With the literature reported aerobic alcohol oxidation protocol in hand, understanding the substrate scope under various reaction conditions for a methodology is essential for selecting the suitable reaction conditions based on the target compound structure in practical synthesis. It is typically challenging to predict the reaction yield based on first-principle theories, while recently emerging machine learning based methods need a decent amount of experimental data to train the neural model for accurate predictions . Thus, the second step for synthesis development process generally involves the exploration of substrate scope for a specific protocol. The recent development of automated high-throughput screening (HTS) technology has been proven as a powerful tool to accelerate the experimental data acquisition for these substrate scope studies . However, HTS technology is still not a routine tool that synthesis practitioners would use on their daily reaction development workflows. Apart from the high costs of the required HTS hardware (e.g., liquid handler platform, automated synthesis platform, and HTS analytical apparatus), the time-consuming programming for executing the automation platforms and manual analysis of large amount of HTS results create barriers for chemists with minimal coding experience to use HTS technology in their routine workflows.