2015
DOI: 10.1016/j.saa.2015.01.126
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Laccase immobilization on the electrode surface to design a biosensor for the detection of phenolic compound such as catechol

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Cited by 82 publications
(48 citation statements)
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“…In order to evaluate the novelty and superiority of the biosensor, we made the comparison table for analytical performance of the as-prepared biosensor with previously reported laccase biosensors for CC and the comparative results are shown in Table 1. The comparative results clearly show that the as-prepared laccase biosensor exhibited a lower LOD (85 nM) towards CC than previously reported CC biosensors based on laccase immobilized on reduced graphene oxide supported palladium–copper alloyed nanocages42, nitrogen-doped ordered mesoporous/PVA matrix43, copper-containing ordered mesoporous carbon/chitosan matrix44, electrospun copper/carbon composite nanofibers45, 1-aminopyrene functionalized reduced graphene oxide46, carbon nanotubes–chitosan composite47, polyaniline48, multi-walled carbon nanotubes49 and ZnO sol-gel/chitosan modified electrodes50. However, the LOD of the as-fabricated CC biosensor is higher than the LOD (76 nM) of previously reported CC biosensor based on laccase immobilized reduced graphene oxide–glycol chitosan nanohybrid modified electrode, yet the sensitivity and linear response range of our biosensor is more comparable for the determination of CC.…”
Section: Resultsmentioning
confidence: 72%
“…In order to evaluate the novelty and superiority of the biosensor, we made the comparison table for analytical performance of the as-prepared biosensor with previously reported laccase biosensors for CC and the comparative results are shown in Table 1. The comparative results clearly show that the as-prepared laccase biosensor exhibited a lower LOD (85 nM) towards CC than previously reported CC biosensors based on laccase immobilized on reduced graphene oxide supported palladium–copper alloyed nanocages42, nitrogen-doped ordered mesoporous/PVA matrix43, copper-containing ordered mesoporous carbon/chitosan matrix44, electrospun copper/carbon composite nanofibers45, 1-aminopyrene functionalized reduced graphene oxide46, carbon nanotubes–chitosan composite47, polyaniline48, multi-walled carbon nanotubes49 and ZnO sol-gel/chitosan modified electrodes50. However, the LOD of the as-fabricated CC biosensor is higher than the LOD (76 nM) of previously reported CC biosensor based on laccase immobilized reduced graphene oxide–glycol chitosan nanohybrid modified electrode, yet the sensitivity and linear response range of our biosensor is more comparable for the determination of CC.…”
Section: Resultsmentioning
confidence: 72%
“…As an end result, several phenol bio-sensors based on laccase have been fabricated as they have high selectivity, convenience, reliability, and high sensitivity (Mei et al 2015). Various researchers have worked on the field for creating bio-sensors with the help of laccase for the detection of phenols (Cabaj et al 2011;Nazari et al 2015), pesticides, and laccase inhibitors (Mousty et al 2007). The combination of twoenzyme system has also been developed to determine the synergistic effect in the detection of the desired product or pollutant (Leite et al 2003;Medina-Plaza et al 2014).…”
Section: Bio-sensor-a New Approach Having Multiple Usagesmentioning
confidence: 99%
“…In recent years, laccase based biosensors are becoming relevant for areas like food analysis and environmental monitoring due to their properties such as fast response time, low-cost, low reagents consumption and the most important one, the possibility to use them in field. The ability of laccase to catalyse the one-electron oxidation of different polyphenols has been used to develop laccase based electrochemical biosensors for the determination of polyphenolic index in wines (García-Guzmán et al, 2015;Lanzellotto et al, 2014), in tea infusions (Cortina-Puig et al, 2010;Eremia et al, 2013) and tea leaves extracts (Rawal et al, 2012) as well as for the determination of environmental pollutants (Brondani et al, 2013;Nazari et al, 2015;Oliveira et al, 2013).…”
Section: Introductionmentioning
confidence: 99%