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Inverted Micelle‐in‐Micelle Configuration in Cationic/Carbohydrate Surfactant Mixtures

Overview of attention for article published in ChemPhysChem, November 2016
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Article details
Title
Inverted Micelle‐in‐Micelle Configuration in Cationic/Carbohydrate Surfactant Mixtures
Published in
ChemPhysChem, November 2016
DOI 10.1002/cphc.201600908
Pubmed ID
Authors
Abstract

Nuclear magnetic resonance is applied to investigate the relative positions and interactions between cationic and non-ionic carbohydrate-based surfactants in mixed micelles with D2O as the solvent.This is accomplished using relaxation measurements (spin-lattice (T1) and spin-spin (T2) analysis) and nuclear Overhauser effect spectroscopy (NOESY).This study focuses on the interactions of n-octyl β-D-glucopyranoside (C8G1) and β-D-xylopyranoside (C8X1) with cationic surfactant hexadecyltrimethylammonium bromide (C16TAB).While the interactions between carbohydrate and cationic surfactants are thermodynamically favorable, the NOESY results suggest that both of the sugar head groups are located preferentially at the interior core of the mixed micelles, so that they are not directly exposed to the bulk solution. The more hydrophilic sugar headgroups of C8G1 have more mobility than sugar heads of C8X1 due to increased hydration. Here for the first time an inverted carbohydrate configuration in mixed micelles is proposed and supported by fluorescence spectroscopy experiments.This inverted carbohydrate headgroup configuration would limit the use of these mixed surfactants when access to the carbohydrate headgroup is important, but may present new opportunities where the carbohydrate-rich core of the micelles can be exploited.

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Mendeley demographics

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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.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Professor 3 38%
Researcher 2 25%
Professor > Associate Professor 1 13%
Student > Doctoral Student 1 13%
Unknown 1 13%
Readers by discipline
Readers by discipline Count As %
Chemical Engineering 2 25%
Chemistry 2 25%
Environmental Science 1 13%
Materials Science 1 13%
Unknown 2 25%
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 11 November 2016.
All research outputs
#18,480,433
of 22,899,952 outputs
Outputs from ChemPhysChem
#2,696
of 4,826 outputs
Outputs of similar age
#235,356
of 310,683 outputs
Outputs of similar age from ChemPhysChem
#44
of 95 outputs
Altmetric has tracked 22,899,952 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 4,826 research outputs from this source. They receive a mean Attention Score of 2.2. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
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 310,683 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.