Understanding CO oxidation on the Pt(111) surface based on a reaction route network

Analysis of a reaction on a solid surface is an important task for understanding the catalytic reaction mechanism. In this study, we studied CO oxidation on the Pt(111) surface by using the artificial force induced reaction (AFIR) method. A systematic reaction path search was done, and the reaction route network was created. This network included not only bond rearrangement paths but also migration paths of adsorbed species. Then, the obtained network was analyzed using a kinetics method called rate constant matrix contraction (RCMC). It is found that the bottleneck of the overall reaction is the CO2 generation step from an adsorbed CO molecule and an O atom. This result is consistent with the Langmuir-Hinshelwood (LH) mechanism with O-2 dissociation discussed in previous studies. The present procedure, i.e., construction of the reaction route network by the AFIR method followed by application of the RCMC kinetics method to the resultant reaction route network, was fully systematic and uncovered two aspects: the impact of the existence of multiple paths in each bond rearrangement step and an entropic contribution arising from short-range migration of adsorbed species

Quantum chemical calculations to trace back reaction paths for the prediction of reactants

The long-due development of a computational method for the ab initio prediction of chemical reactants that provide a target compound has been hampered by the combinatorial explosion that occurs when reactions consist of multiple elementary reaction processes. To address this challenge, we have developed a quantum chemical calculation method that can enumerate the reactant candidates from a given target compound by combining an exhaustive automatic reaction path search method with a kinetics method for narrowing down the possibilities. Two conventional name reactions were then assessed by tracing back reaction paths using this new method to determine whether the known reactants could be identified. Our method is expected to be a powerful tool for the prediction of reactants and the discovery of new reactions

Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants

The long-due development of a computational method for the ab initio prediction of chemical reactants that provide a target compound has been hampered by the combinatorial explosion that occurs when reactions consist of multiple elementary reaction processes. To address this challenge, we have developed a quantum chemical calculation method that can enumerate the reactant candidates from a given target compound by combining an exhaustive automated reaction path search method with a kinetics method for narrowing down the possibilities. Two conventional name reactions were then assessed by tracing back the reaction paths using this new method to determine whether the known reactants could be identified. Our method is expected to be a powerful tool for the prediction of reactants and the discovery of new reactions

Importance of the Habenula for Avoidance Learning Including Contextual Cues in the Human Brain: A Preliminary fMRI Study

Human habenula studies are gradually advancing, primarily through the use of functional magnetic resonance imaging (fMRI) analysis of passive (Pavlovian) conditioning tasks as well as probabilistic reinforcement learning tasks. However, no studies have particularly targeted aversive prediction errors, despite the essential importance for the habenula in the field. Complicated learned strategies including contextual contents are involved in making aversive prediction errors during the learning process. Therefore, we examined habenula activation during a contextual learning task. We performed fMRI on a group of 19 healthy controls. We assessed the manually traced habenula during negative outcomes during the contextual learning task. The Beck Depression Inventory-Second Edition (BDI-II), the State-Trait-Anxiety Inventory (STAI), and the Temperament and Character Inventory (TCI) were also administered. The left and right habenula were activated during aversive outcomes and the activation was associated with aversive prediction errors. There was also a positive correlation between TCI reward dependence scores and habenula activation. Furthermore, dynamic causal modeling (DCM) analyses demonstrated the left and right habenula to the left and right hippocampus connections during the presentation of contextual stimuli. These findings serve to highlight the neural mechanisms that may be relevant to understanding the broader relationship between the habenula and learning processes