A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers.

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations.

Deep Learning-Assisted Smartphone-Based Electrochemiluminescence Visual Monitoring Biosensor: A Fully Integrated Portable Platform.

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing and wax printing methods with paper-based substrate were used to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed portable black box served as a reaction chamber.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT.

Machine learning assisted and smartphone integrated homogeneous electrochemiluminescence biosensor platform for sample to answer detection of various human metabolites.

The sensitive and accurate detection of glucose and lactate is essential for early diagnosis and effective management of diabetes complications. Herein, a 3D Printed ECL imaging system integrated with a Smartphone has been demonstrated to advance the traditional ECL to make a portable, affordable, and turnkey point-of-care solution to detect various human metabolites. A universal cross-platform application was introduced for analyzing ECL emitted signals to automate the whole detection process for real-time monitoring and rapid diagnostics.

Multiplexed and simultaneous biosensing in a 3D-printed portable six-well smartphone operated electrochemiluminescence standalone point-of-care platform.

3D-printed portable devices have immense and proven potential to transform the field of electrochemiluminescence (ECL) for diverse biochemical applications. 3D printing (3DP) offers unparalleled ability to build tiny devices in a single step with high accuracy and compatibility, and integrability as per the requirement. In this study, for the first time, a six-well 3D-printed closed bipolar electrochemiluminescence (3DP-CBPE-ECL) device has been successfully fabricated and validated by performing single-step detection of various biochemicals such as glucose and choline.

Internet of things-enabled photomultiplier tube- and smartphone-based electrochemiluminescence platform to detect choline and dopamine using 3D-printed closed bipolar electrodes.

There is a growing demand to realize low-cost miniaturized point-of-care testing diagnostic devices capable of performing many analytical assays. To fabricate such devices, three-dimensional printing (3DP)-based fabrication techniques provide a turnkey approach with marked precision and accuracy. Here, a 3DP fabrication technique was successfully utilized to fabricate closed bipolar electrode-based electrochemiluminescence (ECL) devices using conductive graphene filament.

Miniaturized Electrochemiluminescence Platform With Laser-Induced Graphene Electrodes for Multiple Biosensing.

The present work demonstrates a miniaturized 3D printed Electrochemiluminescence (ECL) sensing platform with Laser-Induced Graphene (LIG) based Open Bipolar Electrodes (OBEs). To fabricate OBEs, polyimide (PI) substrate has been used as it provides properties like low-cost fabrication, high selectivity, great stability, easy reproducibility, cost-effectiveness and rapid prototyping. Moreover, graphene can be created on PI in a single step during the ablation of the CO laser.