Development of Smartphone-based ECL Sensor for Dopamine Detection: Practical Approaches

In this work, a compact, mobile phone-based ECL sensor apparatus was developed using the phone cameras, screen-printed electrodes (SPE), and mobile app for dopamine detection. Methods of DC voltage application for ECL reaction were comprehensively studied from the mobile phone itself or external power. Under optimized sensing conditions, with disposable carbon SPE and 20 mM coreactant tri-n-propylamine (TPrA), acceptable repeatability and reproducibility were achieved in terms of relative standard deviation (RSD) of intra- and interassays, which were 6.7 and 5.5%, respectively. The biochemical compound dopamine was measured due to its ECL quenching characteristics and its clinical importance. The quenching mechanism of Ru(bpy)32+/TPrA by dopamine was investigated based on the estimation of the constants of the Stern-Volmer equations. The linear range for detectable dopamine concentration was from 1.0 to 50 μM (R2 = 0.982). As the developed mobile phone-based ECL sensor is simple, small and assembled from low-cost components, it offers new opportunities for the development of inexpensive analytical methods and compact sensors

B-01 BibleOL v2.0 – AU research and Global Learning

Bible-Online-Learner was updated on Oct 4 with a brand-new user interface and new functionality. Most of the new features have been contributed by our FRG-funded research group at AU. The BibleOL allows teachers and students to approach Biblical languages with the tools developed persuasive object learning technology (PLOT). This has proven to improve the language proficiency of students by about 12%. Therefore, students of classes that use the BibleOL as a basic tool for learning will translate significantly faster and more accurate Biblical texts in their original language. In our presentation, we will showcase the linguistic features, the automatic grading feature, and the exam feature that we have developed over the last years. Several universities on most continents use BibleOL. The new features will allow for much broader adoption of the BibleOL and make grading and exam production much more effortless. With the latest update, BibleOL can now offer TOEFL-like tests for Biblical Hebrew and Biblical Greek

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru(bpy)32+ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e Ru(bpy)2+3 luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru(bpy)32+ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e Ru(bpy)2+3 /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru(bpy)32+ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e Ru(bpy)2+3 concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru(bpy)32+ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e Ru(bpy)2+3 using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling