOF materials. The top 20 most significant MACCS molecular fingerprints were picked out, revealing that the presence of six-membered ring structures and nitrogen atoms in the MOFs were the key structural factors that enhanced iodine adsorption, followed by the existence of oxygen atoms. This work combined high-throughput computation, machine learning, and molecular fingerprints to comprehensively elucidate the multifaceted factors influencing the iodine adsorption performance of MOFs, offering profound insightful guidelines for screening and structural design of advanced MOF materials.

cs.LG↗

arXiv2023

Edge-pinning effect of graphene nanoflakes sliding atop graphene

Edge effect is one of the detrimental factors preventing superlubricity in laminar solid lubricants. Separating the friction contribution from the edge atom and inner atom is of paramount importance for rational design of ultralow friction across scales in van der Waals heterostructures. To decouple these contributions and provide the underlying microscopic origin at the atomistic level, we considered two contrast models, namely, graphene nanoflakes with dimerized and pristine edges sliding on graphene monolayer based on extensive ab initio calculations. We found the edge contribution to friction is lattice orientation dependence. In particular, edge pinning effect by dimerization is obvious for misaligned contact but suppressed in aligned lattice orientation. The former case providing local commensuration along edges is reminiscent of Aubry's pinned phase and the contribution of per edge carbon atom to the sliding potential energy corrugation is even 1.5 times more than that of an atom in bilayer graphene under commensurate contact. Furthermore, we demonstrated that the dimerized edges as high frictional pinning sites are robust to strain engineering and even enhanced by fluorination. Both structural and chemical modification in the tribological system constructed here offers the atomic details to dissect the undesirable edge pinning effect in layered materials which may give rise to the marked discrepancies in measured friction parameters from the same superlubric sample or different samples with the same size and identical preparation.

cond-mat.mtrl-sci↗

arXiv2023

Combined Experimental and Theoretical Studies on Iodine Capture of Zr-based Metal-Organic Frameworks: Effect of N-functionalization and Adsorption Mechanism

The potential leakage of nuclear waste, especially radioiodine, is a major safety concerning issue around the world. To remove radioiodine from nuclear waste efficiently, there is an urgent demand for adsorbents that possess both high stability and strong adsorption affinity for environmental remediation. Herein, two Zr-based metal-organic frameworks (Zr-MOFs) and their N-functionalized analogues have been synthesized and researched for iodine adsorption in both vapours and solutions. It was found that Zr-MOFs with N-enriched ligands (e.g., pyridine and amino) exhibited the faster iodine adsorption rate and the higher iodine uptake amount (e.g., reaching adsorption equilibrium within 4 hours with the removal rate of above 85% for iodine solution adsorption) than their unfunctionalized counterparts (UiO-66 and UiO-67). The critical role played by N-enriched groups in enhancing iodine adsorption has been revealed through versatile model fittings, X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy characterizations, as well as density functional theory (DFT) calculations. Compared to those in amino-group, the N-atoms in pyridine-groups showed a deeper affinity towards iodine molecules. Remarkably, the N-enriched UiOs adsorbents also exhibited good recyclability, especially UiO-66-PYDC and UiO-67-NH2 could maintain the removal efficiency of 89.05% and 85.49% after four adsorption-desorption recycling tests. With the strong iodine uptake affinity and outstanding regeneration performance, this work has systematically investigated the impact of N-functionalization on the enhanced performance for iodine capture by using the N-enriched UiO MOFs as promising adsorbents, providing an insightful guideline into the physical chemistry of adsorption mechanism behind the radioiodine capture.

cond-mat.mtrl-sci↗

arXiv2022

An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest

Recently, deep learning technology have been extensively used in the field of image recognition. However, its main application is the recognition and detection of ordinary pictures and common scenes. It is challenging to effectively and expediently analyze remote-sensing images obtained by the image acquisition systems on unmanned aerial vehicles (UAVs), which includes the identification of the target and calculation of its position. Aerial remote sensing images have different shooting angles and methods compared with ordinary pictures or images, which makes remote-sensing images play an irreplaceable role in some areas. In this study, a new target detection and recognition method in remote-sensing images is proposed based on deep convolution neural network (CNN) for the provision of multilevel information of images in combination with a region proposal network used to generate multiangle regions-of-interest. The proposed method generated results that were much more accurate and precise than those obtained with traditional ways. This demonstrated that the model proposed herein displays tremendous applicability potential in remote-sensing image recognition.

