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Generating carbon schwarzites via zeolite-templating

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, August 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
11 news outlets
blogs
1 blog
twitter
15 tweeters
facebook
3 Facebook pages
googleplus
2 Google+ users
video
1 video uploader

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
100 Mendeley
Title
Generating carbon schwarzites via zeolite-templating
Published in
Proceedings of the National Academy of Sciences of the United States of America, August 2018
DOI 10.1073/pnas.1805062115
Pubmed ID
Authors

Efrem Braun, Yongjin Lee, Seyed Mohamad Moosavi, Senja Barthel, Rocio Mercado, Igor A. Baburin, Davide M. Proserpio, Berend Smit

Abstract

Zeolite-templated carbons (ZTCs) comprise a relatively recent material class synthesized via the chemical vapor deposition of a carbon-containing precursor on a zeolite template, followed by the removal of the template. We have developed a theoretical framework to generate a ZTC model from any given zeolite structure, which we show can successfully predict the structure of known ZTCs. We use our method to generate a library of ZTCs from all known zeolites, to establish criteria for which zeolites can produce experimentally accessible ZTCs, and to identify over 10 ZTCs that have never before been synthesized. We show that ZTCs partition space into two disjoint labyrinths that can be described by a pair of interpenetrating nets. Since such a pair of nets also describes a triply periodic minimal surface (TPMS), our results establish the relationship between ZTCs and schwarzites-carbon materials with negative Gaussian curvature that resemble TPMSs-linking the research topics and demonstrating that schwarzites should no longer be thought of as purely hypothetical materials.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 27%
Researcher 19 19%
Student > Bachelor 10 10%
Student > Master 10 10%
Student > Doctoral Student 7 7%
Other 15 15%
Unknown 12 12%
Readers by discipline Count As %
Chemistry 30 30%
Materials Science 17 17%
Engineering 11 11%
Physics and Astronomy 7 7%
Chemical Engineering 7 7%
Other 13 13%
Unknown 15 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 101. 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 11 March 2019.
All research outputs
#266,199
of 18,812,015 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#5,554
of 91,795 outputs
Outputs of similar age
#7,523
of 289,163 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#131
of 904 outputs
Altmetric has tracked 18,812,015 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 91,795 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 32.5. This one has done particularly well, scoring higher than 93% 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 289,163 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 904 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.