Publications by authors named "Junfei Cai"

4 Publications

  • Page 1 of 1

A novel free-standing metal organic frameworks-derived cobalt sulfide polyhedron array for shuttle effect suppressive lithium-sulfur batteries.

Nanotechnology 2021 Nov 24. Epub 2021 Nov 24.

Chinese Academy of Sciences - Intelligent Machines Institute, Science Island 1130 M B, Hefei 230026, Hefei, 230031, CHINA.

Metal-organic-foams (MOFs)-derived nanostructures have received broad attention for secondary batteries. However, common strategies are focusing on the preparation of dispersive materials, which need complicated steps and some additives for making electrodes of batteries. Here, we develop a novel free-standing Co9S8 polyhedron array derived from ZIF-67, which grows on a three-dimensional carbon cloth for lithium-sulfur (Li-S) battery. The polar Co9S8 provides strong chemical binding to immobilize polysulfides, which enables efficiently suppressing of the shuttle effect. The free-standing [email protected] polyhedron array-based cathode exhibits ultrahigh capacity of 1079 mAh g-1 after cycling 100 times at 0.1C, and long cycling life of 500 cycles at 1C, recoverable rate-performance and good temperature tolerance. Furthermore, the adsorption energies towards polysulfides are investigated by using density functional theory (DFT) calculations, which display a strong binding with polysulfides.
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http://dx.doi.org/10.1088/1361-6528/ac3ce5DOI Listing
November 2021

Sinoacutine inhibits inflammatory responses to attenuates acute lung injury by regulating NF-κB and JNK signaling pathways.

BMC Complement Med Ther 2021 Nov 20;21(1):284. Epub 2021 Nov 20.

School of Chinese Material Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China.

Background: Stephania yunnanensis H. S. Lo is widely used as an antipyretic, analgesic and anti-inflammatory herbal medicine in SouthWest China. In this study, we investigated the anti-inflammatory activity and mechanism of sinoacutine (sino), one of the primary components extracted from this plant.

Methods: A RAW264.7 cell model was established using lipopolysaccharide (LPS) induced for estimation of cytokines in vitro, qPCR was used to estimate gene expression, western blot analysis was used to estimate protein level and investigate the regulation of NF- κB, JNK and MAPK signal pathway. In addition, an acute lung injury model was established to determine lung index and levels of influencing factors.

Results: Using the RAW264.7 model, we found that sino reduced levels of nitric oxide (NO), tumour necrosis factor-α (TNF-α), interleukin (IL)-1β and prostaglandin E (PGE) but increased levels of IL-6. qPCR analysis revealed that sino (50, 25 μg/ml) inhibited gene expression of nitric oxide synthase (iNOS). western blot analysis showed that sino significantly inhibited protein levels of both iNOS and COX-2. Further signalling pathway analysis validated that sino also inhibited phosphorylation of p65 in the NF-κB and c-Jun NH2 terminal kinase (JNK) signalling pathways but promoted the phosphorylation of extracellular signal regulated kinase (ERK) and p38 in the MAPK signalling pathway. In addition, in a mouse model induced by LPS, we determined that sino reduced the lung index and the levels of myeloperoxidase (MPO), NO, IL-6 and TNF-α in lung tissues and bronchoalveolar lavage fluid (BALF) in acute lung injury (ALI).

Conclusion: Taken together, our results demonstrate that sino is a promising drug to alleviate LPS-induced inflammatory reactions.
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http://dx.doi.org/10.1186/s12906-021-03458-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605577PMC
November 2021

Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery.

ACS Appl Mater Interfaces 2021 Nov 19;13(45):53388-53397. Epub 2021 Aug 19.

Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China.

The shuttle effect has been a major obstacle to the development of lithium-sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB-type sulfur host material to suppress the shuttle effect using the database, discovering 14 new structures (PdN, TaS, PtN, TaSe, AgCl, NbSe, TaTe, AgF, NiN, AuS, TmI, NbTe, NiBi, and AuBr) from 1320 AB-type compounds. These structures have strong adsorptions of greater than 1.0 eV for lithium polysulfides and appreciable electron-transportation capability, which can serve as the most promising AB-type host materials in lithium-sulfur batteries. On the basis of a small data set, we successfully predicted LiS adsorption at arbitrary sites on substrate materials using transfer learning, with a considerably low mean absolute error (below 0.05 eV). The proposed data-driven method, as accurate as density functional theory calculations, significantly shortens the research cycle of screening AB-type sulfur host materials by approximately 8 years. This method provides high-precision and expeditious solutions for other high-throughput calculations and material screenings based on adsorption energy predictions.
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http://dx.doi.org/10.1021/acsami.1c10749DOI Listing
November 2021

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression.

Sci Rep 2021 06 8;11(1):12112. Epub 2021 Jun 8.

National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China.

State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data.
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http://dx.doi.org/10.1038/s41598-021-91241-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187390PMC
June 2021
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