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Title: Development of a semigraphitic sulfur-doped ordered mesoporous carbon material for electroanalytical applications

Journal Article · · Sensors and Actuators. B, Chemical
 [1];  [1];  [2];  [2];  [3];  [2]
  1. Univ. of Sao Paulo (Brazil)
  2. Ames Lab., Ames, IA (United States)
  3. Univ. of Campinas (UNICAMP), Sao Paulo (Brazil)
+ Show Author Affiliations

The modification of traditional electrodes with mesoporous carbons is a promising strategy to produce high performance electrodes for electrochemical sensing. The high surface area of mesoporous carbons provides a large number of electroactive sites for binding analytes. Controlling the pore size and structure of mesoporous carbons and modifying their electronic properties via doping offers additional benefits like maximizing transport and tuning the electrochemical processes associated with analyte detection. This work reports a facile method to produce sulfur-doped ordered mesoporous carbon materials (S-OMC) with uniform pore structure, large pore volume, high surface area and semigraphitic structure. The synthesis used thiophenol as a single source of carbon and sulfur, and iron as a catalyst for low temperature carbonization. The S-OMC material was deposited on a glassy carbon electrode and used as a sensor with high sensitivity (11.7 A L mol-1) and selectivity for chloramphenicol detection in presence of other antibiotics. As a proof-of-concept, the sensor was applied to the direct analysis of the drug in reconstituted powdered milk and in commercial eye drops.

Research Organization:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-07CH11358
OSTI ID:
1413141
Alternate ID(s):
OSTI ID: 1496345
Report Number(s):
IS-J-9359; PII: S0925400517320786
Journal Information:
Sensors and Actuators. B, Chemical, Vol. 257, Issue C; Related Information: Also available at http://lib.dr.iastate.edu/ameslab_manuscripts/69/; ISSN 0925-4005
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 19 works
Citation information provided by
Web of Science

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Cited By (4)

Prediction of Carbon Dioxide Adsorption via Deep Learning journal December 2018
Highly Sensitive and Selective Dopamine Detection Utilizing Nitrogen‐Doped Mesoporous Carbon Prepared by a Molten Glucose‐Assisted Hard‐Template Approach journal June 2019
Prediction of Carbon Dioxide Adsorption via Deep Learning journal December 2018
Nitrogen-Dopped Ordered Mesoporous Carbon Anchored Pd Nanoparticles for Solvent Free Selective Oxidation of Benzyl Alcohol to Benzaldehyde by Using O2 journal June 2019

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36 MATERIALS SCIENCE