Effect of Dynamic and Preferential Decoration of Pt Catalyst Surfaces by WOx on Hydrodeoxygenation Reactions

Catalysts containing Pt nanoparticles and reducible transition-metal oxides (WOx, NbOx, TiOx) exhibit remarkable selectivity to aromatic products in hydrodeoxygenation (HDO) reactions for biomass valorization, contrasting the undesired aromatic hydrogenation typically observed for metal catalysts. However, the active site(s) responsible for the high selectivity remains elusive. Here, theoretical and experimental analyses are combined to explain the observed HDO reactivity by interrogating the organization of reduced WOx domains on Pt surfaces at sub-monolayer coverage. The SurfGraph algorithm is used to develop model structures that capture the configurational space (∼1000 configurations) for density functional theory (DFT) calculations of a W3O7 trimer on stepped Pt surfaces. Machine-learning models trained on the DFT calculations identify the preferential occupation of well-coordinated Pt sites (≥8 Pt coordination number) by WOx and structural features governing WOx-Pt stability. WOx/Pt/SiO2 catalysts are synthesized with varying W loadings to test the theoretical predictions and relate them to HDO reactivity. Spectroscopy- and microscopy-based catalyst characterizations identify the dynamic and preferential decoration of well-coordinated sites on Pt nanoparticles by reduced WOx species, consistent with theoretical predictions. The catalytic consequences of this preferential decoration on the HDO of a lignin model compound, dihydroeugenol, are clarified. The effect of WOx decoration on Pt nanoparticles for HDO involves WOx inhibition of aromatic ring hydrogenation by preferentially blocking well-coordinated Pt sites. The identification of preferential decoration on specific sites of late-transition-metal surfaces by reducible metal oxides provides a new perspective for understanding and controlling metal-support interactions in heterogeneous catalysis.

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

Hydrogenolysis of Poly(Ethylene-co-Vinyl Alcohol) and Related Polymer Blends over Ruthenium Heterogeneous Catalysts

The hydrogenolysis of polymers is emerging as a promising approach to deconstruct plastic waste into valuable chemicals. Yet, the complexity of plastic waste, including multilayer packaging, is a significant barrier to handling realistic waste streams. Herein, we reveal fundamental insights into a new chemical route for transforming a previously unaddressed fraction of plastic waste - poly(ethylene-co-vinyl alcohol) (EVOH) and related polymer blends - into alkane products. We report that Ru/ZrO2 is active for the concurrent hydrogenolysis, hydrogenation, and hydrodeoxygenation of EVOH and its thermal degradation products into alkanes (C1-C35) and water. Detailed reaction data, product analysis, and catalyst characterization reveal that the in-situ thermal degradation of EVOH forms aromatic intermediates that are detrimental to catalytic activity. Increased hydrogen pressure promotes hydrogenation of these aromatics, preventing catalyst deactivation and improving alkane product yields. Calculated apparent rates of C-C scission reveal that the hydrogenolysis of EVOH is slower than low-density polyethylene. We apply these findings to achieve hydrogenolysis of EVOH/polyethylene blends and elucidate the sensitivity of hydrogenolysis catalysts to such blends. Overall, we demonstrate progress towards efficient catalytic processes for the hydroconversion of waste multilayer film plastic packaging into valuable products.

Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen

Hydrogen cyanide (HCN) is synthesized from ammonia (NH3) and methane (CH4) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH4 and nitrogen (N2) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N2/CH4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH4 concentrations with ethane (C2H6) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH3) is a common intermediate in HCN and C2H6 synthesis. Compared to current industrial processes, N2/CH4 DBD plasma can achieve minimal CO2 emissions.

