A5100356830- X-ray Diffraction in Crystallography
- Crystallization and Solubility Studies
- Advanced Photocatalysis Techniques
- Machine Learning in Materials Science
- Market Dynamics and Volatility
- Energy Load and Power Forecasting
- Physics of Superconductivity and Magnetism
- Perovskite Materials and Applications
- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
- Superconductivity in MgB2 and Alloys
- Housing Market and Economics
- Catalytic Processes in Materials Science
- Magnetic and transport properties of perovskites and related materials
- Iron-based superconductors research
- Quantum optics and atomic interactions
- TiO2 Photocatalysis and Solar Cells
- Photonic Crystals and Applications
- Aluminum Alloys Composites Properties
- Conducting polymers and applications
- Microstructure and Mechanical Properties of Steels
- Multiferroics and related materials
- Catalysis and Oxidation Reactions
- Complex Network Analysis Techniques
- Advanced machining processes and optimization
Northwestern Polytechnical University
2022-2025
North Carolina State University
2016-2024
Xi'an University of Science and Technology
2024
Chinese Academy of Medical Sciences & Peking Union Medical College
2024
Jiangnan University
2024
Chinese Academy of Agricultural Engineering
2024
Ministry of Agriculture and Rural Affairs
2024
Wuxi Vocational Institute of Commerce
2021-2024
North Central State College
2024
Belarusian National Technical University
2022-2024
Abstract Low‐dimensional Ruddlesden–Popper perovskites (RPPs) exhibit excellent stability in comparison with 3D perovskites; however, the relatively low power conversion efficiency (PCE) limits their future application. In this work, a new fluorine‐substituted phenylethlammonium (PEA) cation is developed as spacer to fabricate quasi‐2D (4FPEA) 2 (MA) 4 Pb 5 I 16 ( n = 5) perovskite solar cells. The champion device exhibits remarkable PCE of 17.3% J sc 19.00 mA cm −2 , V oc 1.16 V, and fill...
Titanium dioxide (TiO2) photocatalysts in the form of thin films are great interest due to their tunable optical band gaps, Eg's, which promising candidates for applications visible-light photocatalytic activities. Previous studies have shown that processing conditions, dopant types and concentrations, different combinations two impacts on structural, microscopic, properties TiO2 films. The lattice parameters surface area strongly correlated with Eg values, conventionally simulated studied...
Summary Agricultural commodity price forecasting represents a key concern for market participants. We explore the usefulness of neural network modeling problems in datasets daily prices over periods greater than 50 years coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. By investigating different model settings across algorithm, delay, hidden neuron, data‐splitting ratio, we arrive at models leading to decent performance each commodity, with overall relative root mean...
It is an important task to make predictions of trading volumes financial indices market participants. In the present study, we focus on this issue for Chinese Stock Index 300 (CSI300) spot by exploring high-frequency one-minute data spanning launch date corresponding futures two years after all constituent stocks become shortable, a period witnessing expanding activities. As volume series rather irregular, neural network considered tackle prediction problem. Several questions are pursued,...
To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models' potential is explored for forecasting daily prices platinum and palladium over about fifty-year period. For purpose, one hundred twenty model settings are examined, including different training algorithms, numbers hidden neurons delays, ratios used to segment the data. With analysis, two models leading stable accurate forecast results...
Purpose For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale index for period January 1, 2010 10, 2020 used address specific challenge Chinese market. Design/methodology/approach The nonlinear auto-regressive neural network (NAR-NN) model method used. Forecasting performance based on various settings, such as...
The house market has been rapidly growing for the past decade in China, making price forecasting an important issue to people and policy makers. We approach this problem by exploring neural networks of prices from one hundred major cities period June 2010–May 2019, serving as first study with such wide coverage emerging Chinese through a machine learning technique. aim at constructing simple accurate contribution pure technical prices. To facilitate analysis, we investigate different model...
The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity their alloying elements. Nearly 140 samples, containing elements Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, Sb, are explored for this purpose. modeling approach demonstrates high degree accuracy stability, contributing efficient low-cost estimations constants compounds.
Solid-state refrigeration techniques have drawn increasing attention due to their potential for improving the energy efficiency of and temperature-control systems without using harmful gas as in conventional compression techniques. Research on magnetocaloric lanthanum manganites with near-room-temperature Curie temperature shows promising results development magnetic devices. Chemical substitutions are one most effective methods tune effect, represented by maximum entropy change (MMEC),...
The GPR model (M2) is developed to elucidate the statistical relationship among ionic radii, electronegativities, oxidation states, and lattice constants for cubic A<sub>2</sub><sup>2+</sup>BB′O<sub>6</sub> perovskites. demonstrates a high degree of accuracy stability.
As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, are promising alternatives predictions of Tg in a fast and robust way. The molecular traceless quadrupole moment molecule average hexadecapole closely correlated with Tg. In the current work, these two parameters used as descriptors Gaussian process regression model predict We investigate 60...
Efficient and spectrally stable pure-red perovskite light-emitting diodes (PeLEDs) are still rare urgently needed for high-definition display. The traditional color tuning method by varying halide composition undergoes phase segregation has spectral instability issues. Instead of mixing, we fabricate PeLEDs based on quasi-two-dimensional (quasi-2D) perovskites simultaneously incorporating phenethylammonium (PEA) 1-naphthylmethylammonium (NMA) cations. control PEA NMA cospacer ratio modulates...