Wulin Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Wulin Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Changsong Zhou
Nanjing Normal University
1 shared publication - Hao Wu
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
1 shared publication - Hongmin Yang
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
1 shared publication - Jing Zhou
School of Computer Science and Engineering, Nanyang Technological University, Singapore 637141, Singapore
1 shared publication - Qiangqiang Ren
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
1 shared publication - Renhua Huang
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
1 shared publication
Latest Publications
- Open Access
Analysis and Joint Correlation Models on Pore Structures of Graphitized Phenolic Resin Char Under Ni-Zn-B Catalytic Effect
Phenolic resin plastic is mainly composed of phenolic resin and pyrolysis is often used to perform the important task of treating it. While there are large quantities of char generating, the char can be graphitized for upgrading under Ni-Zn-B catalytic effect. Pore structure is an important index for evaluating graphitic carbon. In this study, the phenolic resin char was graphitized with Ni-Zn-B at low temperature based on orthogonal rules (graphitization temperature: 1300 °C, 1400 °C, 1500 °C; retention time: 60 min, 120 min, 180 min; catalyst additive ratio: 5%, 10%, 15%), and their pore structures were determined by N2 adsorption and desorption method. The graphitized phenolic resin chars were porous carbon materials whose specific surface areas were commonly between 110 to 160 m2/g; they also mainly consisted of wholly equivalent micropores and mesopores. The effects of the graphitization conditions on pore structures of GPRCs were analyzed; this revealed that the increase in graphitization temperature destroyed micropores to form mesopores, with a longer retention time leading to the production of small quantities of micropores and mesopores, some micropore spaces were occupied and mesopore skeletons were destroyed to from large pores with more Ni-Zn-B addition. The correlation models of the pore structures and reaction parameters were built; it was found that the multiple linear regression model showed an advantage in predicting micropore structures and the built artificial neural network model was better at predicting the total pore and mesopore.
Open AccessAnalysis and Joint Correlation Models on Pore Structures of Graphitized Phenolic Resin Char Under Ni-Zn-B Catalytic Effect
Phenolic resin plastic is mainly composed of phenolic resin and pyrolysis is often used to perform the important task of treating it. While there are large quantities of char generating, the char can be graphitized for upgrading under Ni-Zn-B catalytic effect. Pore structure is an important index for evaluating graphitic carbon. In this study, the phenolic resin char was graphitized with Ni-Zn-B at low temperature based on orthogonal rules (graphitization temperature: 1300 °C, 1400 °C, 1500 °C; retention time: 60 min, 120 min, 180 min; catalyst additive ratio: 5%, 10%, 15%), and their pore structures were determined by N2 adsorption and desorption method. The graphitized phenolic resin chars were porous carbon materials whose specific surface areas were commonly between 110 to 160 m2/g; they also mainly consisted of wholly equivalent micropores and mesopores. The effects of the graphitization conditions on pore structures of GPRCs were analyzed; this revealed that the increase in graphitization temperature destroyed micropores to form mesopores, with a longer retention time leading to the production of small quantities of micropores and mesopores, some micropore spaces were occupied and mesopore skeletons were destroyed to from large pores with more Ni-Zn-B addition. The correlation models of the pore structures and reaction parameters were built; it was found that the multiple linear regression model showed an advantage in predicting micropore structures and the built artificial neural network model was better at predicting the total pore and mesopore.