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Molecular corking is a phenomenon where a molecule selectively binds to the catalytic atom of a single-atom alloy (SAA) and prevents the recombination of atomized gas molecules at the surface of a metal, functionally trapping them. A necessary feature of a molecular cork is strong, yet reversible binding and thermal stability. There is active experimental and theoretical research on potential candidates, which rank the viability of molecular corks based on their ability to donate electrons into sigma bonds and accept back-donation from the metal via pi bonding. This is often assessed experimentally and computationally using chemical shifts in NMR of selenium and phosphorus adducts as well as other metal complexes. A prime candidate for this purpose are N-heterocyclic carbenes (NHC). However, there have been few studies that perform bond decomposition analysis of periodic quantum mechanical calculations to qualify if the observed chemical shifts in NMR and binding strengths on metals are in fact due to sigma donation and pi back bonding. Moreover, the role of conformational flexibility plays in reported NMR chemical shifts is relatively unexplored. Using periodic vdW-DFT calculations and localized bonding MOs extracted from periodic plane-wave basis sets, along with molecular calculations, we seek to answer these questions for simple, but representative molecular systems bound to Cu-Pd SAA surfaces. 

This study investigates the integration of generative artificial intelligence (Gen AI) into a General Chemistry II laboratory at St. Bonaventure University to assess its impact on student learning, inquiry, and chemical reasoning. In a redesigned of a common thermodynamics/equilibrium experiment involving cobalt(II) chloride, students used ChatGPT and a retrieval-augmented Gen AI (BonnieChemBot) to assist in calculating equilibrium constants, thermodynamic values, and answering open-ended chemical questions. The lab targeted four key educational goals: (A) mastery of chemical concepts, (B) development of prompt engineering strategies, (C) facilitation of inquiry-driven dialogue with instructors, and (D) critical evaluation of Gen AI outputs by students using chemical intuition. Students attempted all analyses twice: first independently, then with the assistance of Gen AI. Finally, they were asked to reflect on their trust in the Gen AI solutions and generate final answers. Instructor intervention in post-lab analyses was limited to cases where students had clearly attempted the first two steps in this process. While students were encouraged to practice prompt engineering, no grades were assigned to its execution. Instead, grading focused solely on students’ final answers.