A Data and DFT-Driven Framework for Predicting the Microstructure of Submonolayer Inverse Metal Oxide on Metal Catalysts

Metal oxides on metal (inverse) catalysts can selectively drive many important reactions. However, understanding the active site under experimentally relevant conditions is lacking. Herein, we introduce a computational framework for predicting atomic models of stable inverse catalysts and demonstrate it for WOx on Pt(553) and a Pt79 nanoparticle at variable WOx coverages. An evolutionary algorithm identifies a small (5%) subset of promising atomic configurations on which DFT simulations are performed. We predict a maximum coverage of ∼50% WOx on Pt(553), consisting of small clusters (tetramers and pentamers), which preferentially reside on the terrace, with their oxygen atoms interacting with the Pt step sites. Consistently, WOx does not lie on curved and undercoordinated metal sites of Pt nanoparticles. The oxide clusters prefer a partially reduced oxidation state. Theoretical EXAFS spectra for select configurations provide insights into interpreting experimental spectra of inverse catalysts. The framework applies to other catalysts.

Tuning Active Site Flexibility by Defect Engineering of Graphene Ribbon Edge-hosted Fe-N3 Sites

Nitrogen-doped, carbon-supported transition metal catalysts are excellent for several reactions. Structural engineering of M-Nx sites to boost catalytic activity is rarely studied. Here, we demonstrate that the structural flexibility of Fe-N3 site is vital for tuning the electronic structure of Fe atoms and regulating the catalytic transfer hydrogenation (CTH) activity. By introducing carbon defects, we construct Fe-N3 sites with varying Fe-N bond lengths distinguishable by X-ray absorption spectroscopy. We investigate the CTH activity by density-functional theory and microkinetic calculations and reveal that the vertical displacement of the Fe atom out of the plane of the support, induced by the Fe-N3 distortion, raises the Fe3 d z 2 ${3{d}_{{z}^{2}}{\rm \ }}$ orbital and strengthens binding. We propose that the activity is controlled by the relaxation of the reconstructed site, which is further affected by Fe-N bond length, an excellent activity descriptor. We elucidate the origin of the CTH activity and principles for high-performing Fe-N-C catalysts by defect engineering.

Synthesis of Long Chain Oxygenates via Aldol Condensation of Furfural and Acetone over Metal-Organic Frameworks

Enormous efforts have been made to convert biomass to liquid fuels and products catalytically. Long molecules with a suitable structure are ideal precursors for fuels and value-added products. Here, a C21 oxygenate was synthesized for the first time in one step through aldol condensation of furfural and acetone over the amine-functionalized zirconium-based metal-organic framework (MOF), UiO-66-NH2. Structural changes of UiO-66-NH2 were investigated to improve the yield and evaluate the role of the ligand, cluster node, defectiveness, modulator, surface area, and textural properties on the product distribution. We demonstrate the possibility of making long-chain oxygenates without using vegetable oil-derived fatty acids toward 100% waste biomass-derived renewable fuels, lubricants, and surfactants.

Plasma-Enabled Ligand Removal for Improved Catalysis: Furfural Conversion on Pd/SiO2

A nonthermal, atmospheric He/O2 plasma (NTAP) successfully removed polyvinylpyrrolidone (PVP) from Pd cubic nanoparticles supported on SiO2 quickly and controllably. Transmission electron microscopy (TEM) revealed that the shape and size of Pd nanoparticles remain intact during plasma treatment, unlike mild calcination, which causes sintering and polycrystallinity. Using Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS), we demonstrate the quantitative estimation of the PVP plasma removal rate and control of the nanoparticle synthesis. First-principles calculations of the XPS and CO FTIR spectra elucidate electron transfer from the ligand to the metal and allow for estimates of ligand coverages. Reactivity testing indicated that PVP surface crowding inhibits furfural conversion but does not alter furfural selectivity. Overall, the data demonstrate NTAP as a more efficient method than traditional calcination for organic ligand removal in nanoparticle synthesis.

