Theoretical Design And Performance Study Of Carbon-based Catalysts For H₂O₂ Production

Hydrogen peroxide（H₂O₂）is an important chemical raw material that plays an essential role in medicine,food and environmental protection.Currently,the industrial production of H₂O₂ is mainly based on the traditional anthraquinone process,which suffers from several problems such as complex procedures,high energy consumption and environmental pollution.Therefore,there is an urgent demand for a simple,clean,and efficient method of H₂O₂ production.Electrochemical oxygen reduction to H₂O₂ presents an ideal alternative that utilizes renewable energy sources such as wind and solar to achieve carbon-free emission under moderate conditions suitable for decentralized and onsite on-demand H₂O₂ production.The development of high-performance catalysts is critical for the industrialization of this technology,and understanding the reaction mechanism can accelerate the research and development of efficient catalysts.This dissertation selects economic carbon-based catalysts as the research subjects,and theoretically designs a series of carbon-based catalysts for the efficient production of H₂O₂,and reveals the catalytic mechanism based on Ab-initio molecular dynamics（AIMD）,providing an important guide for experimental optimization.Specifically,the main research contents include the following four parts:1.Numerous studies have reported that oxygen-functionalized carbon materials as potential electrocatalyst for H₂O₂ production.However,due to the complexity of oxygen functional groups in carbon systems and the limitations of in situ characterization techniques,the identification of active sites and the understanding of the reaction mechanism remain controversial.This work presents a workflow that combines the conventional hydrogen electrode model with AIMD simulations to identify active sites among various oxygen-containing carbon structures.Theoretical calculations highlight the crucial role of steric hydroxyl functional groups at the edges,which modulate the intra-and inter-molecular hydrogen bonding strength of the key intermediate OOH during the two-electron oxygen reduction process.Under basic electrolyte conditions,the OOH intermediate dissociates more readily into H₂O₂.Increased applied potentials inhibit the competing four-electron process,further enhancing reaction selectivity.Subsequent comparative experiments validate the active sites,and optimized material preparation processes based on theoretical insights significantly improve the selectivity and current density of H₂O₂ production.2.Although theoretical calculations suggest that oxygenated carbon materials with steric hydroxyl groups facilitate H₂O₂ production,accurate preparation and experimental characterization of such structures is challenging.This work proposes the incorporation of non-catalytic main group metals,i.e.,Mg and Al,into carbon materials to form C-O-Al/Mg coordination sites similar to the hydroxyl（C-OH）structures for efficient electrocatalytic H₂O₂ production.Theoretical calculations demonstrate that the metal sites coordinate with water molecules,modifying the electronic structure of the carbon active sites.Calculated adsorption free energy shows that the binding of the OOH intermediate to in-plane carbon sites is weak,while edge models with metal coordination can robustly form OOH intermediates.Kinetic studies indicate that the two-electron barrier is significantly lower than that of the four-electron process.Compared to transition metal doped systems,main group metal,especially Mg,doped carbon materials exhibit higher activity and selectivity,providing new insights and theoretical support for main group metal single atom catalysts for H₂O₂ production.3.Beyond the introduction of functional groups or heteroatoms,modifying the topology of the carbon material is another strategy to tune the catalytic activity.Considering the inherent flexibility of carbon layers,this study proposes to adjust the curvature of the carbon layers to enhance their activity to achieve efficient electrocatalytic oxygen reduction for H₂O₂ production.Theoretical calculations show that increasing the curvature promotes the binding of O₂ molecules and the formation of OOH intermediates on carbon layers.In particular,biaxially compressed structures show high reactivity while maintaining excellent conductivity.Dynamic simulations suggest that the OOH intermediate tends towards a two-electron process with a lower C-O bond cleavage barrier compared to the more difficult four-electron water evolution process.Guided by theoretical predictions,experimentally synthesized curved carbon materials exhibit high selectivity and a wider voltage window（selectivity>95%between 0 and 0.7 vs.RHE）.4.The AIMD simulations mentioned above consume substantial computational resources,especially when real solvent dynamics are taken into account.To accelerate the process of reaching equilibrium states on carbon-based catalyst surfaces,this work develops a machine learning potential based on deep learning methods.This model achieves almost first-principles computational accuracy for carbon systems with an order-of-magnitude improvement in computational efficiency,allowing exploration over longer time scales and larger spatial scales.Besides,the developed user-friendly web interface allows easy addition of solvent molecules to the catalyst surface and pre-equilibrium processes based on the built-in machine learning potential.This dissertation focuses on the development of efficient carbon-based catalysts for the electrocatalytic H₂O₂ production by elucidating the oxygen reduction mechanism in oxygen-containing carbon materials and proposing material improvements through specific functional group and main group metal coordination.In the research,the classical computational hydrogen electrode model is combined with the AIMD simulation,which takes into account the effects of real solvation and applied potentials to comprehensively consider the interfacial characteristics of the electrochemical reaction,and the pre-solvation model based on the machine learning potential is established,which greatly improves the efficiency of computational simulation and is of great significance for the study of interfacial catalytic reaction.By closely combining theoretical calculations with experimental validation,it breaks the catalyst development model represented by the trial-and-error method.The proposed curved carbon materials are expected to be green and efficient H₂O₂ production catalysts suitable for industrial applications due to their low cost and excellent catalytic performance.