Biomass-derived, Target Specific, and Ecologically Safer Insecticide Active Ingredients.

With the continuous increase in food production to support the growing population, ensuring agricultural sustainability using crop-protecting agents, such as pesticides, is vital. Conventional pesticides pose significant environmental risks, prompting the need for eco-friendly alternatives. This study reports the synthesis of new amide-based insecticidal active ingredients from biomass-derived monomers, specifically furfural and vanillin. The process involves reductive amination followed by carbonylation. The synthesis of the furfural-based carbamate yield reaches a cumulative 88%, with catalysts Rh/Al2O3 and La(OTf)3 being recyclable at each stage. Insecticidal activity assessments reveal that the furfural carbamate exhibits competitive performance, achieving an LC50 of 6.35 µg/cm², compared to 6.27 µg/cm² for carbofuran. Ecotoxicity predictions indicate significantly lower toxicity levels toward non-target aquatic and terrestrial species. The importance of the low octanol-water partition coefficient of the biobased carbamate, attributed to the oxygen heteroatom and electron density of the furan ring, is discussed in detail. Building on these promising results, the synthesis strategy was extended to six other biobased aldehydes, resulting in a diverse portfolio of biomass-derived carbamates. A techno-economic analysis reveals a minimum selling price of 11.1$/kg, only half that of comparable carbamates, demonstrating the economic viability of these new biobased insecticides.

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.

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.

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.

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.

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.

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 Fe 3 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.

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.

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.

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.

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.

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.

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.

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 same catalyst and reaction, but the reasons have remained elusive. Here, we exploit if we can rationalize (most of) this variation through catalyst structure sensitivity. We introduce a methodology to build a structure-descriptor-based microkinetic model and investigate the relations between nanoparticle structure and reaction kinetics using the complete methane oxidation on Pt as a model reaction and literature data mining. A volcano-like rate is observed with an optimum coordination number. Unlike common expectations, smaller particles have very low reactivity because of carbon poisoning. Interestingly, most of the data variation can be successfully traced to structure sensitivity. This methodology also enables rapid prediction of kinetic performance and active site determination for designing optimal catalyst structures. It can also serve as a data quality tool to assess experimental outliers. Additional reasons for data variability are discussed.

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.

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.

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.

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.