An optical sensor for the detection and quantification of lidocaine in cocaine samples

A novel miniaturized potentiometric electrode based on carbon nanotubes and molecularly imprinted polymer for the determination of lidocaine

A novel miniaturized, solid-contact potentiometric screen-printed electrode was developed for highly sensitive and selective determination of lidocaine anesthetic. The electrode integrated single-walled carbon nanotubes as a solid-contact material and a molecularly imprinted polymer as a recognition sensory material. The performance characteristics of the electrode were evaluated and optimized to display a Nernstian slope of 58.92 ± 0.98 mV/decade over a linear concentration range of 4.53 × 10-7 to 6.18 × 10-3 mol/l within < 6 s. The detection limit was 7.75 × 10-8 mol/l (18.16 ng/ml) of lidocaine. The use of the molecularly imprinted polymer significantly enhanced the selectivity of the electrode, and carbon nanotubes increased the sensitivity, accuracy, and potential stability. The electrode was successfully used for determining lidocaine in pharmaceutical preparations and human urine. The results favorably compared with data obtained by liquid chromatography-tandem mass spectrometry.

Printed graphene-based electrochemical sensor with integrated paper microfluidics for rapid lidocaine detection in blood

Topical lidocaine patches are commonly used to relieve pain and suffering in various clinical and household settings. Despite its extensive use, excessive skin absorption during numbing or pain reduction procedures can cause systemic toxicity, which can lead to life-threatening conditions. Rapid and reliable monitoring of escalating levels of lidocaine in the blood could help management/prevention of lidocaine overdose and its associated complications. To address this need, here we have developed a disposable point-of-care (POC) diagnostic platform composed of an integrated graphene-based electrochemical sensor with paper-based microfluidics for rapid detection of lidocaine levels in serum and blood samples. The fabrication process takes advantage of advanced, scalable manufacturing techniques, including printing, laser processing, and nondestructive near infrared (NIR) drying. The sensitivity tests of the platform revealed a sensitivity of ∼0.2 μA μM^-1 towards lidocaine concentrations in the clinically relevant range (1-100 μM) in both complex matrix fluids of serum and blood with high cross specificity in the presence of the interfering analytes. This proof-of-concept platform could be regarded as the first step toward the development of low-cost and translational POC devices that could help in better pain management and reduce potential side effects or misuse of analgesics.

Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts

A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balanced (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5 min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng L^-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.