cs.CV↗

arXiv2022

Prediction of novel two-dimensional rare-earth material with room-temperature ferromagnetism and large perpendicular magnetic anisotropy

Novel 2D ferromagnets with high Curie temperature and large perpendicular magnetic anisotropy are especially attractive owing to the future promising application in modern spintronics, but meanwhile the 2D ferromagnetic materials with high Curie temperature and large perpendicular magnetic anisotropy are rarely reported. Based on density functional theory (DFT) calculations, we predict a new kind of 2D ferromagnetic materials - GdB2N2, which possesses large magnetic moment, high Curie temperature (335 K) and large perpendicular magnetic anisotropy (10.38 meV/f.u.). Biaxial strain ranging from -0.5% to 5% and different concentrations of charge-carrier doping are applied to reveal the influence on the Curie temperature and magnetic anisotropy energy (MAE). Besides, magnetic coupling process within GdB2N2 is found to be via a Ruderman-Kittel-Kasuya-Yosida (RKKY) mechanism. In summary, our work here predicts a novel 2D rare-earth material GdB2N2, which not only enriches the category of 2D room-temperature ferromagnets, but also proposes a new possibility of combining traditional 2D materials and rare-earth materials to achieve more intriguing magnetic properties, finally it carves out the path for the next-generation spintronic devices and sensors.

cond-mat.mtrl-sci↗

arXiv2021

Prediction of 2D ferromagnetism and monovalent europium ions in the EuBr/graphene heterojunctions

Europium, one of the rare earth elements, exhibits +2 and +3 valence states and has been widely used for magnetic modification of materials. Based on density functional theory calculations, we predict the 2D EuBr/graphene heterojunctions to exhibit metallicity, huge intrinsic-ferromagnetism nearly 7.0 μB per Eu and the special monovalent Eu ions. Electron localization function (ELF), difference charge densities and Bader charge analyses demonstrate that there are cation-π interactions between the EuBr films and graphene, which explains the stability of these unusual heterojunctions. Graphene works as substrate to enable the stability of EuBr monolayer crystals where EuBr plays an important role to yield ferromagnetism and enhance metallicity in the heterojunctions. Monte Carlo simulations are used to estimate a Curie temperature of about 7 K, which, together with magnetic configurations, can be further modulated by external strains and charge-carrier doping. In general, our theoretical work predicts the properties of the novel 2D ferromagnetic EuBr/graphene heterojunctions, suggests the possibility of combining the 2D intrinsic-ferromagnetic metal halide crystals and graphene, and opens up a new perspective in next-generation electronic, spintronic devices and high-performance sensors.

cond-mat.mtrl-sci↗

arXiv2021

Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures

Magnetism prediction is of great significance for Fe-based metallic glasses (FeMGs), which have shown great commercial value. Theories or models established based on condensed matter physics exhibit several exceptions and limited accuracy. In this work, machine learning (ML) models learned from a large amount of experimental data were trained based on eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and random forest to predict the magnetic properties of FeMGs. The XGBoost and ANN models exhibited comparably excellent predictive performance, with R^2 >= 0.903, mean absolute percentage error (MAPE) <= 6.17, and root mean squared error (RMSE) <= 0.098. The trained ML models aggregate the influence of 13 factors, which is difficult to achieve in traditional physical models. The influence of local structure, which was represented by the experimental parameter of the supercooled liquid region, presented a significant impact on the predictive performance of ML models. The developed ML-based method here can predict the magnetic properties of FeMGs by considering multiple factors simultaneously, including complex local structures.

cond-mat.mtrl-sci↗

arXiv2020

Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network

Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition. On one hand, it is how to correctly recognize different mathematical symbols. On the other hand, it is how to correctly recognize the two-dimensional structure existing in mathematical expressions. Inspired by recent work in deep learning, a new neural network model that combines a Multi-Scale convolutional neural network (CNN) with an Attention recurrent neural network (RNN) is proposed to identify two-dimensional handwritten mathematical expressions as one-dimensional LaTeX sequences. As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.

cs.CV↗

arXiv2020

A highly sensitive piezoresistive sensor based on MXene and polyvinyl butyral with a wide detection limit and low power consumption