Temperature-dependent complex dielectric permittivity: a simple measurement strategy for liquid-phase samples

Microwaves (MWs) are an emerging technology for intensified and electrified chemical manufacturing. MW heating is intimately linked to a material's dielectric permittivity. These properties are highly dependent on temperature and pressure, but such datasets are not readily available due to the limited accessibility of the current methodologies to process-oriented laboratories. We introduce a simple, benchtop approach for producing these datasets near the 2.45 GHz industrial, medical, and scientific (ISM) frequency for liquid samples. By building upon a previously-demonstrated bireentrant microwave measurement cavity, we introduce larger pressure- and temperature-capable vials to deduce temperature-dependent permittivity quickly and accurately for vapor pressures up to 7 bar. Our methodology is validated using literature data, demonstrating broad applicability for materials with dielectric constant ε' ranging from 1 to 100. We provide new permittivity data for water, organic solvents, and hydrochloric acid solutions. Finally, we provide simple fits to our data for easy use.

Highly active, ultra-low loading single-atom iron catalysts for catalytic transfer hydrogenation

Highly effective and selective noble metal-free catalysts attract significant attention. Here, a single-atom iron catalyst is fabricated by saturated adsorption of trace iron onto zeolitic imidazolate framework-8 (ZIF-8) followed by pyrolysis. Its performance toward catalytic transfer hydrogenation of furfural is comparable to state-of-the-art catalysts and up to four orders higher than other Fe catalysts. Isotopic labeling experiments demonstrate an intermolecular hydride transfer mechanism. First principles simulations, spectroscopic calculations and experiments, and kinetic correlations reveal that the synthesis creates pyrrolic Fe(II)-plN3 as the active center whose flexibility manifested by being pulled out of the plane, enabled by defects, is crucial for collocating the reagents and allowing the chemistry to proceed. The catalyst catalyzes chemoselectively several substrates and possesses a unique trait whereby the chemistry is hindered for more acidic substrates than the hydrogen donors. This work paves the way toward noble-metal free single-atom catalysts for important chemical reactions.

Microwave-assisted, performance-advantaged electrification of propane dehydrogenation

Nonoxidative propane dehydrogenation (PDH) produces on-site propylene for value-added chemicals. While commercial, its modest selectivity and catalyst deactivation hamper the process efficiency and limit operation to lower temperatures. We demonstrate PDH in a microwave (MW)-heated reactor over PtSn/SiO2 catalyst pellets loaded in a SiC monolith acting as MW susceptor and a heat distributor while ensuring comparable conditions with conventional reactors. Time-on-stream experiments show active and stable operation at 500°C without hydrogen addition. Upon increasing temperature or feed partial pressure at high space velocity, catalysts under MWs show resistance in coking and sintering, high activity, and selectivity, starkly contrasting conventional reactors whose catalyst undergoes deactivation. Mechanistic differences in coke formation are exposed. Gas-solid temperature gradients are computationally investigated, and nanoscale temperature inhomogeneities are proposed to rationalize the different performances of the heating modes. The approach highlights the great potential of electrification of endothermic catalytic reactions.

Structure-Property Relationships for Nickel Aluminate Catalysts in Polyethylene Hydrogenolysis with Low Methane Selectivity

Earth-abundant metals have recently been demonstrated as cheap catalyst alternatives to scarce noble metals for polyethylene hydrogenolysis. However, high methane selectivities hinder industrial feasibility. Herein, we demonstrate that low-temperature ex-situ reduction (350 °C) of coprecipitated nickel aluminate catalysts yields a methane selectivity of <5% at moderate polymer deconstruction (25-45%). A reduction temperature up to 550 °C increases the methane selectivity nearly sevenfold. Catalyst characterization (XRD, XAS, 27Al MAS NMR, H2 TPR, XPS, and CO-IR) elucidates the complex process of Ni nanoparticle formation, and air-free XPS directly after reaction reveals tetrahedrally coordinated Ni2+ cations promote methane production. Metallic and the specific cationic Ni appear responsible for hydrogenolysis of internal and terminal C-C scissions, respectively. A structure-methane selectivity relationship is discovered to guide the design of Ni-based catalysts with low methane generation. It paves the way for discovering other structure-property relations in plastics hydrogenolysis. These catalysts are also effective for polypropylene hydrogenolysis.

