Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management

Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management.</jats:p

Improved atomic layer deposition method enhances the unit catalytic activity of platinum catalyst in oxygen reduction reaction

The construction of uniformly dispersed and low-loading platinum-containing catalysts is a challenging task to promote the oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) using highly efficient and relatively low-cost catalysts. Here, we propose a strategy to prepare active Pt catalysts with cluster structures by a simplified atomic layer deposition (ALD) method that does not require the incorporation of equipment such as a fluidized bed reactor to control the distribution of carriers in the reaction chamber. Unlike the conventional ALD methods, the deposition of precious metal Pt on carbon aerogel (CA) does not require O3 or O2 to participate in all reactions, and the platinum content is directly controlled by the ALD cycle method, and a series of Pt@CA materials have successfully synthesized catalysts with different platinum content by adjusting the pulse time, purge time and cycle times. Compared with commercial platinum carbon catalysts, these Pt@CA catalysts synthesized by the simplified ALD method have higher electrochemical active surface area (ECSA) and unit mass activity (MA) due to their excellent uniformity, dispersion, and high utilization of Pt active sites. This study, which focuses on atomic-scale strategies for Pt-containing materials, marks a step towards modern high-performance catalysts for future fuel cell technologies

Synthesis of micron-particle-size HZSM-5 zeolite with different Si/Al ratio and its catalytic activity for toluene methanol alkylation reaction

HZSM-5 molecular sieve is one of the commonly used catalysts for the alkylation of toluene with methanol to para-xylene (PX). This study introduces a one-step method to directly prepare microscale HZSM-5 catalysts with suitable pore structure and acidity. Specifically, this paper combined crystal seed regulation of the morphology of HZSM-5 and adjusted the Si/Al ratios of the precursor solution for HZSM-5 synthesis to regulate the acidic phase of the HZSM-5. It analyzed the phase composition, pore structure parameters, microstructure, and acidity of HZSM-5 molecular sieve through X-ray diffraction, N2 adsorption desorption, SEM, NH3 temperature programmed desorption, pyridine infrared spectroscopy, etc. This study investigated the catalytic performance of HZSM-5 in the alkylation of toluene with methanol to PX, and explored the effects of pore structure and acidity on the catalytic performance. Results indicate that in the hydrothermal crystallization method, the crystal seeds and aluminum content in the synthesis solution have a significant impact on the acidity and morphology structure of the HZSM-5 molecular sieve. The HZSM-5 (150)molecular sieve catalyst with 5μm grain size exhibits 60% PX selectivity and 11% toluene conversion rate in the evaluation reaction of toluene methanol alkylation due to its longer diffusion path and more suitable acidic sites. By fine-tuning the pores and acidic sites of HZSM-5 molecular sieve, the selectivity for PX has been effectively improved, providing new methods and approaches for the design of shape-selective catalysts

Synthesis and characterization of cellulose triacetate aerogels with ultralow densities

A novel method that needs only dissolution, gelation and ScCO2drying has been developed to fabricate ultralow density, nanostructured TAC aerogels.</p