Theoretical examination of solvent and R group dependence in gold thiolate nanoparticle synthesis
文献情報
Suzanne M. Neidhart, Brian M. Barngrover, Christine M. Aikens
The growth of gold thiolate nanoparticles can be affected by the solvent and the R group on the ligand. In this work, the difference between methanol and benzene solvents as well as the effect of alkyl (methyl) and aromatic (phenyl) thiols on the reaction energies and barrier heights is investigated theoretically. Density functional theory (DFT) calculations using the BP86 functional and a triple ΞΆ polarized basis set show that the overall reaction favors methylthiol over phenylthiol with reaction energies of β0.54 and β0.39 eV in methanol, respectively. At the same level of theory, the methanol solvent is favored over the benzene solvent for reactions forming ions; in benzene, the overall reaction energies for methylthiol and phenylthiol reacting with AuCl4β to form Au(HSR)2+ are 0.37 eV and 0.44 eV, respectively. Methylthiol in methanol also has the lowest barrier heights at about 0.3 eV, whereas phenylthiol has barrier heights around 0.4 eV. Barrier heights in benzene are significantly larger than those in methanol.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.











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