Methanol synthesis on ZnO(0001¯). III. Free energy landscapes, reaction pathways, and mechanistic insights

Emerging ZnO Semiconductors for Photocatalytic CO2 Reduction to Methanol

Carbon recycling is poised to emerge as a prominent trend for mitigating severe climate change and meeting the rising demand for energy. Converting carbon dioxide (CO2) into green energy and valuable feedstocks through photocatalytic CO2 reduction (PCCR) offers a promising solution to global warming and energy needs. Among all semiconductors, zinc oxide (ZnO) has garnered considerable interest due to its ecofriendly nature, biocompatibility, abundance, exceptional semiconducting and optical properties, cost-effectiveness, easy synthesis, and durability. This review thoroughly discusses recent advances in mechanistic insights, fundamental principles, experimental parameters, and modulation of ZnO catalysts for direct PCCR to C1 products (methanol). Various ZnO modification techniques are explored, including atomic size regulation, synthesis strategies, morphology manipulation, doping with cocatalysts, defect engineering, incorporation of plasmonic metals, and single atom modulation to boost its photocatalytic performance. Additionally, the review highlights the importance of photoreactor design, reactor types, geometries, operating modes, and phases. Future research endeavors should prioritize the development of cost-effective catalyst immobilization methods for solid-liquid separation and catalyst recycling, while emphasizing the use of abundant and non-toxic materials to ensure environmental sustainability and economic viability. Finally, the review outlines key challenges and proposes novel directions for further enhancing ZnO-based photocatalytic CO2 conversion processes.

Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis

Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.

Complex Reaction Network Thermodynamic and Kinetic Autoconstruction Based on Ab Initio Statistical Mechanics: A Case Study of O2 Activation on Ag4 Clusters

An approach based on ab initio statistical mechanics is demonstrated for autoconstructing complex reaction networks. Ab initio molecular dynamics combined with Markov state models are employed to study relevant transitions and corresponding thermodynamic and kinetic properties of a reaction. To explore the capability and flexibility of this approach, we present a study of oxygen activation on Ag4 as a model reaction. Specifically, with the same sampled trajectories, it is possible to study the structural effects and the reaction rate of the cited reaction. The results show that this approach is suitable for automatized construction of reaction networks, especially for non-well-studied reactions, which can benefit from this ab initio molecular dynamics based approach to construct comprehensive reaction networks with Markov state models without prior knowledge about the potential energy landscape.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Molecular Modelling for Reactor Design

Chemical reactor modelling based on insights and data on a molecular level has become reality over the last few years. Multiscale models describing elementary reaction steps and full microkinetic schemes, pore structures, multicomponent adsorption and diffusion inside pores, and entire reactors have been presented. Quantum mechanical (QM) approaches, molecular simulations (Monte Carlo and molecular dynamics), and continuum equations have been employed for this purpose. Some recent developments in these approaches are presented, in particular time-dependent QM methods, calculation of van der Waals forces, new approaches for force field generation, automatic setup of reaction schemes, and pore modelling. Multiscale simulations are discussed. Applications of these approaches to heterogeneous catalysis are demonstrated for examples that have found growing interest over the last few years, such as metal-support interactions, influence of pore geometry on reactions, noncovalent bonding, reaction dynamics, dynamic changes in catalyst nanoparticle structure, electrocatalysis, solvent effects in catalysis, and multiscale modelling.

Cu/ZnO nanocatalysts in response to environmental conditions: surface morphology, electronic structure, redox state and CO2 activation

Methanol synthesis is one of the landmarks of heterogeneous catalysis due to the great industrial significance of methanol as a clean liquid fuel and as a raw material for industry.

CO2 phonon mode renormalization using phonon-assisted energy up-conversion

Molecular dissociation under incident light whose energy is lower than the bond dissociation energy has been achieved through multi step excitation using a coupled state of a photon, electron, and multimode-coherent phonon as known as the dressed photon phonon (DPP). Here, we have investigated the effects of the DPP on CO₂, a very stable molecule with high absorption and dissociation energies, by introducing ZnO nanorods to generate the DPP. Then, the changes in CO₂ absorption bands were evaluated using light with a wavelength longer than the absorption wavelength, which confirmed the DPP-assisted energy up-conversion. To evaluate the specific CO₂ modes related to this process, we measured the CO₂ vibration-rotation spectra in the near-infrared region. Detailed analysis of the 3ν₃ vibrational band when a DPP source is present showed that DPP causes a significant increase in the intensity of certain absorption bands, especially those that require higher energies to activate.