CKineticsDB─An Extensible and FAIR Data Management Framework and Datahub for Multiscale Modeling in Heterogeneous Catalysis

A great advantage of computational research is its reproducibility and reusability. However, an enormous amount of computational research data in heterogeneous catalysis is barricaded due to logistical limitations. Sufficient provenance and characterization of data and computational environment, with uniform organization and easy accessibility, can allow the development of software tools for integration across the multiscale modeling workflow. Here, we develop the Chemical Kinetics Database, CKineticsDB, a state-of-the-art datahub for multiscale modeling, designed to be compliant with the FAIR guiding principles for scientific data management. CKineticsDB utilizes a MongoDB back-end for extensibility and adaptation to varying data formats, with a referencing-based data model to reduce redundancy in storage. We have developed a Python software program for data processing operations and with built-in features to extract data for common applications. CKineticsDB evaluates the incoming data for quality and uniformity, retains curated information from simulations, enables accurate regeneration of publication results, optimizes storage, and allows the selective retrieval of files based on domain-relevant catalyst and simulation parameters. CKineticsDB provides data from multiple scales of theory (ab initio calculations, thermochemistry, and microkinetic models) to accelerate the development of new reaction pathways, kinetic analysis of reaction mechanisms, and catalysis discovery, along with several data-driven applications.

Enhanced Catalytic Hydrodeoxygenation of Activated Carbon-Supported Metal Catalysts via Rapid Plasma Surface Functionalization

We employ a nonthermal, He/O₂ atmospheric plasma as an efficient surface functionalization method of activated carbons. We show that plasma treatment rapidly increases the surface oxygen content from 4.1 to 23.4% on a polymer-based spherical activated carbon in 10 min. Plasma treatment is 3 orders of magnitude faster than acidic oxidation and introduces a diverse range of carbonyl (C═O) and carboxyl (O-C═O) functionalities that were not found with acidic oxidation. The increased oxygen functionalities reduce the particle size of a high 20 wt % loading Cu catalyst by >44% and suppress the formation of large agglomerates. Increased metal dispersion exposes additional active sites and improves the yield of hydrodeoxygenation of 5-hydroxymethyl furfural to 2,5-dimethyl furan, an essential compound for biofuel replacement, by 47%. Surface functionalization via plasma can advance catalysis synthesis while being rapid and sustainable.

OpenMKM: An Open-Source C++ Multiscale Modeling Simulator for Homogeneous and Heterogeneous Catalytic Reactions

Microkinetic modeling is invaluable for coupling "microscale" atomistic data with "macroscale" reactor observables. We introduce an Open-source Microkinetic Modeling (OpenMKM) multiscale mean-field microkinetics modeling toolkit targeting mainly heterogeneous catalytic reactions but applies equally to homogeneous reactions. OpenMKM is a modular, object-oriented, C++ software, built on top of the robust open-source Cantera built mainly for homogeneous reactions. Reaction mechanisms can be input from human-readable files or automatic reaction generators, avoiding tedious work and errors. The governing equations are also built automatically, unlike Matlab and Python manual implementations, providing speed and error-free models. OpenMKM has built-in interfaces with numerical software, SUNDIALS, for solving ordinary differential equations and differential-algebraic equations. Users can choose various ideal reactors and energy balance options, such as isothermal, adiabatic, temperature ramp, and an experimentally measured temperature profile. OpenMKM is tightly integrated with pMuTT for thermochemistry input file generation from density functional theory (DFT), streamlining the workflow from DFT to MKM and eliminating tedious work and human errors. It is also seamlessly integrated with the RenView software for visualizing the reaction pathways and performing the reaction path or flux analysis (RPA). OpenMKM includes local sensitivity analysis (LSA) by solving the augmented system of equations or using the one-at-a-time finite difference (first or second order) method. LSA can identify not only kinetically influential reactions but also species. The software provides two techniques for large reaction mechanisms for which LSA is too expensive to run. One is the Fischer Information Matrix, which is approximate but comes at nearly zero cost. The other is a new method that we term RPA-guided LSA, which is a finite difference-based method but uses RPA to select kinetically relevant reactions instead of exploiting the entire reaction network. Users can quickly set up and conduct microkinetic simulations without writing code. The user inputs are conveniently divided into reactor setup files and thermodynamic and kinetic definition files to set up different reactors. The source code and documentation are openly available at https://github.com/VlachosGroup/openmkm.

