computational chemistry

Many organic molecules are efficient light emitters used for optoelectronic devices such as OLEDs, due to their advantages over metallic counterparts, including lower toxicity, simpler disposal, and sustainability. However, the methodologies commonly used in organic synthesis to obtain these molecules often rely on harsh conditions and generate large amounts of waste, making them both ineffective and inefficient. This work aligns with some of the principles of green chemistry across different stages.

Ice formation remains a critical challenge across multiple industries, posing safety risks, economic burdens, and, in extreme cases, fatalities. Effective anti-icing strategies are essential to mitigate these issues, yet the demand for environmentally friendly, cost-effective, and efficient solutions persists. Natural deep eutectic solvents (NADES) have emerged as a promising low-toxicity alternative for addressing ice formation.

Neglected tropical diseases, such as Chagas disease caused by Trypanosoma cruzi, remain a critical global health problem, particularly in underprivileged communities. Our research aims to develop potential therapeutic agents by synthesizing and characterizing analogues of aureothin, a polyketide with known antiparasitic activity.

A novel statistical correlation method, MM-DP4+, was developed to enhance NMR-based molecular structure elucidation by significantly reducing computational costs through the use of MM-optimized geometries. A comprehensive evaluation of 36 theory levels identified SMD/ωB97XD/6-31+G**//MMFF as the most accurate and cost-effective approach, achieving 91% accuracy in stereochemical assignments. A Python-based software, DP4+App, was created to streamline the implementation of DP4+, MM-DP4+, and customizable DP4+ calculations via a user-friendly interface.