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您的检索式：作者名="Guopeng Tu"
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| 1 | Vacuum-tuned-atmosphere induced assembly of Au@Ag core/shell nanocubes into multi-dimensional superstructures and the ultrasensitive IAPP proteins SERS detection显示文摘Utilizing vacuum-tuned-atmosphere induced dip coating method,we achieve the cross-dimensional macroscopic diverse self-assemblies by using one building block with one chemical functionality.Coordinated modulating the vacuum degree,colloid concentration and evaporation atmosphere,Au@Ag core/shell nanocubes (NCs) can controllably assemble into diverse multi-dimensional superstructures.Under 0.08 MPa,we obtained the two-dimensional (2D) stepped superstructures with continuously tunable step width.In addition,we generated a series of tailorable nanoscale-roughened 2D Au@Ag NCs superstructures at 0.04 MPa,which exhibited the label-free ultrasensitive SERS detection for the different mutants of IAPP8-37 proteins.Under 0.01 MPa,we obtained the cross-dimensional tailorable Au@Ag NCs assemblies from random to macroscale 2D and three-dimensional (3D) densest superstructures by adjusting the capping ligand-environmental molecule interactions.This is a flexible method to generate as-prepared Au@Ag core/shell NCs into well-defined macroscopic diverse superstructures and to promote the exploitation into biological applications. | Meng Xu Guopeng Tu Muwei Ji Xiaodong Wan Jiajia Liu Jia Liu Hongpan Rong Yanlian Yang Chen Wang Jiatao Zhang | 2019 | Nano Research2019，12，6： | 3 |
| 2 | An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM显示文摘New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage. | Bingjie Yan Jun Wang Zhen Zhang Xiangyan Tang Yize Zhou Guopeng Zheng Qi Zou Yao Lu Boyi Liu Wenxuan Tu Neal Xiong | 2020 | Computers, Materials & Continua2020，，9： | 2 |
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