As a new class of two-dimensional transition-metal carbide and carbonitride, MXene have been widely used in the energy storage, sensor, catalysis, electromagnetic interference shielding and other field. It is a challenge to simultaneously realize a sensor of extremely high sensitivity, wide detection limits, low power consumption and good mechanical stability. In this work, taking advantage of high conductivity of MXene and porous structure of polyvinyl butyral, a highly sensitive piezoresistive sensor was fabricated. The fabricated MXene/PVB-based sensor exhibits highly sensitive reliably with a factor of ~11.9 kPa^-1, ~1.15 kPa^-1 and ~0.20 kPa^-1 in the ranges of 31.2 Pa-312 Pa, 312 Pa- 62.4 kPa and 62.4 kPa-1248.4 kPa, respectively. The sensor has a wide detection range (~31.2 Pa to ~2.205 MPa), low detection limit (6.8 Pa), low detection voltage (0.1 mV), low power consumption (~3.6 * 10^-10 W), fast response time ( ~110 ms), as well as good mechanical stability (over 10,000 maximum-pressure cycles). Moreover, it is demonstrated that the sensor can detect subtle bending and release activities of human, including arterial pulses and voice signal, which is potentially suitable as a wide detection range, highly sensitive and low power consumption piezoresistive sensor. This work provides a new avenue to expand the application of MXene-based flexible pressure sensor in the field of wide sensing range and ultra-low power consumption.

physics.app-ph↗

arXiv2020

Accelerating temporal action proposal generation via high performance computing

Temporal action recognition always depends on temporal action proposal generation to hypothesize actions and algorithms usually need to process very long video sequences and output the starting and ending times of each potential action in each video suffering from high computation cost. To address this, based on boundary sensitive network we propose a new temporal convolution network called Multipath Temporal ConvNet (MTN), which consists of two parts i.e. Multipath DenseNet and SE-ConvNet. In this work, one novel high performance ring parallel architecture based on Message Passing Interface (MPI) is further introduced into temporal action proposal generation, which is a reliable communication protocol, in order to respond to the requirements of large memory occupation and a large number of videos. Remarkably, the total data transmission is reduced by adding a connection between multiple computing load in the newly developed architecture. It is found that, compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection task with multiple GPUs, which is suitable for dealing with the tasks of temporal action proposal generation, especially for large datasets of millions of videos. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms other state-of-art temporal action detection methods with high recall and high temporal precision. In addition, a time metric is further proposed here to evaluate the speed performance in the distributed training process.

cs.CV↗

arXiv2020

Hydrogen Bond Interaction Promotes Flash Energy Transport at MXene-water Interface

There are emerging applications for photothermal conversion utilizing MXene, but the mechanism under these applications related interfacial energy migration from MXene to the attached surface layer is still unknown. In this paper, with comprehensive ultrafast studies, we reported the energy migration pathway from MXene (Ti3C2Tx) to local environment under plasmonic excitation. Our data found that in water, energy dissipation is divided into fast hydrogen bond mediated channel and slow lattice motion mediated channel.The experimental results suggest that in water, nearly 80% energy in MXene that gained from the photoexcitation quickly dissipates into surrounding water molecules within 7 ps as a hydrogen bond mediated fast channel, and the remaining energy vanishes with time constant ~100 ps as a lattice motion mediated slow channel. The fast energy migration would result in the prominent interfacial energy conductance 150-300 MW *m{-2}* K{-1} for MXene-water interface. Tuning the solvent into ethanol could both narrow the energy dissipation to 35% through the fast channel and slow down the thermal channel (400 ps). To gain the molecular insight, molecular dynamic results presented different solvents had significantly different H bond forming ability on MXene surface. Our results suggested that interfacial interaction is crucial for effective hydrogen bonds on MXene surface to channel the excitation dissipation, providing important insights into the photothermal applications with MXene.

physics.chem-ph↗

arXiv2020

Recognizing Handwritten Mathematical Expressions as LaTex Sequences Using a Multiscale Robust Neural Network

In this paper, a robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output LaTeX sequences, which can effectively and correctly focus on where each step of output should be concerned and has a positive effect on analyzing the two-dimensional structure of handwritten mathematical expressions and identifying different mathematical symbols in a long expression. With the addition of visualization, the model's recognition process is shown in detail. In addition, our model achieved 49.459% and 46.062% ExpRate on the public CROHME 2014 and CROHME 2016 datasets. The present model results suggest that the state-of-the-art model has better robustness, fewer errors, and higher accuracy.

