↓ Skip to main content

A new method for the determination of peak distribution across a two-dimensional separation space for the identification of optimal column combinations

Overview of attention for article published in Analytical & Bioanalytical Chemistry, September 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
twitter
1 X user

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
32 Mendeley
citeulike
1 CiteULike
Title
A new method for the determination of peak distribution across a two-dimensional separation space for the identification of optimal column combinations
Published in
Analytical & Bioanalytical Chemistry, September 2016
DOI 10.1007/s00216-016-9911-3
Pubmed ID
Authors

Juri Leonhardt, Thorsten Teutenberg, Greta Buschmann, Oliver Gassner, Torsten C. Schmidt

Abstract

For the identification of the optimal column combinations, a comparative orthogonality study of single columns and columns coupled in series for the first dimension of a microscale two-dimensional liquid chromatographic approach was performed. In total, eight columns or column combinations were chosen. For the assessment of the optimal column combination, the orthogonality value as well as the peak distributions across the first and second dimension was used. In total, three different methods of orthogonality calculation, namely the Convex Hull, Bin Counting, and Asterisk methods, were compared. Unfortunately, the first two methods do not provide any information of peak distribution. The third method provides this important information, but is not optimal when only a limited number of components are used for method development. Therefore, a new concept for peak distribution assessment across the separation space of two-dimensional chromatographic systems and clustering detection was developed. It could be shown that the Bin Counting method in combination with additionally calculated histograms for the respective dimensions is well suited for the evaluation of orthogonality and peak clustering. The newly developed method could be used generally in the assessment of 2D separations. Graphical Abstract ᅟ.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 25%
Student > Master 6 19%
Researcher 3 9%
Professor 2 6%
Other 2 6%
Other 3 9%
Unknown 8 25%
Readers by discipline Count As %
Chemistry 18 56%
Biochemistry, Genetics and Molecular Biology 2 6%
Agricultural and Biological Sciences 1 3%
Unknown 11 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 27 February 2018.
All research outputs
#2,174,550
of 25,374,647 outputs
Outputs from Analytical & Bioanalytical Chemistry
#132
of 9,619 outputs
Outputs of similar age
#36,481
of 330,899 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#4
of 200 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,619 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 98% 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 330,899 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.