PhD Thesis Defense - David Alejandro Naranjo Tovar

Hydrogels hold immense promise for clean water technologies and flexible biosensors, but realizing their full potential requires precisely balancing network architecture, water transport, and functional additives, properties that often degrade under real-world conditions.

This doctoral research solves these design challenges by combining AI-driven molecular modeling with advanced materials synthesis, creating robust, multifunctional hydrogels for next-generation purification and soft sensing.

Key highlights of the research:

• AI-Guided Network Modeling: Developing SuSi, a Python framework using AI tree-search algorithms to construct realistic polymer networks and quantify connectivity for molecular simulations.

• Molecular & Interfacial Engineering: Mapping how side-chain structure, PEDOT nanoparticles, and carbon quantum dots dictate thermoresponsiveness, hydration microenvironments, and photothermal activity.

• Next-Gen Water Purification: Translating hybrid hydrogels into solar-driven evaporators for efficient desalination and durable coatings for capacitive-deionization electrodes to enhance ion transport.

• Flexible Soft (Bio)Sensing: Engineering skin-compatible temperature sensors, metal-free hydrogen peroxide detectors via polypyrrole networks, and antibacterial silver-microstructure sensors tuned with glycerol.

Ultimately, this work bridges microscopic computer simulations and macroscopic device engineering, offering a unified strategy to build adaptable hydrogel technologies for sustainable water treatment and soft electronics.