Optimization Methodology for Project Management: A Case Study for Quantum Dots Synthesis

Quantum dots (QDs) have emerged as promising materials for various applications due to their unique optoelectronic properties, leading to their recognition with the 2023 Nobel Prize in Chemistry. However, challenges associated with metal-based QDs, including toxicity and resource depletion, have spurred interest in alternatives like carbon quantum dots (CQDs) and graphene quantum dots (GQDs). This paper introduces a mathematical optimization model to aid researchers in selecting the most suitable organic waste precursors and synthesis methods for CQDs and GQDs. The model, implemented in Python, utilizes mixed-integer linear programming to maximise project success while considering criteria such as precursor availability, waste utilization, distance to synthesis location, and number of references in literature. A case study conducted in Mexico City demonstrates the model's efficacy in identifying optimal scenarios and allocation of resources. Results reveal insights into preferred precursor-synthesis combinations and highlight the model's adaptability to varying weights of evaluation criteria. Moreover, the model's low computational cost and potential for scalability allows addressing broader challenges related to nanomaterial synthesis and sustainability. © 2025 Author(s)

Optimization Methodology for Project Management: A Case Study for Quantum Dots Synthesis Academic Article in Scopus