EFFECT OF THE ORDER OF APPLICATION OF COPPER AND ZINC COMPOUNDS ON CATALYTIC ACTIVITY IN CO-OXIDATION REACTION

На основе алюмохромового носителя экстракционно-пиролитическим методом (ЭПМ) синтезирована серия медь-цинк-содержащих катализаторов, проанализировано влияние порядка внесения активных компонентов (меди и цинка) на качественный и количественный состав формирующихся фаз, а также глубину размещения в гранулах носителя, структурно-прочностные и каталитические свойства образцов в процессе окисления СО кислородом воздуха.Рассчитанные значения энергии активации и предэкспоненциального множителя показали, что порядок внесения активных компонентов оказывает влияние на кинетические параметры: наилучшие результаты наблюдаются в образцах, полученных совместной пропиткой алюмохромового носителя. Константы скорости реакции окисления СО (k), рассчитанные во всем температурном диапазоне более, чем в 2 раза выше, чем при совместном внесении компонентов.С помощью электронно-зондового микроанализа изучена текстура поверхности катализаторов ЭПМ, установлено, что не смотря на порядок внесения, технология ЭПМ позволяет получать однородную текстуру - поверхность катализаторов характеризуется слоистостью с частицами чешуйчатой структуры с минимальным размером частиц во всех случаях 0,4 мкм, максимальным 3-4 мкм. Совместное внесение Zn и Cu обеспечивает равномерное распределение компонентов по сечению гранул катализаторов.Результаты работы могут быть применимы для построения технологии эффективных медь-цинковых катализаторов для процессов окисления. Область применения катализаторов – комплексная очистка многокомпонентных газовых смесей. A series of copper-zinc-containing catalysts was synthesized on the aluminum - chromium oxides base by the extraction -pyrolytic method (EPM), the effect of the order application active components (copper and zinc) on the qualitative and quantitative composition of the formed phases, as well as the depth of placement in the granules of the baser, structural strength and catalytic properties of samples during oxidation with air oxygen was analyzed.The calculated values of the activation energy and the pre-exponential multiplier showed that the order of application active components has an effect on the kinetic parameters: the best results are observed in samples obtained by joint impregnation of an alumochrome carrier. The rate constants oxidation CO (k) calculated over the entire temperature range are more than 2 times higher when the components added together.Using electron probe microanalysis, the surface texture of the EPM catalysts was studied, it was found that, despite the order of application, the EPM technology allows to obtain a homogeneous texture - the surface of the catalysts is characterized by layering with particles of a scaly structure with a minimum particle size of 0.4 microns in all cases, and maximum 3-4 microns. The joint application of Zn and Cu ensures an even distribution of the components over the cross section of the catalyst granules.The results of the work can be applied to build a technology of effective copper-zinc catalysts for oxidation processes. The scope of application of catalysts is complex purification of multicomponent gas mixtures.

The Regulation of O2 Spin State and Direct Oxidation of CO at Room Temperature Using Triboelectric Plasma by Harvesting Mechanical Energy

Oxidation reactions play a critical role in processes involving energy utilization, chemical conversion, and pollutant elimination. However, due to its spin-forbidden nature, the reaction of molecular dioxygen (O2) with a substrate is difficult under mild conditions. Herein, we describe a system that activates O2 via the direct modulation of its spin state by mechanical energy-induced triboelectric corona plasma, enabling the CO oxidation reaction under normal temperature and pressure. Under optimized reaction conditions, the activity was 7.2 μmol h−1, and the energy consumption per mole CO was 4.2 MJ. The results of kinetic isotope effect, colorimetry, and density functional theory calculation studies demonstrated that electrons generated in the triboelectric plasma were directly injected into the antibonding orbital of O2 to form highly reactive negative ions O2−, which effectively promoted the rate-limiting step of O2 dissociation. The barrier of the reaction of O2− ions and CO molecular was 3.4 eV lower than that of O2 and CO molecular. This work provides an effective strategy for using renewable and green mechanical energy to realize spin-forbidden reactions of small molecules.

High Catalytic Activity of Pt/Al2O3 Catalyst in CO Oxidation at Room Temperature—A New Insight into Strong Metal–Support Interactions

The concept of very strong metal–support interactions (VSMSI) was defined in regard to the interactions that influence the catalytic properties of catalysts due to the creation of a new phase as a result of a solid-state chemical reaction between the metal and support. In this context, the high catalytic activity of the 1%Pt/Al2O3 catalyst in the CO oxidation reaction at room temperature was explained. The catalyst samples were reduced at different temperatures ranging from 500 °C to 800 °C and characterized using TPR, O2/H2 titration, CO chemisorption, TPD-CO, FTIR-CO, XRD, and TOF-SIMS methods. Based on the obtained results, it was claimed that with very high temperature reduction (800 °C), nonstoichiometric platinum species [Pt(Cl)Ox] strongly anchored to Al2O3 surface are formed. These species act as the oxygen adsorption sites.

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Model Hamiltonians based on the so-called cluster expansion (CE), which consist of a linear fit of parameters corresponding to geometric patterns, provide an efficient and rigorous means to quickly evaluate the energy of diverse arrangements of adsorbate mixtures on reactive surfaces as typically relevant for heterogeneous catalysis. However, establishing the model Hamiltonian is a tedious task, requiring the construction and optimization of many geometries. Today, most of these geometries are constructed by hand, based on chemical intuition or random choices. Hence, the quality of the training set is unlikely to be optimal and its construction is not reproducible. Herein, we propose a reformulation of the construction of the training set as a strategy-based game, aiming at an efficient exploration of the relevant patterns constituting the model Hamiltonian. Based on this reformulation, we exploit a typical active learning solution for machine-learning such a strategy game: an upper confidence tree (UCT) based framework. However, in contrast to standard games, evaluating the true score is computationally expensive, as it requires a costly geometry optimization. Hence, we augment the UCT with a pre-exploration step inspired by the variance-based Design of Experiments (DoE) methods. This novel mixed UCT+DoE framework allows to automatically construct a well adapted training set, minimizing computational cost and user-intervention. As a proof of principle, we apply our UCT+DoE approach on the CO oxidation reaction on Pd(111), for which a relevant model Hamiltonian has been established previously. The results demonstrate the effectiveness of the custom built UCT and its significant benefits on a DoE-based approach.

