2021
DOI: 10.1016/j.est.2021.103012
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Machine learning technique-based data-driven model of exploring effects of electrolyte additives on LiNi0.6Mn0.2Co0.2O2/graphite cell

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Cited by 3 publications
(2 citation statements)
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“…Meanwhile, the baseline only decreases by 32.9% compared to the first cycle. These results could be explained by the porous and amorphous structure of SiO 2 /C facilitated a considerable amount of Li + ions from the NMC cathode to intercalate by shortening the Li + ion diffusion pathway, resulting in the significant charging capacity 25 . However, the superior specific surface area of SiO 2 /C material reacted strongly with electrolyte to consume Li + ions for SEI layer formation, which indicated “active lithium loss.” 26 Hence the discharged capacity of the baseline is always lower than the 100GR.…”
Section: Resultsmentioning
confidence: 99%
“…Meanwhile, the baseline only decreases by 32.9% compared to the first cycle. These results could be explained by the porous and amorphous structure of SiO 2 /C facilitated a considerable amount of Li + ions from the NMC cathode to intercalate by shortening the Li + ion diffusion pathway, resulting in the significant charging capacity 25 . However, the superior specific surface area of SiO 2 /C material reacted strongly with electrolyte to consume Li + ions for SEI layer formation, which indicated “active lithium loss.” 26 Hence the discharged capacity of the baseline is always lower than the 100GR.…”
Section: Resultsmentioning
confidence: 99%
“…Such reports exist for LIB electrodes like NMC622 cathode, where they have determined the impact of additives on cell performance with respect to FEC, VC, and lithium bis(oxalate) borate (LiBOB) additives in EMC solvent. [111]…”
Section: Chemsuschemmentioning
confidence: 99%