ArticleScientific reports2022
Simultaneous electrochemical determination of morphine and methadone by using CMK-5 mesoporous carbon and multivariate calibration.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- Facile construction of 3D pyramidal Cu nanoparticles on indium tin oxide: a simple and sensitive platform for methadone monitoring in biological samples.Mikrochimica acta · 2026Article
- Poly-taurine/poly-L-glutamic acid double-layer coating as potential candidates for surface modification of carbon felt electrode for discrimination and simultaneous detection of morphine and tramadol.Mikrochimica acta · 2025Article
- Article
- Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.Scientific reports · 2024Article
- Unveiling Morphine: A Rapid and Selective Fluorescence Sensor for Forensic and Medical Analysis.Sensors (Basel, Switzerland) · 2024Article
- Synthesis and characterization of nanocatalyst CuScientific reports · 2023Article
- Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For the first time, a sensitive electrochemical sensor using a glassy carbon electrode modified with CMK-5 Ordered mesoporous carbon was fabricated for simultaneous analysis of morphine and methadone. Modern electrochemical FFT-SWV techniques and partial least-squares as a multivariable analysis were used in this method. CMK-5 nanostructures were characterized by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction analysis, and Raman spectroscopy. Variables such as accumulation time and pH for the proposed sensor were optimized before quantitative analysis. To train the proposed sensor, standard mixtures of morphine (MOR), and methadone (MET) were prepared in the established linear ranges of the analyzes. The results obtained from training samples were used for PLS modeling. The efficiency of the model was determined using test and real matrix samples. The root mean square error of prediction and the squared correlation coefficients (R
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Registered trials
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