Yingying Chen

1 month ago | onlinelibrary.wiley.com | Yingying Chen |Sun Yat-sen Guangzhou |Hui Li |Yongmei Zhang

Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description imm13946-sup-0001-Supinfo.docxWord 2007 document , 1.8 MB Figure S1. BCG abolishes the immunosuppressive function of M-MDSCs. C57BL/6 mice were injected subcutaneously 3–4 days after birth with BCG in a single dose of 5 × 105 CFU, or with PBS as a control. Mice were sacrificed and cells in the spleen were analysed at day 3 after BCG vaccination.

Oct 7, 2024 | nature.com | Yingying Chen |CHEN CHEN |Seongon Jang |Paul Braun |Jianjun Cheng

Correction to: Nature Materials https://doi.org/10.1038/s41563-024-01966-1, published online 6 August 2024. In the version of the article initially published, in Fig. 2a and d, the y-axis scale bars originally read “2.0” and have now been amended to “0.2” in the HTML and PDF versions of the article. About this articleChen, Y., Xue, T., Chen, C. et al. Publisher Correction: Helical peptide structure improves conductivity and stability of solid electrolytes. Nat. Mater. (2024).

Sep 2, 2024 | mdpi.com | Jing Juan Liu |Yingying Chen |Kai Huang

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Aug 6, 2024 | nature.com | Yingying Chen |Chen Chen |Seongon Jang |Paul Braun |Jianjun Cheng

AbstractIon transport is essential to energy storage, cellular signalling and desalination. Polymers have been explored for decades as solid-state electrolytes by either adding salt to polar polymers or tethering ions to the backbone to create less flammable and more robust systems. New design paradigms are needed to advance the performance of solid polymer electrolytes beyond conventional systems.

Jun 26, 2024 | pubs.rsc.org | Yihang Li |Xinying Zhang |Tao Li |Yingying Chen

Accelerating Materials Discovery for Electrocatalytic Water Oxidation via Center-Environment Deep Learning in Spinel Oxides Identifying efficient electrocatalysts for oxygen evolution reaction (OER) is vital for sustainable energy. This study focuses on efficient spinel OER electrocatalysts. We utilize a covalent competition-based interpretable machine learning process, combining DFT and ML, to evaluate 5329 spinel structures for identifying promising OER electrocatalysts.