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The performance of Brønsted- and Lewis-acidic La, Nb, and Zr phosphates (LaPO, NbPO, and ZrPO) during the aqueous phase conversion of dihydroxyacetone (DHA) to lactic acid (LA) is investigated using a fixed-bed reactor. Mass-transfer phenomena are thoroughly investigated, and the masstransfer coefficient is deconvoluted from the intrinsic kinetic constant for each catalyst to enable the quantitative assessment of both. NbPO is found to be masstransfer-limited. Despite this limitation, NbPO shows the highest yield of LA at 36%. The reaction over ZrPO is not transport-limited, allowing for a rigorous analysis of intrinsic kinetics. This analysis shows that the conversion of DHA into pyruvaldehyde (PVA) follows a second-order reaction mechanism via a dimeric intermediate, which consolidates previous reports in the literature. Additionally, a correlation between LA production and the carbon missing from the carbon balance (carbon loss) is observed. Finally, NbPO and ZrPO show stable performance up to 10 h on stream at 150 °C. After 15 h of reaction, the PVA yield increases at the expense of LA with NbPO. This is ascribed to the deactivation of the active sites necessary to produce LA, which are different from the sites that produce PVA.This hypothesis is supported by the characterization of the spent catalyst with 13C magic-angle spinning nuclear magnetic resonance and attenuated total reflectance infrared spectroscopy.

Synthesis of ammonia through photo- and electrocatalysis is a rapidly growing field. Titania-based catalysts are widely reported for photocatalytic ammonia synthesis and have also been suggested as electrocatalysts. The addition of transition-metal dopants is one strategy for improving the performance of titania-based catalysts. In this work, we screen d-block transition-metal dopants for surface site stability and evaluate trends in their performance as the active site for the reduction of nitrogen to ammonia on TiO$_2$. We find a linear relationship between the d-band center and formation energy of the dopant site, while the binding energies of N$_2$, N$_2$H, and NH$_2$ all are strongly correlated with the cohesive energies of the dopant metals. The activity of the metal-doped systems shows a volcano type relationship with the NH$_2$ and N$_2$H energies as descriptors. Some metals such as Co, Mo, and V are predicted to slightly improve photo- and electrocatalytic performance, but most metals inhibit the ammonia synthesis reaction. The results provide insight into the role of transition-metal dopants for promoting ammonia synthesis, and the trends are based on unexpected electronic structure factors that may have broader implications for single-atom catalysis and doped oxides.

Understanding surface reactions of biomass-derived oxygenates on metal oxides is important for designing catalysts for valorization of biomass. This work elucidated the effect of different pretreat...

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. This approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data., Pre-print for first submission

An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sensitivity analyses and PDE-constrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate descriptions of microkinetic models. The mathematical and structural details of TAPsolver are outlined, as well as validation of the forward and inverse problems against well-defined prototype problems. Benchmarks of the code are presented, and a case study for extracting kinetic parameters that do not violate thermodynamics from experimental TAP measurements of CO oxidation on supported platinum particles is presented. TAPsolver will act as a foundation for future development and dissemination of TAP data processing techniques.

Zeolites are nanoporous aluminosilicates widely used in catalysis and separations applications. Though generally formed as 3D crystals, new synthesis techniques have given access to 2D zeolite nanosheets with small diffusion path lengths and accelerated molecular diffusion. Since most previous research has focused on bulk zeolite crystals, there is little understanding of the surface adsorption and diffusion mechanisms likely involved at such length scales and their contributions to the permeability and selectivity of different species. To enable the systematic examination of such surface properties, we constructed a database of more than 800,000 computation-ready 2D zeolite nanosheets from the full range of known zeolite structures in the IZA database of zeolite structure types. The nanosheet surfaces cover a wide range of orientations and were created via the principle of minimizing the number of bonds broken during the termination of a unit cell. The database consists of two sets of nanosheets: one set...

Photo-catalytic fixation of nitrogen by titania catalysts at ambient conditions has been reported for decades, yet the active site capable of adsorbing an inert N2 molecule at ambient pressure and the mechanism of dissociating the strong dinitrogen triple bond at room temperature remain unknown. In this work in situ near-ambient-pressure X-ray photo-electron spectroscopy and density functional theory calculations are used to probe the active state of the rutile (110) surface. The experimental results indicate that photon-driven interaction of N2 and TiO2 is observed only if adventitious surface carbon is present, and computational results show a remarkably strong interaction between N2 and carbon substitution (C*) sites that act as surface-bound carbon radicals. A carbon-assisted nitrogen reduction mechanism is proposed and shown to be thermodynamically feasible. The findings provide a molecular-scale explanation for the long-standing mystery of photo-catalytic nitrogen fixation on titania. The results suggest that controlling and characterizing carbon-based active sites may provide a route to engineering more efficient photo(electro)-catalysts and improving experimental reproducibility.