Insights into solvent and surface charge effects on Volmer step kinetics on Pt (111)

The mechanism of pH-dependent hydrogen oxidation and evolution kinetics is still a matter of significant debate. To make progress, we study the Volmer step kinetics on platinum (111) using classical molecular dynamics simulations with an embedded Anderson-Newns Hamiltonian for the redox process and constant potential electrodes. We investigate how negative electrode electrostatic potential affects Volmer step kinetics. We find that the redox solvent reorganization energy is insensitive to changes in interfacial field strength. The negatively charged surface attracts adsorbed H as well as H⁺, increasing hydrogen binding energy, but also trapping H⁺ in the double layer. While more negative electrostatic potential in the double layer accelerates the oxidation charge transfer, it becomes difficult for the proton to move to the bulk. Conversely, reduction becomes more difficult because the transition state occurs farther from equilibrium solvation polarization. Our results help to clarify how the charged surface plays a role in hydrogen electrocatalysis kinetics.

Tuning the reactivity of carbon surfaces with oxygen-containing functional groups

Oxygen-containing carbons are promising supports and metal-free catalysts for many reactions. However, distinguishing the role of various oxygen functional groups and quantifying and tuning each functionality is still difficult. Here we investigate the role of Brønsted acidic oxygen-containing functional groups by synthesizing a diverse library of materials. By combining acid-catalyzed elimination probe chemistry, comprehensive surface characterizations, ¹⁵N isotopically labeled acetonitrile adsorption coupled with magic-angle spinning nuclear magnetic resonance, machine learning, and density-functional theory calculations, we demonstrate that phenolic is the main acid site in gas-phase chemistries and unexpectedly carboxylic groups are much less acidic than phenolic groups in the graphitized mesoporous carbon due to electron density delocalization induced by the aromatic rings of graphitic carbon. The methodology can identify acidic sites in oxygenated carbon materials in solid acid catalyst-driven chemistry.

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts

Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO₂(111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.

Transition-state correlations for predicting thermochemistry of adsorbates and surface reactions

Estimating thermochemical properties from linear correlations may provide a pathway to circumvent expensive density functional theory (DFT) calculations for quantities such as pre-exponentials and temperature corrections to DFT energies. Here, we construct thermochemical scaling relations between C₁-C₆n-alkanes in the gas phase and adsorbed alkyl chains extending from several transition metal surfaces, and examine changes in the slope and fit between metals and adsorption sites. We subsequently add -OH, -NH₂, CO, and CC functional groups to the C₁-C₆ molecules and demonstrate strong linear correlations for thermochemistry across all species. We broaden the correlations to incorporate transition states of C₁-C₆n-alkane dehydrogenation reactions, where thermochemistry for computationally prohibitive transition-state calculations can be quickly assessed. Additionally, we rationalize the linearity of thermochemical correlations based on the composition of the homologous series and theoretical assessments. As an application of the correlations, we estimate pre-exponentials for elementary surface reactions of ethane and propane hydrogenolysis on Ru(0001), which is of relevance to plastic hydrogenolysis. Depending on kinetically important steps, entropic contributions may be necessary to include in certain reaction mechanisms; in contrasting examples, entropies are found to be relatively insignificant for ethane hydrogenolysis but pertinent for propane hydrogenolysis.