cs.CV↗

arXiv2020

Understanding high ordering temperature in Gd$_6$FeBi$_2$ magnet: critical behavior, electronic structure and crystal-field analysis

Gd6FeBi2 is reported as the only one room-temperature magnet with a Curie temperature (Tc) of ca. 350 K among more than hundreds of compounds with its structural type, which makes it more attractive in potential applications. To reveal the origin of such high ordering temperature, critical behaviors, electronic structure and crystal-field effects of Gd6FeBi2 are investigated in this work. The short-range Gd-Fe ferrimagnetic interaction is supported by the non-Curie-Weiss paramagnetic behavior, crystal and electronic structure analyses, in agreement with previous DFT calculations. Unlike the strong TM-TM exchange interactions, the Gd-Fe exchange interaction shows limited influence on the critical exponents determined by long-range exchange interactions, which seems a common feature in RE-TM based alloys without TM-TM exchange interactions. However, the strong Gd-Fe hybridization reduces the influence of vibronic couplings on the short-range exchange interaction and thus allows a high Tc. The broadening or splitting mechanism of Gd 4f-electron bands is addressed based on crystal-field analysis and likely another factor for elevated Tc in Gd6FeBi2 and Gd-based compounds with non-magnetic elements. Different magnetic behaviors among isostructural compounds, and the relationship between the band splitting and crystal-field effects is also discussed.

physics.app-ph↗

arXiv2019

Metal oxide nanomaterials for pseudocapacitors

With the rapid development of economy, the consumption of fossil fuels, and the increasing environment pollution, there is in urgent need of seeking clean and renewable energy sources, as well as highly efficient and low-cost energy storage technologies. Concerning electrochemically active materials, transition metal oxides (TMOs) are regarded as the most promising candidates for the next generation SCs and have been widely reported. Therefore, in this review, we mainly pay attention to TMOs with excellent electrochemical performance. This review is divided into four parts: (a) the background including energy storage mechanisms and electrochemical characterizations of SCs; (b) various kinds of TMOs for SCs are analyzed in detail involved in crystal structure, conductivity, and energy storage mechanism; (c) hybridizations with other materials to improve the electrochemical performance of TMOs are summarized and discussed; (d) a perspective and short remarks about the development and design of TMOs for superior performance SCs are shown.

physics.app-ph↗

arXiv2019

Plasmonic Light Illumination Creates a Channel to Achieve Fast Degradation of Ti3C2Tx Nanosheets

Two-dimensional (2D) material-controllable degradation under light radiation is crucial for their photonics and medical-related applications, which are yet to be investigated. In this paper, we first report the laser illumination method to regulate the degradation rate of Ti3C2Tx nanosheets in aqueous solution. Comprehensive characterization of intermediates and final products confirmed that plasmonic laser promoting the oxidation was strikingly different from heating the aqueous solution homogeneously. Laser illumination would nearly 10 times accelerate the degradation of Ti3C2Tx nanosheets in initial stage and create many smaller-sized oxidized products in a short time. Laser-induced fast degradation was principally ascribed to surface plasmonic resonance effect of Ti3C2Tx nanosheets. The degradation ability of such illumination could be controlled either by tuning the excitation wavelength or changing the excitation power. Furthermore, the laser- or thermal-induced degradation could be retarded by surface protection of Ti3C2Tx nanosheets. Our results suggest that plasmonic electron excitation of Ti3C2Tx nanosheets could build a new reaction channel and lead to the fast oxidation of nanosheets in aqueous solution, potentially enabling a series of water-based applications.

physics.app-ph↗

arXiv2019

AED-Net: An Abnormal Event Detection Network

It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.

cs.CV↗

arXiv2019

Two-Dimensional Scanning Phased Array Based on Pattern Reconfigurable Antenna

This paper presents a novel planar phased array that can work in Ka band designed for two-dimensional scanning. A four-modes pattern reconfigurable element based on microstrip Yagi antenna is proposed, and then developed into the 4X4 phased array, in which two feed points are introduced to reduce sidelobe level (SLL). The simulated results show that our proposed phased array is able to cover the scanning range from -60° to +60°in H-plane(yoz-plane) and -47.2°to +47.2°in E-plane(xoz-plane) with the gain reduction less than 3dB, both the SLLs of which are 10dB less than main beam. The two-dimensional scanning phased array based on pattern reconfigurable antenna (PRA) presented here is suitable for the satellite communication.

physics.ins-det↗