Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications.

ACS materials Au·2026
Same author

Structure-function correlations in graphene screen-printed electrodes: capacitive and faradaic behaviour.

Physical chemistry chemical physics : PCCP·2026
Same author

Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.

Digital health·2025
Same author

Controlling electrochemical lignin depolymerization <i>via</i> halide chemistry at boron-doped diamond electrodes.

RSC advances·2025
Same author

Enhancing microplastic classification through filter-interfered FTIR spectra using dimensionality reduction and deep learning in low-dimensional spaces.

Marine pollution bulletin·2025
Same author

Ai-Aun Chatbot: A Pilot Study on the Effectiveness of an Artificial Intelligence Intervention for Mental Health Among Thai Older Adults.

Nursing & health sciences·2025

Related Experiment Video

Updated: May 9, 2025

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
10:31

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Published on: December 6, 2015

28.0K

Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.

Keerakit Kaewket1,2, Théo Claude Roland Outrequin3, Somrudee Deepaisarn3

  • 1School of Chemistry, Institute of Science, Suranaree University of Technology, 111 University Avenue, Suranaree, Muang, Nakhon Ratchasima 30000, Thailand.

ACS Sensors
|May 6, 2025
PubMed
Summary

This study presents a novel electrochemical sensor for creatinine monitoring, using a cobalt@copper electrode and machine learning. The developed sensor offers a reliable, cost-effective, and highly sensitive method for creatinine detection in biological samples.

Keywords:
creatininemachine learningsensorurinevoltammetry

More Related Videos

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging
04:33

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Published on: December 8, 2023

726
Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics
10:50

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Published on: July 16, 2018

16.2K

Related Experiment Videos

Last Updated: May 9, 2025

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
10:31

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Published on: December 6, 2015

28.0K
Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging
04:33

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Published on: December 8, 2023

726
Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics
10:50

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Published on: July 16, 2018

16.2K

Area of Science:

  • Electrochemistry
  • Nanomaterials Science
  • Biomedical Engineering

Background:

  • Creatinine monitoring is crucial for assessing kidney function.
  • Existing methods for creatinine detection can be expensive or complex.
  • Development of cost-effective and sensitive electrochemical sensors is needed.

Purpose of the Study:

  • To develop a reliable and cost-effective electrochemical sensor for creatinine monitoring.
  • To leverage synergistic effects of dual-metal electrodes and machine learning for enhanced performance.
  • To validate the sensor's performance in real-world biological samples.

Main Methods:

  • Fabrication of a dual-metal cobalt@copper electrode via sequential electrodeposition of nanoparticles.
  • Characterization of electrode-nanoparticle complexation using cyclic voltammetry and spectroelectrochemical analyses.
  • Application of machine learning algorithms (Random Forest, Extra Trees, XGBoost) for data analysis and feature optimization.

Main Results:

  • Achieved a linear detection range of 0.00-4.00 mM with high sensitivity (6.06 ± 0.65 microA mM-1) and a low limit of detection (0.13 mM).
  • Demonstrated excellent selectivity against common interfering substances like urea, glucose, and ascorbic acid.
  • Validated practical application in urine samples, showing strong agreement with standard creatinine assays.

Conclusions:

  • The developed cobalt@copper electrochemical sensor, enhanced by machine learning, provides a sensitive, selective, and cost-effective platform for creatinine monitoring.
  • The synergistic effect of dual metals and optimized data analysis significantly improves sensor performance.
  • This approach holds promise for improved diagnostics and point-of-care applications in kidney function assessment.