Project description:A theory of nonlinear response of chemical kinetics, in which multiple perturbations are used to probe the time evolution of nonlinear chemical systems, is developed. Expressions for nonlinear chemical response functions and susceptibilities, which can serve as multidimensional measures of the kinetic pathways and rates, are derived. A new class of multidimensional measures that combine multiple perturbations and measurements is also introduced. Nonlinear fluctuation-dissipation relations for steady-state chemical systems, which replace operations of concentration measurement and perturbations, are proposed. Several applications to the analysis of complex reaction mechanisms are provided.
Project description:Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts' local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.
Project description:Access to the potential energy Hessian enables determination of the Gibbs free energy and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned potentials (MLPs) determine the Hessian with great success (58 cm-1 mean absolute error (MAE)) for intermediates adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models for the aforementioned applications. The top performing model, with a simple offset correction, gives good estimations of the vibrational entropy contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The ability to leverage models to capture the translational entropy was also explored. It was determined that 94% of randomly sampled systems had a translational entropy greater than 0.1 eV at 300 K. This underscores the need to go beyond the harmonic approximation to consider the entropy introduced by adsorbate translation, which increases with temperature. Lastly, we used MLP-determined Hessian information for transition state search and found we were able to reduce the number of unconverged systems by 65 to 93% overall convergence, improving on the baseline established by CatTSunami.
Project description:Heterogeneous chemical processes occupy a pivotal position in many fields of applied chemistry. Monitoring reaction kinetics in such heterogeneous systems together with challenges associated with ex-situ analytical methodologies can lead to inaccurate information about the nature of the catalyst surfaces as well as information about the steps involved. The present work explores the possibility of kinetic measurements of chemical reactions and adsorption processes of homogeneous and heterogeneous systems through the variation of RGB intensities of digital images using a smartphone combined with a program written in Python to accelerate and facilitate data acquisition. In order to validate the method proposed, the base promoted hydrolysis of 4-nitrophenyl acetate was initially investigated. The rate constants obtained through RGB analysis (0.01854 min-1) is almost identical to that using traditional UV-Vis spectroscopy (0.01848 min-1). The proposed method was then applied to monitor the kinetics of three heterogeneous processes: (1) reduction of 4-nitrophenolate in the presence of dispersed Pd/C; (2) decomposition of methyl orange with TiO2; and (3) adsorption of rhodamine on montmorillonite. In general, the method via digital images showed high reproducibility and analytical frequency, allowing the execution of simultaneous analyses, with an accuracy comparable to UV-Vis spectrophotometry. The method developed herein is a practical and valuable alternative for obtaining kinetic data of heterogeneous reactions and processes where a color change is involved, bypassing sampling collection and processing which decreases analytical frequency and may lead to data errors.
Project description:Anthropogenic climate change urgently calls for the greening and intensification of the chemical industry. Most chemical reactors make use of catalysts to increase their conversion yields, but their operation at steady-state temperatures limits their rate, selectivity, and energy efficiency. Here, we show how to break such a steady-state paradigm using ultrashort light pulses and photothermal nanoparticle arrays to modulate the temperature of catalytic sites at timescales typical of chemical processes. Using heat dissipation and time-dependent microkinetic modeling for a number of catalytic landscapes, we numerically demonstrate that pulsed photothermal catalysis can result in a favorable, dynamic mode of operation with higher energy efficiency, higher catalyst activity than for any steady-state temperature, reactor operation at room temperature, resilience against catalyst poisons, and access to adsorbed reagent distributions that are normally out of reach. Our work identifies the key experimental parameters controlling reaction rates in pulsed heterogeneous catalysis and provides specific recommendations to explore its potential in real experiments, paving the way to a more energy-efficient and process-intensive operation of catalytic reactors.
Project description:The performance of heterogeneous catalysts for electrocatalytic CO2 reduction suffers from unwanted side reactions and kinetic inefficiencies at the required large overpotential. However, immobilized CO2 reduction enzymes-such as formate dehydrogenase-can operate with high turnover and selectivity at a minimal overpotential and are therefore 'ideal' model catalysts. Here, through the co-immobilization of carbonic anhydrase, we study the effect of CO2 hydration on the local environment and performance of a range of disparate CO2 reduction systems from enzymatic (formate dehydrogenase) to heterogeneous systems. We show that the co-immobilization of carbonic anhydrase increases the kinetics of CO2 hydration at the electrode. This benefits enzymatic CO2 reduction-despite the decrease in CO2 concentration-due to a reduction in local pH change, whereas it is detrimental to heterogeneous catalysis (on Au) because the system is unable to suppress the H2 evolution side reaction. Understanding the role of CO2 hydration kinetics within the local environment on the performance of electrocatalyst systems provides important insights for the development of next-generation synthetic CO2 reduction catalysts.
Project description:The ever-increasing landscape of heterogeneous catalysis, pure and applied, utilizes many different catalysts. Academic insights along with many industrial adaptations paved the way for the growth. In designing a catalyst, it is desirable to have a priori knowledge of what structure needs to be targeted to help in achieving the goal. When focusing on catalysis, one needs to cope with a vast corpus of knowledge and information. The overwhelming desire to exploit catalysis toward commercial ends is irresistible. In today's world, one of the requirements of developing a new catalyst is to address the environmental concerns. The well-established heterogeneous catalysts have microporous structures (<25 Å), which find use in many industrial processes. The metal-organic framework (MOF) compounds, being pursued vigorously during the last two decades, have similar microporosity with well-defined pores and channels. The MOFs possess large surface area and assemble to delicate structural and compositional variations either during the preparation or through postsynthetic modifications (PSMs). The MOFs, in fact, offer excellent scope as simple Lewis acidic, Brönsted acidic, Lewis basic, and more importantly bifunctional (acidic as well as basic) agents for carrying out catalysis. The many advances that happened over the years in biology helped in the design of many good biocatalysts. The tools and techniques (advanced preparative approaches coupled with computational insights), on the other hand, have helped in generating interesting and good inorganic catalysts. In this review, the recent advances in bifunctional catalysis employing MOFs are presented. In doing so, we have concentrated on the developments that happened during the past decade or so.
Project description:Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.
Project description:The efficient transformation of carbon dioxide into useful chemical feedstock is of great significance, attracting intense research interest. The widely studied porous-coordinated polymers possess large pores to adsorb guest molecules and further allow the contact and to transfer the substrate molecule within their microenvironment. Here we present the synthesis of a silver-based metal-organic frameworks (MOFs) material with a three-dimensional structure by incorporating a tetraphenyl-ethylene moiety as the four-point connected node via the solvothermal method. This polymer exhibits as an efficient heterogeneous catalyst for the carboxylative cyclization of CO2 to α-methylene cyclic carbonates in excellent yields. Moreover, the introduction of silver (Ag (I)) chains in this framework shows the specific alkynophilicity to activate C≡C bonds in propargylic alcohols to greatly accelerate the efficient conversion, and the large pores in the catalyst exhibit a size-selective catalytic performance.
Project description:The generation of immobilised oxidase biocatalysts allowing multifunctional oxidation of valuable chemicals using molecular oxygen is described. Engineered galactose oxidase (GOase) variants M1 and M3-5, an engineered choline oxidase (AcCO6) and monoamine oxidase (MAO-N D9) displayed long-term stability and reusability over several weeks when covalently attached on a solid support, outperforming their free counterparts in terms of stability (more than 20 fold), resistance to heat at 60 °C, and tolerance to neat organic solvents such as hexane and toluene. These robust heterogenous oxidation catalysts can be recovered after each reaction and be reused multiple times for the oxidation of different substrates.