Conformations of polyolefins on platinum catalysts control product distribution in plastics recycling

The design of catalysts for the chemical recycling of plastic waste will benefit greatly from an intimate knowledge of the interfacial polymer-catalyst interactions that determine reactant and product distributions. Here, we investigate backbone chain length, side chain length, and concentration effects on the density and conformation of polyethylene surrogates at the interface with Pt(111) and relate them to experimental product distributions resulting from carbon-carbon bond cleavage. Using replica-exchange molecular dynamics simulations, we characterize the polymer conformations at the interface by the distributions of trains, loops, and tails and their first moments. We find that the preponderance of short chains, in the range of 20 carbon atoms, lies entirely on the Pt surface, whereas longer chains exhibit much broader distributions of conformational features. Remarkably, the average length of trains is independent of the chain length but can be tuned via the polymer-surface interaction. Branching profoundly impacts the conformations of long chains at the interface as the distributions of trains become less dispersed and more structured, localized around short trains, with the immediate implication of a wider carbon product distribution upon C-C bond cleavage. The degree of localization increases with the number and size of the side chains. Long chains can adsorb from the melt onto the Pt surface even in melt mixtures containing shorter polymer chains at high concentrations. We confirm experimentally key computational findings and demonstrate that blends may provide a strategy to reduce the selectivity for undesired light gases.

Selective hydrogenation via precise hydrogen bond interactions on catalytic scaffolds

The active site environment in enzymes has been known to affect catalyst performance through weak interactions with a substrate, but precise synthetic control of enzyme inspired heterogeneous catalysts remains challenging. Here, we synthesize hyper-crosslinked porous polymer (HCPs) with solely -OH or -CH₃ groups on the polymer scaffold to tune the environment of active sites. Reaction rate measurements, spectroscopic techniques, along with DFT calculations show that HCP-OH catalysts enhance the hydrogenation rate of H-acceptor substrates containing carbonyl groups whereas hydrophobic HCP- CH₃ ones promote non-H bond substrate activation. The functional groups go beyond enhancing substrate adsorption to partially activate the C = O bond and tune the catalytic sites. They also expose selectivity control in the hydrogenation of multifunctional substrates through preferential substrate functional group adsorption. The proposed synthetic strategy opens a new class of porous polymers for selective catalysis.

Active Learning of Chemical Reaction Networks via Probabilistic Graphical Models and Boolean Reaction Circuits

Discerning networks of many reactions among multiple interconverting species is challenging. Here, we present a reaction network identification methodology. Our methodology enumerates all stoichiometrically and chemically feasible reactions and requires...

Reconciling Experimental Catalytic Data Stemming from Structure Sensitivity

Experimental data have long served as a valuable resource for model validation and identification of the active site. Yet, literature kinetics data often exhibit significant differences among laboratories for the...

Microflow chemistry and its electrification for sustainable chemical manufacturing

Sustainability is vital in solving global societal problems. Still, it requires a holistic view by considering renewable energy and carbon sources, recycling waste streams, environmentally friendly resource extraction and handling, and green manufacturing. Flow chemistry at the microscale can enable continuous sustainable manufacturing by opening up new operating windows, precise residence time control, enhanced mixing and transport, improved yield and productivity, and inherent safety. Furthermore, integrating microfluidic systems with alternative energy sources, such as microwaves and plasmas, offers tremendous promise for electrifying and intensifying modular and distributed chemical processing. This review provides an overview of microflow chemistry, electrification, their integration toward sustainable manufacturing, and their application to biomass upgrade (a select number of other processes are also touched upon). Finally, we identify critical areas for future research, such as matching technology to the scale of the application, techno-economic analysis, and life cycle assessment.

