ArticleACS sensors2025
Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.
Article in ACS sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence in Electrochemical Sensing: A Network Evidence Map of Translational Barriers and Pathways to Point-of-Care Deployment.Biosensors · 2026Review
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Machine Learning-Powered Smart Sensing of Copper Ions in Water Based on a Carbon Dot-Incorporated Hydrogel Platform: An Easy Path from Bench to Onsite Detection.Sensors (Basel, Switzerland) · 2026Article
- Cu nanoparticles regulated by sodium lignosulfonate-CuMikrochimica acta · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By utilizing the synergistic effects of a dual-metal cobalt@copper electrode and advanced machine learning algorithms, we have developed a reliable and cost-effective electrochemical sensor for creatinine monitoring. The sensor's active surface was fabricated through the sequential electrodeposition of copper and cobalt nanoparticles, with their complexation with creatinine confirmed via cyclic voltammetry and spectroelectrochemical analyses. The combined contributions of both transition metals significantly enhanced the sensor's sensitivity and selectivity, yielding a linear detection range of 0.00-4.00 mM, a sensitivity of 6.06 ± 0.65 μA mM
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Registered trials
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