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Competitive Inhibition of the Enzyme-Mimic Activity of Gd-Based Nanorods toward Highly Specific Colorimetric Sensing of l‑Cysteine

Overview of attention for article published in Langmuir, September 2017
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Title
Competitive Inhibition of the Enzyme-Mimic Activity of Gd-Based Nanorods toward Highly Specific Colorimetric Sensing of l‑Cysteine
Published in
Langmuir, September 2017
DOI 10.1021/acs.langmuir.7b01926
Pubmed ID
Authors

Mandeep Singh, Pabudi Weerathunge, Piyumi Dinusha Liyanage, Edwin Mayes, Rajesh Ramanathan, Vipul Bansal

Abstract

Gd-based nanomaterials offer interesting magnetic properties, and have been heavily investigated for magnetic resonance imaging. The applicability of these materials beyond biomedical imaging remains limited. The current study explores the applicability of these rare-earth nanomaterials as nanozyme-mediated catalysts for colorimetric sensing of cysteine, an amino acid of high biomedical relevance. We show a facile solution-based strategy to synthesise two Gd-based nanomaterials viz. Gd(OH)3 and Gd2O3 nanorods. We further establish the catalytic peroxidase-mimic nanozyme activity of these Gd(OH)3 and Gd2O3 nanorods. This catalytic activity was suppressed specifically in the presence of L-cysteine that allowed us to develop a colorimetric sensor to detect this biologically relevant molecule among various other contaminants. This suppression, which could either be caused due to catalyst poisoning or enzyme inhibition, prompted extensive investigation of the kinetics of this catalytic inhibition in the presence of cysteine. This revealed a competitive inhibition process, a mechanism akin to those observed in natural enzymes, bringing nanozymes a step closer to the biological systems.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 15%
Student > Ph. D. Student 4 12%
Student > Bachelor 3 9%
Student > Doctoral Student 2 6%
Professor > Associate Professor 2 6%
Other 6 18%
Unknown 11 33%
Readers by discipline Count As %
Materials Science 7 21%
Chemistry 7 21%
Chemical Engineering 3 9%
Biochemistry, Genetics and Molecular Biology 3 9%
Nursing and Health Professions 1 3%
Other 2 6%
Unknown 10 30%
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 27 August 2017.
All research outputs
#20,444,703
of 22,999,744 outputs
Outputs from Langmuir
#12,666
of 14,027 outputs
Outputs of similar age
#275,650
of 315,613 outputs
Outputs of similar age from Langmuir
#121
of 183 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,027 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 1st percentile – i.e., 1% 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 315,613 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 183 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.