Abstract
Chemical synthesis experiments are crucial for developing new materials, yet their inherent complexity and variability often lead to suboptimal results, causing significant resource and time wastage. This study proposes a novel decision support model to enhance the success rate of macromolecule synthesis experiments for sensor applications. The model leverages principles of experimental design and decision-making. We identified synthesis alternatives through a literature review and expert consultations, narrowing it down to 6 alternatives for metal salts, 3 for reactant mole ratios, 5 for organic solvents, 3 for reaction temperatures, and 4 for reaction times. Considering the time and cost constraints, this selection process reduced the potential 1080 experiments to a manageable number. The model incorporates conflicting objectives such as yield, purity, cost, and time to maximize reaction yield. Using the analytical hierarchy process (AHP), the model assists decision makers in balancing these tradeoffs, thereby increasing the likelihood of experimental success. The validity of the model was confirmed by conducting 25 laboratory experiments and comparing the results with existing literature.