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

Bifunctional hydroformylation on heterogeneous Rh-WOx pair site catalysts

Metal-catalysed reactions are often hypothesized to proceed on bifunctional active sites, whereby colocalized reactive species facilitate distinct elementary steps in a catalytic cycle^1-8. Bifunctional active sites have been established on homogeneous binuclear organometallic catalysts^9-11. Empirical evidence exists for bifunctional active sites on supported metal catalysts, for example, at metal-oxide support interfaces^2,6,7,12. However, elucidating bifunctional reaction mechanisms on supported metal catalysts is challenging due to the distribution of potential active-site structures, their dynamic reconstruction and required non-mean-field kinetic descriptions^7,12,13. We overcome these limitations by synthesizing supported, atomically dispersed rhodium-tungsten oxide (Rh-WO_x) pair site catalysts. The relative simplicity of the pair site structure and sufficient description by mean-field modelling enable correlation of the experimental kinetics with first principles-based microkinetic simulations. The Rh-WO_x pair sites catalyse ethylene hydroformylation through a bifunctional mechanism involving Rh-assisted WO_x reduction, transfer of ethylene from WO_x to Rh and H₂ dissociation at the Rh-WO_x interface. The pair sites exhibited >95% selectivity at a product formation rate of 0.1 g_propanal cm^-3 h^-1 in gas-phase ethylene hydroformylation. Our results demonstrate that oxide-supported pair sites can enable bifunctional reaction mechanisms with high activity and selectivity for reactions that are performed in industry using homogeneous catalysts.

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. This work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Programmable heating and quenching for efficient thermochemical synthesis

Conventional thermochemical syntheses by continuous heating under near-equilibrium conditions face critical challenges in improving the synthesis rate, selectivity, catalyst stability and energy efficiency, owing to the lack of temporal control over the reaction temperature and time, and thus the reaction pathways^1-3. As an alternative, we present a non-equilibrium, continuous synthesis technique that uses pulsed heating and quenching (for example, 0.02 s on, 1.08 s off) using a programmable electric current to rapidly switch the reaction between high (for example, up to 2,400 K) and low temperatures. The rapid quenching ensures high selectivity and good catalyst stability, as well as lowers the average temperature to reduce the energy cost. Using CH₄ pyrolysis as a model reaction, our programmable heating and quenching technique leads to high selectivity to value-added C₂ products (>75% versus <35% by the conventional non-catalytic method and versus <60% by most conventional methods using optimized catalysts). Our technique can be extended to a range of thermochemical reactions, such as NH₃ synthesis, for which we achieve a stable and high synthesis rate of about 6,000 μmol g_Fe^-1 h^-1 at ambient pressure for >100 h using a non-optimized catalyst. This study establishes a new model towards highly efficient non-equilibrium thermochemical synthesis.

In Situ Tracking of Nonthermal Plasma Etching of ZIF-8 Films

Surface characterization is critical for understanding the processes used for preparing catalysts, sorbents, and membranes. Nonthermal plasma (NTP) is a process that achieves high reactivity at low temperatures and is used to tailor the surface properties of materials. In this work, we combine the capabilities of infrared reflection absorption spectroscopy (IRRAS) with NTP for the in situ interrogation of zeolitic imidazolate framework-8 (ZIF-8) thin films to probe modifications in the material induced by oxygen and nitrogen plasmas. The IRRAS measurements in oxygen plasma reveal etching of organic ligands with sequential removal of the methyl group and imidazole ring and with the formation of carbonyl moieties (C═O). In contrast, nitrogen plasma induces mild etching and grafting of nitrile groups (-C≡N). Scanning electron microscopy imaging shows that oxygen plasma, at prolonged times, significantly degrades the ZIF-8 film at the grain boundaries. Treatment of ZIF-8 membranes using mild plasma conditions yields a fivefold enhancement for H₂/N₂ and CO₂/CH₄ ideal selectivities and an eightfold enhancement for CO₂/N₂ ideal selectivity. Additionally, the new tools described here can be used for spectroscopic in situ tracking of plasma-induced chemistry on thin films in general.

