Periodic Reporting for period 3 - OPERANDOCAT (In situ and Operando Nanocatalysis: Size, Shape and Chemical State Effects)
Reporting period: 2019-04-01 to 2020-09-30
Complicating the analysis is the fact that the former parameters cannot be considered independently, since the NP size as well as the support will have an impact on the most stable NP shapes. In addition, the dynamic nature of the NP catalysts and their response to the environment must be taken into consideration, since the working state of a NP catalyst might not be the state in which the catalyst was prepared, but rather a structural and/or chemical isomer that adapted to the particular reaction conditions. To address the complexity of real-world catalysts, a synergistic approach taking advantage of a variety of cutting-edge experimental methods must be undertaken.
This project focuses on model heterogeneous catalysts for reactions of tremendous societal and industrial relevance, namely the gas-phase hydrogenation and electrocatalytic reduction of carbon dioxide. Important components missing from existing studies that are addressing with our research are the systematic design of catalytically active model NPs with narrow size and shape distributions and tunable oxidation state, and in situ and operando structural, chemical, and reactivity characterization of such model catalysts as a function of the reaction environment. The results are expected to open up new routes for the reutilization of CO2 through its direct conversion into valuable chemicals and fuels such as methanol, ethylene and ethanol.
We have employed size-controlled (∼5 nm) Cu100−xZnx nanoparticles (NPs) supported on carbon to investigate the correlation between their structure and composition and catalytic performance. By tuning the concentration of Zn, a drastic increase in CH4 selectivity [∼70% Faradaic efficiency (F.E.)] could be achieved for Zn contents from 10 to 50, which was accompanied by a suppression of the H2 production. Samples containing a higher Zn concentration displayed significantly lower CH4 production and an abrupt switch in the selectivity to CO, as shown in Figure 1.
Lack of metal leaching was observed based on quasi in situ X-ray photoelectron spectroscopy (XPS). Operando X-ray absorption fine structure (XAFS) spectroscopy measurements revealed that the alloying of Cu atoms with Zn atoms takes place under reaction conditions and plays a determining role in the product selectivity. Time-dependent XAFS analysis showed that the local structure and chemical environment around the Cu atoms continuously evolve during CO2RR for several hours. In particular, cationic Zn species initially present were found to get reduced as the reaction proceeded, leading to the formation of a CuZn alloy (brass). The evolution of the Cu−Zn interaction with time during CO2RR was found to be responsible for the change in the selectivity from CH4 over Cu-ZnO NPs to CO over CuZn alloy NPs. This study highlights the importance of having access to in depth information on the interplay between the different atomic species in bimetallic NP electrocatalysts under operando reaction conditions in order to understand and ultimately tune their reactivity.
In a different approach we found that the electrochemical synthesis of CO2RR catalysts is highly selective for C2+ products via electrolyte‐driven nanostructuring. Nanostructured Cu catalysts synthesized in the presence of specific anions selectively convert CO2 into ethylene and multicarbon alcohols in aqueous 0.1 M KHCO3 solution, with the iodine‐modified catalyst displaying the highest Faradaic efficiency of 80 % and a partial geometric current density of ca. 31.2 mA cm−2 for C2+ products at −0.9 V vs. RHE. Operando XAFS and quasi in situ XPS measurements revealed that the high C2+ selectivity of these nanostructured Cu catalysts can be attributed to the highly roughened surface morphology induced by the synthesis, presence of subsurface oxygen and Cu+ species, and the adsorbed halides (Angew. Chem. Int. Ed. 2019, 58, 17047-17053).
In situ and Operando studies for thermal CO2 hydrogenation applications
In order to gain insight into the reaction mechanism, a systematic study (J. Phys. Chem. C. 2019, 123, 8421-8428) of size-selected 5 nm micellar Cu0.5Ni0.5 NPs/SiO2 was performed using near ambient pressure XPS (NAP-XPS), AFM, STEM, and catalytic characterization in a fixed-bed flow reactor, combined with DFT calculations. In order to obtain a depth profile of the elemental composition and chemical state of the Cu0.5Ni0.5 NPs, NAP-XPS spectra were acquired under different environmental conditions at two different photon energies. Moderate surface segregation of Ni occurs in O2, stronger Ni surface segregation in H2, and even stronger Ni segregation in CO. A remarkable inversion of this trend is observed in the CO2+CO+H2 reaction mixture, which drives Cu to the surface.
Similar NAP-XPS measurements performed in a reactant mixture consisting only of CO2 and H2 showed Ni surface segregation. Since CO by itself also induces Ni surface segregation, the Cu surface segregation can only be explained by taking into consideration the influence of the products and reaction intermediates. DFT calculations from our collaborator showed that surface segregation energies change depending on the adsorbates on the surface, especially the different reaction intermediates, in the case, methoxy species. These theoretical findings are consistent with the observed segregation trends in the NAP-XPS experiments. The promoting role of CO in the reactant mixture was confirmed with catalytic characterization measurements of the CuNi NP/SiO2 powder. We are currently investigating the CuNi system further by looking at different sized NPs with operando EXAFS.
In addition, we expect to be able to develop more efficient and selective CO2 hydrogenation catalysts based on rational catalyst design, and more specifically, on our ability to tune the nanoparticle size, shape and composition.
We also expect to continue making advances in the analysis of spectroscopic data acquired under operando reaction conditions based on the use of machine learning and neural networks.