Highly Sensitive and Selective Detection of the Antidepressant Amitriptyline Using a Functionalised Graphene‐Based Sensor
Source abstract
Abstract
Abstract The accurate measurement of drug concentration in patient samples is vital and is often conducted using point of care devices for therapeutic drug monitoring and other clinical activities. Two dimensional (2D) nanomaterials provide an opportunity to develop miniaturized, affordable and accurate drug sensors, with the promise of developing point of care tests, which are more convenient compared to current techniques that often require advanced instrumentation and laboratory tests. Here we present a reduced graphene oxide‐based electrochemical sensor that permits precise measurements of amitriptyline, a well‐known anti‐depressant. The sensor uses functionalised graphene where amitriptyline selective receptors directly attached to the carbon atoms on the surface of the graphene through diels‐alder reaction. The simplicity of the functionalisation procedure along with a tunable receptor used in this study implies that the same method can be used for developing similar selective sensors, simply by changing the small functional groups attached to the receptor molecule. The electrochemical characteristics of the functionalised graphene were studied with cyclic voltammetry, where the sensor demonstrated excellent selectivity, responding only to amitriptyline when tested against closely related tricyclic antidepressants and a range of other psychoactive drugs. The sensor displayed a wider linear range from 1 ng/mL to 80 μg/mL, which covers the concentration range for amitriptyline studied in therapeutic, pharmaceutical, and forensic investigations. The limit of detection of the developed sensor was 1 ng/ml which competes with the detection limit of gas chromatography – mass spectrometry investigated in this study.
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Research context
OpenAlex topics: Electrochemical sensors and biosensors · Electrochemical Analysis and Applications · Pharmacological Effects and Toxicity Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Science and Technology Bournemouth University Talbot Campus, Fern Barrow BH12 5BB Poole UK
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