Discriminative sensing of DOPA enantiomers by cyclodextrin anchored graphene nanohybrids
Abstract
Discriminative sensing of chiral species with a convenient and robust system is a challenge in chemistry, pharmaceutics and particularly in biomedical science. Advanced nanohybrid materials for discrimination of these biologically active molecules can be developed by combination of individual obvious advantages of different molecular scaffolds. Herein, we report on the comparison of the performance of cyclodextrin functionalized graphene derivatives (x-CD/rGO, x: α-, β-, γ-) for discrimination of DOPA enantiomers. Within this respect, electrochemical measurements were conducted and the experimental results were compared to molecular docking method. Thanks to cavity size of γ-CD and the unique properties of graphene, rGO/γ-CD nanohybrid is capable of selective recognition of DOPA enantiomers. Limit of detection (LOD) value and sensitivity were determined as 15.9 μM and 0.2525 μA μM-1 for D-DOPA, and 14.9 μM and 0.6894 μA μM-1 for L-DOPA.
How strong is the evidence?
Evidence Hub reads this as a experimental / empirical study, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.