Methanol is a major fuel and chemical feedstock currently produced from syngas, a CO/CO2/H2 mixture. Herein we identify formate binding strength as a key parameter limiting the activity and stability of known catalysts for methanol synthesis in the presence of CO2. We present a molybdenum phosphide catalyst for CO and CO2 reduction to methanol, which through a weaker interaction with formate, can improve the activity and stability of methanol synthesis catalysts in a wide range of CO/CO2/H2 feeds.

Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, i...

Using a combined density functional theory and descriptor based microkinetic model approach, we predict production rate volcanos for higher oxygenate formation on (111) transition-metal surfaces. Despite their lower activity for CO conversion compared to stepped surfaces, (111) transition metal surfaces bring the potential for selectivity toward C2+ oxygenates. The volcano plots can be used to rationalize and predict activity and selectivity trends for transition-metal-based catalysts.

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With structural and mechanistic complexities of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, data from an experimental CO oxidation on a platinum catalyst was analyzed to reveal that Langmuir-Hinshelwood mechanism drives the reaction. As oxygen accumulated on the catalyst, a transition in the apparent kinetics was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

We present a generic analysis of the implications of energetic scaling relations on the possibilities for bifunctional gains at homogeneous bimetallic alloy catalysts. Such catalysts exhibit a large number of interface sites, where second-order reaction steps can involve intermediates adsorbed at different active sites. Using different types of model reaction schemes, we show that such site-coupling reaction steps can provide bifunctional gains that allow for a bimetallic catalyst composed of two individually poor catalyst materials to approach the activity of the optimal monomaterial catalyst. However, bifunctional gains cannot result in activities higher than the activity peak of the monomaterial volcano curve as long as both sites obey similar scaling relations, as is generally the case for bimetallic catalysts. These scaling-relation-imposed limitations could be overcome by combining different classes of materials such as metals and oxides.

Over the last century, the industrialization of agriculture and the consumption of fossil fuels have resulted in a significant imbalance and redistribution in nitrogen-containing resources. This has sparked an interest in developing more sustainable and resilient approaches for producing nitrogen-containing commodities such as fertilizers and fuels. One largely neglected but emerging approach is photocatalytic nitrogen fixation. There is significant evidence that this process occurs spontaneously in terrestrial settings, and it has been demonstrated in numerous engineered systems. Yet many questions still remain unanswered regarding the rates, mechanisms, and impacts of photocatalytically producing fixed nitrogen “out of thin air”. This work reviews the fascinating history of the reaction and examines current progress toward understanding and improving photofixation of nitrogen. This is supplemented by a quantitative review of the thermodynamic considerations and limitations for various reaction mechanism...

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations., Finding catalyst mechanisms remains a challenge due to the complexity of hydrocarbon chemistry. Here, the authors shows that scaling relations and machine-learning methods can focus full-accuracy methods on the small subset of rate-limiting reactions allowing larger reaction networks to be treated.

Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few “descriptors” upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem—preferably to 1 or 2 descriptors—thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The “CatMAP” Python module seeks to standardize and automate many of the mathematical routines necessary to move from “descriptor space” to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A “reaction model” can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction.

The potential of bimetallic Pt–Co catalysts for production of higher alcohols in high pressure CO hydrogenation has been assessed. Two catalysts (Pt3Co/SiO2 and PtCo/SiO2) were tested, and the existing literature on CO hydrogenation over Pt–Co catalysts was reviewed. It is found that the catalysts produce mainly methanol in the Pt-rich composition range and mainly hydrocarbons (and to a modest extent higher alcohols) in the Co-rich composition range. The transition between the two types of behavior occurs in a narrow composition range around a molar Pt:Co ratio of 1:1.

Production of higher alcohols directly from synthesis gas is an attractive chemical process due to the high value of alcohols as fuel blends and the numerous possibilities for production of synthesis gas. Despite years of research the industrial viability of such a process is severely limited due to lack of suitable catalysts. In this work we contribute to an understanding why it has been difficult to find transition-metal higher alcohol catalysts, and point to possible strategies for discovering new active and selective catalysts. Our analysis is based on extensive density functional theory calculations to determine the energetics of ethanol formation on a series of metal (211) surfaces. The energetic information is used to construct a mean-field micro-kinetic model for the formation of ethanol via CHx–CO coupling. The kinetic model is used along with a descriptor-based analysis to gain insight into the fundamental factors determining activity and selectivity on transition-metal surfaces.

