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1.
ACS Appl Mater Interfaces ; 16(17): 21924-21931, 2024 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-38647706

RESUMO

The solid-state battery with a lithium metal anode is a promising candidate for next-generation batteries with improved energy density and safety. However, the current polymer electrolytes still cannot fulfill the demands of solid-state batteries. In this work, we propose a "5H" poly(ethylene oxide) (PEO) electrolyte via introducing a multifunctional additive of tris(pentafluorophenyl)borane (TPFPB) for high-performance lithium metal batteries. The addition of TPFPB improves the ionic conductivity from 6.08 × 10-5 to 1.54 × 10-4 S cm-1 via reducing the crystallinity of the PEO electrolyte and enhances the lithium-ion transference number from 0.19 to 0.53 via anion trapping due to its Lewis acid nature. Furthermore, the fluorine and boron segments from TPFPB can optimize the composition of the solid-electrolyte interphase and cathode-electrolyte interphase, providing a high electrochemical stability window over 4.6 V of the PEO electrolyte along with significantly improved interface stability. At last, TPFPB can ensure improved safety through a self-extinguishing effect. As a result, the "5H" electrolyte enables the Li/Li symmetric cells to achieve a stable cycle over 2200 h at the current density of 0.2 mA cm-2 with a capacity of 0.2 mA h cm-2; the LiFePO4/Li full cells with a high LFP loading of 8 mg cm-2 exhibits decay-free capacity of 140 mA h g-1 (99% capacity retention) after 100 cycles; and the NCM811/Li cells exhibit a high capacity of 160 mA h g-1 after 50 cycles at 0.5 C. This work presents an innovative approach to utilizing a "5H" electrolyte for high-performance solid-state lithium batteries.

2.
ACS Appl Mater Interfaces ; 11(33): 29830-29837, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31361114

RESUMO

The conventional lithium-sulfur battery (LSB) undergoes a "solid-liquid-solid" cathode process during which the intermediate polysulfides dissolve into the electrolyte, leading to a serious "shuttle" reaction and significantly shortened lifespan. Here, we realize a novel "solid → solid" cathode mode for LSBs via a transplantable solid electrolyte interface (SEI). The SEI is in situ formed in a carbonate-based electrolyte with high-concentration dual-salt during the initial discharge process. The solid → solid cathode process does not involve any dissolution of the intermediates; hence, the "shuttle effect" can be totally eliminated. Furthermore, the SEI shows a high electrolyte compatibility and can be transplanted to the conventional carbonate-based/ether-based electrolytes. The sulfur/carbon composite with 65% sulfur delivers a reversible specific capacity of 1009 mA h g-1 and negligible self-discharge. The SEI strategy can successfully break the limitation from the traditional "catholyte" electrode mechanism. Meanwhile, it provides large flexibility for designing high-loading carbon hosts and selecting an electrolyte for high-performance LSBs.

3.
Comput Intell Neurosci ; 2018: 6791683, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30405708

RESUMO

Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues previously solved by principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art variant of KECA and can return projections retaining more expressive power than KECA. However, OKECA is sensitive to outliers and accused of its high computational complexities due to its inherent properties of L2-norm. To handle these two problems, we develop a new extension to KECA, namely, KECA-L1, for DR or feature extraction. KECA-L1 aims to find a more robust kernel decomposition matrix such that the extracted features retain information potential as much as possible, which is measured by L1-norm. Accordingly, we design a nongreedy iterative algorithm which has much faster convergence than OKECA's. Moreover, a general semisupervised classifier is developed for KECA-based methods and employed into the data classification. Extensive experiments on data classification and software defect prediction demonstrate that our new method is superior to most existing KECA- and PCA-based approaches. Code has been also made publicly available.


Assuntos
Algoritmos , Reconhecimento Automatizado de Padrão/métodos , Neoplasias da Mama/diagnóstico , Diabetes Mellitus/diagnóstico , Entropia , Humanos , Processamento de Imagem Assistida por Computador/métodos , Software , Vinho/análise
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