S. Shayan Mousavi Masouleh

The synergy of human expertise and AI-enabled active learning accelerated process optimization, enabling battery-grade lithium production from impurity-rich brines while enhancing the economic viability of low-grade resources.

The rapid growth of scientific papers presents several challenges for researchers, including information overload, data fragmentation, lack of standard terminology, and limited interoperability. These issues make it increasingly difficult to keep up with the literature and extract useful, field-specific information efficiently. To address these challenges, we introduce the semantically-linked ontological knowledge extraction framework, a tool that combines smart linking of concepts, flexible ontology building, and graph-based reasoning to organize and extract knowledge. This semantically-linked ontological knowledge extraction graph (SOKE Graph) leverages large-language models to extract domain-specific concepts from scientific articles, ensuring both semantic accuracy and adaptability to new data. These concepts are organized using an ontology—a structured framework of terms and relationships—that supports systematic data collection by grouping information into clearly defined layers. The resulting knowledge graph enables structured representation of extracted domain-specific information, allowing researchers to explore relationships between concepts and efficiently retrieve relevant data aligned with the objectives of this work. This approach allows SOKE Graph to find the connections between concepts and publications, and retrieve the most relevant studies in response to user questions. Initial evaluations of SOKE Graph show that it helps improve the accuracy of filtering high-relevance papers from large datasets compared to using only large-language models, and generates structured, interpretable outputs that facilitate data-driven insights. Moreover, this framework provides a robust and scalable artificial intelligence-based tool that accelerates literature analysis, guides decision-making, and helps researchers efficiently locate relevant information within complex scientific domains.

Employing AI to optimize the production of battery-grade lithium carbonate through a CO₂-driven process, enhancing efficiency and reducing environmental impact of industrial Li production.

The surface plasmon response of a cross-sectional segment of a wrinkled gold film is studied using electron energy loss spectroscopy (EELS). EELS data demonstrate that wrinkled gold structures act as a suitable substrate for surface plasmons to propagate. The intense surface variations in these structures facilitate the resonance of a wide range of surface plasmons, leading to the broadband surface plasmon response of these geometries from the near-infrared to visible wavelengths. The metallic nanoparticle boundary element method toolbox is used to simulate plasmon eigenmodes in these structures. Eigenmode simulations show how the diverse morphology of the wrinkled structure leads to its high spectral complexity. Micron-sized structural features that do not provide interactions between segments of the wrinkle have only a small effect on the surface plasmon resonance response, whereas nanofeatures strongly affect the resonant modes of the geometry. According to eigenmode calculations, different eigenenergy shifts around the sharp folds contribute to the broadband response and infrared activity of these structures; these geometrical features also support higher energy (shorter wavelength) symmetric and anti-symmetric plasmon coupling across the two sides of the folds. It is also shown that additional plasmon eigenstates are introduced from hybridization of modes across nanogaps between structural features in close proximity to each other. All of these factors contribute to the broadband response of the wrinkled gold structures.