OptiDots_v1.0: A Comprehensive Database for Machine Learning-Driven Optimization of Green Synthesis Carbon Dots

Understanding the relationships between the physicochemical properties of Carbon Dots (CDs) and synthesis parameters is crucial for optimizing their use and accelerating the development of CDs. However, this task is complex due to the diversity of materials, heterogeneity of published data, and limited sampling in individual studies. This work addresses this gap by introducing OptiDots_v1.0, a comprehensive database designed to support the scientific community in optimizing the synthesis of CDs thought machine learning (ML) or by integrating other analytical techniques. From a comprehensive set of 157 publications on the synthesis of CDs derived from green precursors and hydrothermal synthesis, we meticulously obtained data samples related to characteristics such as particle size, quantum yield, synthesis yield, maximum emission and excitation, fluorescence, elemental composition, and applications of 199 CDs, as well as experimental conditions of time, temperature, and precursor type. As a case study, we applied exploratory data analysis and ML techniques to OptiDots_v1.0 to demonstrate its potential in predictive modeling and experimental design. We show the relationships between continuous variables such as synthesis yield and nitrogen contente, as well as particle size and photolumiscence, and with machine learning methods, it was possible to make inferences about emission wavelenght. This approach, integrating quantitative and qualitative data, provides a roadmap to investigate the data on the properties of CDs in the literature and suggests that meta-analysis can help develop methods to predict and optimize the applications of these nanomaterials.