Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 

Saved Queries

Sign in to use this feature.

Search Filter Reset All

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
Add Subjects Reset
Select Subjects

Journals

remove_circle_outline
remove_circle_outline
Add Journals Reset
Select Journals

Article Types

remove_circle_outline
Add Article Types Reset
Select Article Types

Countries / Regions

remove_circle_outline
Add Countries / Regions Reset
Select Countries / Regions
Update Search
share announcement

Search Results (2)

Search Parameters:
Authors = Penglei Gao

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
12 pages, 11808 KiB  
Article
Multi-Responsive Sensor Based on Porous Hydrogen-Bonded Organic Frameworks for Selective Sensing of Ions and Dopamine Molecules
by Faqiang Chen, Hui Xu, Youlie Cai, Wei Zhang, Penglei Shen, Wenhua Zhang, Hangqing Xie, Gongxun Bai, Shiqing Xu and Junkuo Gao
Molecules 2022, 27(24), 8750; https://doi.org/10.3390/molecules27248750 - 9 Dec 2022
Cited by 10 | Viewed by 2482
Abstract
Hydrogen-bonded organic frameworks (HOFs), as an emerging porous material, have attracted increasing research interest in fluorescence sensing due to their inherent fluorescence emission units with unique physicochemical properties. Herein, based on the organic building block 3,3′,5,5′-tetrakis-(4-carboxyphenyl)-1,1′-biphenyl (H4TCBP), the porous material HOF-TCBP [...] Read more.
Hydrogen-bonded organic frameworks (HOFs), as an emerging porous material, have attracted increasing research interest in fluorescence sensing due to their inherent fluorescence emission units with unique physicochemical properties. Herein, based on the organic building block 3,3′,5,5′-tetrakis-(4-carboxyphenyl)-1,1′-biphenyl (H4TCBP), the porous material HOF-TCBP was successfully synthesized using hydrogen bond self-assembly in a DMF solution. The fluorescence properties of the HOF-TCBP solution showed that when the concentration was high, excimers were easily formed, the PL emission was red-shifted, and the fluorescence intensity became weaker. HOF-TCBP showed good sensitivity and selectivity to metal ions Fe3+, Cr3+, and anion Cr2O72−. In addition, HOF-TCBP can serve as a label-free fluorescent sensor material for the sensitive and selective detection of dopamine (DA). HOF-based DA sensing is actually easy, low-cost, simple to operate, and highly selective for many potential interfering substances, and it has been successfully applied to the detection of DA in biological samples with satisfactory recoveries (101.1–104.9%). To our knowledge, this is the first report of HOF materials for efficient detection of the neurotransmitter dopamine in biological fluids. In short, this work widely broadens the application of HOF materials as fluorescent sensors for the sensing of ions and biological disease markers. Full article
(This article belongs to the Special Issue Multifunctional Metal-Organic Framework Materials)
►▼ Show Figures

Figure 1

16 pages, 760 KiB  
Article
The Application of Stock Index Price Prediction with Neural Network
by Penglei Gao, Rui Zhang and Xi Yang
Math. Comput. Appl. 2020, 25(3), 53; https://doi.org/10.3390/mca25030053 - 18 Aug 2020
Cited by 53 | Viewed by 12381
Abstract
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we [...] Read more.
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones. Full article
(This article belongs to the Special Issue Intelligent Computation and Its Applications in Financial Technology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 1-50 on page 1 of 1.
Back to TopTop