Catalytic resonance of ammonia synthesis by simulated dynamic ruthenium crystal strain

Ammonia affords dense storage for renewable energy as a fungible liquid fuel, provided it can be efficiently synthesized from hydrogen and nitrogen. In this work, the catalysis of ammonia synthesis was computationally explored beyond the Sabatier limit by dynamically straining a ruthenium crystal (±4%) at the resonant frequencies (10² to 10⁵⁺ Hz) of N₂ surface dissociation and hydrogenation. Density functional theory calculations at different strain conditions indicated that the energies of NH_x surface intermediates and transition states scale linearly, allowing the description of ammonia synthesis at a continuum of strain conditions. A microkinetic model including multiple sites and surface diffusion between step and Ru(0001) terrace sites of varying ratios for nanoparticles of differing size revealed that dynamic strain yields catalytic ammonia synthesis conversion and turnover frequency comparable to industrial reactors (400°C, 200 atm) but at lower temperature (320°C) and an order of magnitude lower pressure (20 atm).

Ambient-pressure lignin valorization to high-performance polymers by intensified reductive catalytic deconstruction

Chemocatalytic lignin valorization strategies are critical for a sustainable bioeconomy, as lignin, especially technical lignin, is one of the most available and underutilized aromatic feedstocks. Here, we provide the first report of an intensified reactive distillation–reductive catalytic deconstruction (RD-RCD) process to concurrently deconstruct technical lignins from diverse sources and purify the aromatic products at ambient pressure. We demonstrate the utility of RD-RCD bio-oils in high-performance additive manufacturing via stereolithography 3D printing and highlight its economic advantages over a conventional reductive catalytic fractionation/RCD process. As an example, our RD-RCD reduces the cost of producing a biobased pressure-sensitive adhesive from softwood Kraft lignin by up to 60% in comparison to the high-pressure RCD approach. Last, a facile screening method was developed to predict deconstruction yields using easy-to-obtain thermal decomposition data. This work presents an integrated lignin valorization approach for upgrading existing lignin streams toward the realization of economically viable biorefineries.

Low-temperature CO oxidation over Rh/Al2O3 in a stagnation-flow reactor

This study provides thorough, novel experimental data for low-temperature CO oxidation on Rh/Al2O3 in a stagnation-flow reactor.

Polyolefin plastic waste hydroconversion to fuels, lubricants, and waxes: a comparative study

A direct comparison of the recent advancements in the hydrogenolysis and hydrocracking of polyolefins is lacking. This perspective aims to address this gap while providing insights from model alkane studies to guide future research.

Higher loadings of Pt single atoms and clusters over reducible metal oxides: application to C–O bond activation

We develop higher loadings of isolated noble metal atoms and clusters on a metal oxide via redistribution.

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub.

Polyethylene Hydrogenolysis at Mild Conditions over Ruthenium on Tungstated Zirconia

Plastics waste has become a major environmental threat, with polyethylene being one of the most produced and hardest to recycle plastics. Hydrogenolysis is potentially the most viable catalytic technology for recycling. Ruthenium (Ru) is one of the most active hydrogenolysis catalysts but yields too much methane. Here we introduce ruthenium supported on tungstated zirconia (Ru-WZr) for hydrogenolysis of low-density polyethylene (LDPE). We show that the Ru-WZr catalysts suppress methane formation and produce a product distribution in the diesel and wax/lubricant base-oil range unattainable by Ru-Zr and other Ru-supported catalysts. Importantly, the enhanced performance is showcased for real-world, single-use LDPE consumables. Reactivity studies combined with characterization and density functional theory calculations reveal that highly dispersed (WO _x )_n clusters store H as surface hydroxyls by spillover. We correlate this hydrogen storage mechanism with hydrogenation and desorption of long alkyl intermediates that would otherwise undergo further C-C scission to produce methane.