Hydride-reduction-induced oxygen vacancies in CoV₂O₆ for machine learning-assisted enhanced electrochemical detection of homovanillic acid

Enhancing the intrinsic properties of metal oxides without relying on external modifiers remains challenging for achieving improved electrochemical response and reducing sensor fabrication costs. Herein, a simple hydride-reduction route is adopted to integrate oxygen vacancies in cobalt vanadium oxide (CoV2O6) microspheres to improve its electrochemical oxidation towards homovanillic acid (HVA), a cancer biomarker. CoV2O6 prepared via hydrothermal route, when systematically exposed to varying concentrations of NaBH4, generates abundant oxygen vacancies. A systematic comparison of CVO and CVOv confirms that vacancies are critical in improving catalytic sites and charge transferability during HAV oxidation in PBS (0.1 M) (pH 6.5). Differential pulse voltammetry (DPV)-based sensing confirms the sensor's excellent workability in the low concentration range of 0.15 to 4.0 uM with a low LOD of 0.03 uM HAV in PBS (0.1 M). Moreover, the sensor exhibits high selectivity towards HAV, even in common interferents. Machine learning (ML)-based algorithms validated the sensor's performance, and the comparative evaluation showed that artificial neural network (ANN) outperformed others in interpreting DPV data, achieving a minimal mean absolute error (MAE) of 0.2927, in contrast to 0.8475 for LightGBM and 0.8785 for support vector machine (SVM), thereby confirming its enhanced accuracy in predicting HVA concentration.Please check the edits made to the article title and amend if necessary.thank youPlease confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author Given name: [Razium Ali] Last name [Soomro]. Also, kindly confirm the details in the metadata are correct.thank you