Kinetics and Thermodynamics of CO Oxidation by (TiO2)6

Molecular level insights into the mechanism and thermodynamics of CO oxidation by a (TiO2)6 cluster have been obtained through density functional calculations. Thereby, in this study, as an example, two different structural isomers of (TiO2)6 are considered with the purpose of understanding the interplay between local structure and activity for the CO oxidation reaction. Active sites in the two isomeric forms were identified on the basis of global and local reactivity descriptors. For the oxidation of CO to CO2, the study considered both sequential and simultaneous adsorption of CO and O2 on (TiO2)6 cluster through the ER and LH mechanisms, respectively. Three different pathways were obtained for CO oxidation by (TiO2)6 cluster, and the mechanistic route of each pathway were identified by locating the transition-state and intermediate structures. The effect of temperature on the rate of the reaction was investigated within the harmonic approximation. The structure-dependent activity of the cluster was rationalized through reactivity descriptors and analysis of the frontier orbitals.

Activation of Pt Nanoclusters on TiO2 via Tuning the Metallic Sites to Promote Low-Temperature CO Oxidation

Metallic Pt sites are imperative in the CO oxidation reaction. Herein, we demonstrate the tuning of Pt sites by treating a Pt catalyst in various reductive atmospheres, influencing the catalyst activities in low-temperature CO oxidation. The H2 pretreatment of Pt clusters at 200 °C decreases the T50 from 208 °C to 183 °C in the 0.1 wt % Pt/TiO2 catalyst. The T50 shows a remarkable improvement using a CO pretreatment, which decreases the T50 further to 135 °C. A comprehensive characterization study reveals the integrated reasons behind this phenomenon: (i) the extent of PtO transition to metallic Pt sites, (ii) the ample surface active oxygen triggered by metallic Pt, (iii) the CO selectively adsorbs on metallic Pt sites which participate in low-temperature CO oxidation, and (iv) the formation of the unstable intermediate such as bicarbonate, contributes together to the enhanced activity of CO pretreated Pt/TiO2.

Stroboscopic operando spectroscopy of the dynamics in heterogeneous catalysis by event-averaging

Heterogeneous catalyst surfaces are dynamic entities that respond rapidly to changes in their local gas environment, and the dynamics of the response is a decisive factor for the catalysts’ action and activity. Few probes are able to map catalyst structure and local gas environment simultaneously under reaction conditions at the timescales of the dynamic changes. Here we use the CO oxidation reaction and a Pd(100) model catalyst to demonstrate how such studies can be performed by time-resolved ambient pressure photoelectron spectroscopy. Central elements of the method are cyclic gas pulsing and software-based event-averaging by image recognition of spectral features. A key finding is that at 3.2 mbar total pressure a metallic, predominantly CO-covered metallic surface turns highly active for a few seconds once the O2:CO ratio becomes high enough to lift the CO poisoning effect before mass transport limitations triggers formation of a √5 oxide.

Rationally Tailoring Catalysts for the CO Oxidation Reaction by Using DFT Calculations

Rational design of catalysts by tailoring specific surface sites with different elements could result in catalysts with high activity, selectivity and stability. In this work, we show that *CO on-top and O* on-top adsorption energies are good descriptors for catalysis of the CO oxidation reaction (COOR) on pure metals and binary alloys. The observed Brønsted-Evans-Polanyi (BEP) and scaling relations for COOR on different surfaces are incorporated into a predictive model that uses the binding strength of the four adjacent metal atoms making up the active site for COOR catalysis to estimate reaction and activation energies. The model is used to screen 161 multi-metallic catalyst candidates made by combining Ru, Pt, Pd, Cu and Au at these four sites. The screening and subsequent calculations suggest that Ru-Pt-Cu alloys are good catalysts for COOR. Our study shows that it is possible to use information from pure metals and binary alloys to predict the catalytic behavior of more complex alloys, and hereby reduce the computational cost of identifying new catalyst candidates for COOR.

Exploring the Relationship between the CO Oxidation Reaction and the Elemental Composition in Electrocatalysts with Machine Learning - a Case Study of PtRuPdRhAu High Entropy Alloys

In recent years, the development of complex multi-metallic nanomaterials like high entropy alloy (HEA) catalysts has gained popularity. Composed of 5 or more metals, the compositions of HEAs exhibit extreme diversity. This is both a promising avenue to identify new catalysts and a severe constraint on their preparation and study. To address the challenges related to the preparation, study and optimization of HEAs, machine learning solutions are attractive. In this paper, the composition of PtRuPdRhAu hydrogen oxidation catalysts is optimized for the CO oxidation reaction. This is achieved by constructing a dataset using Bayesian optimization as guidance. For this quinary nanomaterial, the best performing composition was found within the first 35 experiments. However, the dataset was expanded until a total of 68 samples were investigated. This final dataset was used to construct a random forest regression model and a linear model. These machine learned models were used to assess the relationships between the concentrations of the consituent elements and the CO oxidation reaction onset potential. The onset potentials were found to correlate with the composition dependent adsorption energy of *OH obtained from density functional theory. This study demonstrates, how machine learning can be employed in an experimental setting to investigate the vast compositional space of HEAs.