Bonding of C1 fragments on metal nanoclusters: a search for methane conversion catalysts with swarm intelligence

Molecular Understanding of the Distinction between Adhesive Failure and Cohesive Failure in Adhesive Bonds with Epoxy Resin Adhesives

In the development of adhesives, an understanding of the fracture behavior of the bonded joints is inevitable. Two typical failure modes are known: adhesive failure and cohesive failure. However, a molecular understanding of the cohesive failure process is not as advanced as that of the adhesive failure process. In this study, research was developed to establish a molecular understanding of cohesive failure using the example of a system in which epoxy resin is bonded to a hydroxyl-terminated self-assembled monolayer (SAM) surface. Adhesive failure was modeled as a process in which an epoxy molecule is pulled away from the SAM surface. Cohesive failure, on the other hand, was modeled as the process of an epoxy molecule separating from another epoxy molecule on the SAM surface or breaking of a covalent bond within the epoxy resin. The results of the simulations based on the models described above showed that the results of the calculations using the model of cohesive failure based on the breakdown of intermolecular interactions agreed well with the experimental results in the literature. Therefore, it was suggested that the cohesive failure of epoxy resin adhesives is most likely due to the breakdown of intermolecular interactions between adhesive molecules. We further analyzed the interactions at the adhesive failure and cohesive failure interfaces and found that the interactions at the cohesive failure interface are mainly accounted for by dispersion forces, whereas the interactions at the adhesive failure interface involve not only dispersion forces but also various chemical interactions, including hydrogen bonds. The selectivity between adhesive failure and cohesive failure was explained by the fact that varying the functional group density affected the chemical interactions but not the dispersion forces.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

This paper details the use of computational and informatics methods to design metal nanocluster catalysts for efficient ammonia synthesis. Three main problems are tackled: defining a measure of catalytic activity, choosing the best candidate from a large number of possibilities, and identifying the thermodynamically stable cluster catalyst structure. First-principles calculations, Bayesian optimization, and particle swarm optimization are used to obtain a Ti8 nanocluster as a catalyst candidate. The N2 adsorption structure on Ti8 indicates substantial activation of the N2 molecule, while the NH3 adsorption structure suggests that NH3 is likely to undergo easy desorption. The study also reveals several cluster catalyst candidates that break the general trade-off that surfaces that strongly adsorb reactants also strongly adsorb products.

Oxidative Addition of Methane and Reductive Elimination of Ethane and Hydrogen on Surfaces: From Pure Metals to Single Atom Alloys

Oxidative addition of CH₄ to the catalyst surface produces CH₃ and H. If the CH₃ species generated on the surface couple with each other, reductive elimination of C₂H₆ may be achieved. Similarly, H's could couple to form H₂. This is the outline of nonoxidative coupling of methane (NOCM). It is difficult to achieve this reaction on a typical Pt catalyst surface. This is because methane is overoxidized and coking occurs. In this study, the authors approach this problem from a molecular aspect, relying on organometallic or complex chemistry concepts. Diagrams obtained by extending the concepts of the Walsh diagram to surface reactions are used extensively. C-H bond activation, i.e., oxidative addition, and C-C and H-H bond formation, i.e., reductive elimination, on metal catalyst surfaces are thoroughly discussed from the point of view of orbital theory. The density functional theory method for structural optimization and accurate energy calculations and the extended Hückel method for detailed analysis of crystal orbital changes and interactions play complementary roles. Limitations of monometallic catalysts are noted. Therefore, a rational design of single atom alloy (SAA) catalysts is attempted. As a result, the effectiveness of the Pt₁/Au(111) SAA catalyst for NOCM is theoretically proposed. On such an SAA surface, one would expect to find a single Pt monatomic site in a sea of inert Au atoms. This is desirable for both inhibiting overoxidation and promoting reductive elimination.