Synthesis gas (CO + H2) conversion is a promising route to converting coal, natural gas, or biomass into synthetic liquid fuels. Rhodium has long been studied as it is the only elemental catalyst that has demonstrated selectivity to ethanol and other C2+ oxygenates. However, the fundamentals of syngas conversion over rhodium are still debated. In this work a microkinetic model is developed for conversion of CO and H2 into methane, ethanol, and acetaldehyde on the Rh (211) and (111) surfaces, chosen to describe steps and close-packed facets on catalyst particles. The model is based on DFT calculations using the BEEF-vdW functional. The mean-field kinetic model includes lateral adsorbate-adsorbate interactions, and the BEEF-vdW error estimation ensemble is used to propagate error from the DFT calculations to the predicted rates. The model shows the Rh(211) surface to be ∼6 orders of magnitude more active than the Rh(111) surface, but highly selective toward methane, while the Rh(111) surface is intrinsically selective toward acetaldehyde. A variety of Rh/SiO2 catalysts are synthesized, tested for catalytic oxygenate production, and characterized using TEM. The experimental results indicate that the Rh(111) surface is intrinsically selective toward acetaldehyde, and a strong inverse correlation between catalytic activity and oxygenate selectivity is observed. Furthermore, iron impurities are shown to play a key role in modulating the selectivity of Rh/SiO2 catalysts toward ethanol. The experimental observations are consistent with the structure-sensitivity predicted from theory. This work provides an improved atomic-scale understanding and new insight into the mechanism, active site, and intrinsic selectivity of syngas conversion over rhodium catalysts and may also guide rational design of alloy catalysts made from more abundant elements.

Bifunctional coupling of two different catalytic site types has often been invoked to explain experimentally observed enhanced catalytic activities. We scrutinize such claims with generic scaling-relation-based microkinetic models that allow exploration of the theoretical limits for such a bifunctional gain for several model reactions. For sites at transition-metal surfaces, the universality of the scaling relations between adsorption energies largely prevents any improvements through bifunctionality. Only the consideration of systems that involve the combination of different materials, such as metal particles on oxide supports, offers hope for significant bifunctional gains.

Gold is known to become significantly more catalytically active as its particle size is reduced, and other catalysts are also known to exhibit finite-size effects. To understand the trends related to finite-size effects, we have used density functional theory to study adsorption of representative adsorbates, CO and O, on the late transition metals Co, Ni, Cu, Ir, Pd, Ag, Rh, Pt and Au. We studied adsorption energies and geometries on 13-atom clusters and compared them to the fcc(111) and fcc(211) crystal facets. In all cases, adsorbates were found to bind significantly more strongly to the 13-atom clusters than to the extended surfaces. The binding strength of both adsorbates were found to correlate very strongly with the average coordination number of the metal atoms to which the adsorbate binds, indicating that the finite-size effects in bonding are not specific to gold.

Porous carbon nanofibers with large accessible surface areas and well-developed pore structures were prepared by electrospinning and subsequent thermal and chemical treatments. They were directly used as anodes in lithium-ion batteries without adding any non-active materials such as polymer binders or electronic conductors. The electrochemical performance results show that porous carbon nanofiber anodes have improved lithium-ion storage ability, enhanced charge–discharge kinetics, and better cyclic stability compared with non-porous counterparts. The unique structures and properties of these materials make them excellent candidates for use as anodes in high-performance rechargeable lithium-ion batteries.

Mn oxide-loaded porous carbon nanofibers are prepared by electrospinning polyacrylonitrile nanofibers containing different amounts of Mn(CH3COO)2, followed by thermal treatments in different environments. It is found that the manganese salt may transform into γ-Mn(OOH)2 or other Mn compounds during the thermal oxidation in air environment, while further thermal treatment in argon atmosphere results in MnO and Mn3O4 particles confined to a nanoporous carbon structure. Surface morphology, thermal properties and crystal structures are characterized using various analytical techniques to provide insight into the formation mechanism of the porous structure. These Mn oxide-loaded porous carbon composite nanofibers exhibit high reversible capacity, improved cycling performance, and elevated rate capability even at high current rates when used as anodes for rechargeable lithium-ion batteries without adding any polymer binder or electronic conductor.

Si nanoparticle-incorporated polyacrylonitrile (PAN) fibers are prepared using the electrospinning method and Si-filled carbon (Si/C) fibers are obtained by the subsequent heat treatment of these Si/PAN fibers. Their microstructures are characterized by various analytical techniques. It is found that Si nanoparticles are distributed both inside and on the surface of PAN fibers and this is preserved after the formation of Si/C fibers. The crystal structure characterization indicates that, in Si/C fibers, Si nanoparticles exist in a crystalline state while carbon is in a predominantly amorphous or disordered form. Si/C fibers show high reversible capacity and good capacity retention when tested as anodes in lithium ion batteries (LIBs). The excellent electrochemical performance of these fibers can be ascribed to the combined contributions of carbon matrices and Si nanoparticles, and the favorable textures and surface properties of the Si/C fibers.