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A two-stage deep learning framework for early-stage lifetime prediction for lithium-ion batteries with consideration of features from multiple cycles

Overview of attention for article published in Frontiers in Energy Research, November 2022
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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2 X users

Citations

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4 Dimensions

Readers on

mendeley
8 Mendeley
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Title
A two-stage deep learning framework for early-stage lifetime prediction for lithium-ion batteries with consideration of features from multiple cycles
Published in
Frontiers in Energy Research, November 2022
DOI 10.3389/fenrg.2022.1059126
Authors

Jiwei Yao, Kody Powell, Tao Gao

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Unspecified 1 13%
Unknown 4 50%
Readers by discipline Count As %
Chemical Engineering 1 13%
Unspecified 1 13%
Engineering 1 13%
Unknown 5 63%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2022.
All research outputs
#18,751,750
of 23,243,271 outputs
Outputs from Frontiers in Energy Research
#764
of 3,418 outputs
Outputs of similar age
#306,882
of 444,753 outputs
Outputs of similar age from Frontiers in Energy Research
#18
of 161 outputs
Altmetric has tracked 23,243,271 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,418 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 444,753 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.