Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra.

X-ray photoelectron spectroscopy is an indispensable technique for the quantitative determination of sample composition and electronic structure in diverse research fields. Quantitative analysis of the phases present in XP spectra is usually conducted manually by means of empirical peak fitting performed by trained spectroscopists. However, with recent advancements in the usability and reliability of XPS instruments, ever more (inexperienced) users are creating increasingly large data sets that are harder to analyze by hand. In order to aid users with the analysis of large XPS data sets, more automated, easy-to-use analysis techniques are needed. Here, we propose a supervised machine learning framework based on artificial convolutional neural networks. By training such networks on large numbers of artificially created XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), we created universally applicable models for auto-quantification of transition-metal XPS data that are able to predict the sample composition from spectra within seconds. Upon evaluation against more traditional peak fitting methods, we showed that these neural networks achieve competitive quantification accuracy. The proposed framework is shown to be flexible enough to accommodate spectra containing multiple chemical elements and measured with different experimental parameters. The use of dropout variational inference for the determination of quantification uncertainty is illustrated.

Crystallographic Orientation Dependence of Surface Segregation and Alloying on PdCu Catalysts for CO2 Hydrogenation.

The influence of the crystallographic orientation on surface segregation and alloy formation in model PdCu methanol synthesis catalysts was investigated in situ using near-ambient pressure X-ray photoelectron spectroscopy under CO2 hydrogenation conditions. Combined with scanning tunneling microscopy and density functional theory calculations, the study showed that submonolayers of Pd undergo spontaneous alloy formation on Cu(110) and Cu(100) surfaces in vacuum, whereas they do not form an alloy on Cu(111). Upon heating in H2, inward diffusion of Pd into the Cu lattice is favored, facilitating alloying on all Cu surfaces. Under CO2 hydrogenation reaction conditions, the alloying trend becomes stronger, promoted by the reaction intermediate HCOO*, especially on Pd/Cu(110). This work demonstrates that surface alloying may be a key factor in the enhancement of the catalytic activity of PdCu catalysts as compared to their monometallic counterparts. Furthermore, it sheds light on the hydrogen activation mechanism during catalytic hydrogenation on copper-based catalysts.

Surface Segregation in CuNi Nanoparticle Catalysts During CO2 Hydrogenation: The Role of CO in the Reactant Mixture.

Surface segregation and restructuring in size-selected CuNi nanoparticles were investigated via near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) at various temperatures in different gas environments. Particularly in focus were structural and morphological changes occurring under CO2 hydrogenation conditions in the presence of carbon monoxide (CO) in the reactant gas mixture. Nickel surface segregation was observed when only CO was present as adsorbate. The segregation trend is inverted in a reaction gas mixture consisting of CO2, H2, and CO, resulting in an increase of copper concentration on the surface. Density functional theory calculations attributed the inversion of the segregation trend to the formation of a stable intermediate on the nanocatalyst surface (CH3O) in the CO-containing reactant mixture, which modifies the nickel segregation energy, thus driving copper to the surface. The promoting role of CO for the synthesis of methanol was demonstrated by catalytic characterization measurements of silica-supported CuNi NPs in a fixed-bed reactor, revealing high methanol selectivity (over 85%) at moderate pressures (20 bar). The results underline the important role of intermediate reaction species in determining the surface composition of bimetallic nanocatalysts and help understand the effect of CO cofeed on the properties of CO2 hydrogenation catalysts.

Operando Evolution of the Structure and Oxidation State of Size-Controlled Zn Nanoparticles during CO2 Electroreduction.

We explored the size-dependent activity and selectivity of Zn nanoparticles (NPs) for the electrochemical CO2 reduction reaction (CO2RR). Zn NPs ranging from 3 to 5 nm showed high activity and selectivity (∼70%) for CO production, whereas those above 5 nm exhibited bulk-like catalytic properties. In addition, a drastic increase in hydrogen production was observed for the Zn NPs below 3 nm, which is associated with the enhanced content of low-coordinated sites on small NPs. The presence of residual cationic Zn species in the catalysts was also revealed during CO2RR via operando X-ray absorption fine-structure spectroscopy measurements. Such species are expected to play a role in the selectivity trends obtained. Our findings can serve as guidance for the development of highly active and CO-selective Zn-based catalysts for CO2RR.

Segregation Phenomena in Size-Selected Bimetallic CuNi Nanoparticle Catalysts.

Surface segregation, restructuring, and sintering phenomena in size-selected copper-nickel nanoparticles (NPs) supported on silicon dioxide substrates were systematically investigated as a function of temperature, chemical state, and reactive gas environment. Using near-ambient pressure (NAP-XPS) and ultrahigh vacuum X-ray photoelectron spectroscopy (XPS), we showed that nickel tends to segregate to the surface of the NPs at elevated temperatures in oxygen- or hydrogen-containing atmospheres. It was found that the NP pretreatment, gaseous environment, and oxide formation free energy are the main driving forces of the restructuring and segregation trends observed, overshadowing the role of the surface free energy. The depth profile of the elemental composition of the particles was determined under operando CO2 hydrogenation conditions by varying the energy of the X-ray beam. The temperature dependence of the chemical state of the two metals was systematically studied, revealing the high stability of nickel oxides on the NPs and the important role of high valence oxidation states in the segregation behavior. Atomic force microscopy (AFM) studies revealed a remarkable stability of the NPs against sintering at temperatures as high as 700 °C. The results provide new insights into the complex interplay of the various factors which affect alloy formation and segregation phenomena in bimetallic NP systems, often in ways different from those previously known for their bulk counterparts. This leads to new routes for tuning the surface composition of nanocatalysts, for example, through plasma and annealing pretreatments.