Research on differential pulse voltammetry detection method for low concentration glucose based on machine learning model-Reference-Cited by-同舟云学术

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Research on differential pulse voltammetry detection method for low concentration glucose based on machine learning model

Published:2024-02 Issue:2 Volume:19 Page:100479
ISSN:1452-3981
Container-title:International Journal of Electrochemical Science
language:en
Short-container-title:International Journal of Electrochemical Science

Author:

Zhang Bo,Zhang Yao,Shen Jianing,Zhou Zhehai,Zhu Guixian^ORCID

Publisher

Elsevier BV

Reference36 articles.

1. Blood glucose monitoring-an overview of current and future non-invasive devices;Bolla;Diabetes Metab. Syndr. Clin. Res. Rev.,2020

2. Real-world improvements in hypoglycemia in an insulin-dependent cohort with diabetes mellitus pre/post tandem basal-Iq technology remote software update;Pinsker;Endocr. Pract.,2020

3. Evaluation of a minimally invasive glucose biosensor for continuous tissue monitoring;Sharma;Anal. Bioanal. Chem.,2016

4. Electrochemical glucose sensors in diabetes management: an updated review (2010–2020);Teymourian;Chem. Soc. Rev.,2020

5. The progress of glucose monitoring—a review of invasive to minimally and non-invasive techniques, devices and sensors;Villena Gonzales;Sensors,2019

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1. Metal-organic framework derived Mn0.2Zn0.8Se/C amalgamated with nitrogen-doped graphene hydrogel for antioxidant trolox detection in food, environmental, and biological samples;Chemical Engineering Journal;2024-09

2. Enhanced detection of glucose with carbon quantum dot-modified copper oxide: Computational insight and machine learning modeling of electrochemical sensing;Microchemical Journal;2024-09

3. Deep neural network battery charging curve prediction incorporating external information;Journal of Power Sources;2024-04

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