Population Balance Equations for Reactive Separation in Polymer Upcycling

Many polymer upcycling efforts aim to convert plastic waste into high-value liquid hydrocarbons. However, the subsequent cleavage of middle distillates to light gases can be problematic. The reactor often contains a vapor phase (light gases and middle distillates) and a liquid phase (molten polymers and waxes with a suspended or dissolved catalyst). Because the catalyst resides in the liquid phase, middle distillates that partition into the vapor phase are protected against further cleavage into light gases. In this paper, we consider a simple reactive separation strategy, in which a gas outflow removes the volatile products as they form. We combine vapor-liquid equilibrium models and population balance equations (PBEs) to describe polymer upcycling in a two-phase semibatch reactor. The results suggest that the temperature, headspace volume, and flow rate of the reactor can be used to tune selectivity toward the middle distillates, in addition to the molecular mechanism of catalysis. We anticipate that two-phase reactor models will be important in many polymer upcycling processes and that reactive separation strategies will provide ways to boost the yield of the desired products in these cases.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111)

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06. We show that PBE and RPBE, without dispersion correction, closely model RPA energies for adsorption, transition states, reaction, and activation energies. Next, RPA fails to describe the gas phase energy as unsaturation and chain-length increases in the hydrocarbon. Finally, we show that RPBE has the best accuracy-to-cost ratio, and RPA is likely not superior to RPBE or BEEF-vdW, which also gives a measure of uncertainty.

Unlocking the Potential of A-Site Ca-Doped LaCo0.2Fe0.8O3-δ: A Redox-Stable Cathode Material Enabling High Current Density in Direct CO2 Electrolysis

Massive carbon dioxide (CO2) emission from recent human industrialization has affected the global ecosystem and raised great concern for environmental sustainability. The solid oxide electrolysis cell (SOEC) is a promising energy conversion device capable of efficiently converting CO2 into valuable chemicals using renewable energy sources. However, Sr-containing cathode materials face the challenge of Sr carbonation during CO2 electrolysis, which greatly affects the energy conversion efficiency and long-term stability. Thus, A-site Ca-doped La1-xCaxCo0.2Fe0.8O3-δ (0.2 ≤ x ≤ 0.6) oxides are developed for direct CO2 conversion to carbon monoxide (CO) in an intermediate-temperature SOEC (IT-SOEC). With a polarization resistance as low as 0.18 Ω cm2 in pure CO2 atmosphere, a remarkable current density of 2.24 A cm-2 was achieved at 1.5 V with La0.6Ca0.4Co0.2Fe0.8O3-δ (LCCF64) as the cathode in La0.8Sr0.2Ga0.83Mg0.17O3-δ (LSGM) electrolyte (300 μm) supported electrolysis cells using La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) as the air electrode at 800 °C. Furthermore, symmetrical cells with LCCF64 as the electrodes also show promising electrolysis performance of 1.78 A cm-2 at 1.5 V at 800 °C. In addition, stable cell performance has been achieved on direct CO2 electrolysis at an applied constant current of 0.5 A cm-2 at 800 °C. The easily removable carbonate intermediate produced during direct CO2 electrolysis makes LCCF64 a promising regenerable cathode. The outstanding electrocatalytic performance of the LCCF64 cathode is ascribed to the highly active and stable metal/perovskite interfaces that resulted from the in situ exsolved Co/CoFe nanoparticles and the additional oxygen vacancies originated from the Ca2Fe2O5 phase synergistically providing active sites for CO2 adsorption and electrolysis. This study offers a novel approach to design catalysts with high performance for direct CO2 electrolysis.

Theoretical Investigation of the Electrochemical Oxidation of H2 and CO Fuels on a Ruddlesden-Popper SrLaFeO4-δ Anode

The electrochemical oxidation of H₂ and CO fuels have been investigated on the Ruddlesden-Popper layered perovskite SrLaFeO_4-δ (SLF) under anodic solid oxide fuel cell conditions using periodic density functional theory and microkinetic modeling techniques. Two distinct FeO₂-plane-terminated surface models differing in terms of the underlying rock salt layer (SrO or LaO) are used to identify the active site and limiting factors for the electro-oxidation of H₂, CO, and syngas fuels. Microkinetic modeling predicted an order of magnitude higher turnover frequency for the electro-oxidation of H₂ compared to CO for SLF at short-circuit conditions. The surface model with an underlying SrO layer was found to be more active with respect to H₂ oxidation than the LaO-based surface model. At an operating voltage of less than 0.7 V, surface H₂O/CO₂ formation was found to be the key rate-limiting step, and the surface H₂O/CO₂ desorption was the key charge transfer step. In contrast, the bulk oxygen migration process was found to affect the overall rate at high cell voltage conditions above 0.9 V. In the presence of syngas fuel, the overall electrochemical activity is derived mainly from H₂ electro-oxidation and CO₂ is chemically shifted to CO via the reverse water-gas shift reaction. Substitutional doping of a surface Fe atom with Co, Ni, and Mn revealed that the H₂ electro-oxidation activity of FeO₂-plane terminated anodes with an underlying LaO rock salt layer can be improved with dopant introduction, with Co yielding a three orders of magnitude higher activity relative to the undoped LaO surface model. Constrained ab initio thermodynamic analysis furthermore suggested that the SLF anodes are resistant toward sulfur poisoning both in the presence and absence of dopants. Our findings reflect the role of various elements in controlling the fuel oxidation activity of SLF anodes that could aid the development of new Ruddlesden-Popper phase materials for fuel cell applications.

Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, and experimental data for solvation free energies of phenol adsorption are reanalyzed using multiple adsorption isotherm models. Explicit solvation calculations predict an endergonic solvation free energy for phenol adsorption that agrees well with measurements to within the experimental and force field uncertainties. Computed adsorption free energies of solvation of carbon monoxide, ethylene glycol, benzene, and phenol over the (111) facet of Pt and Cu suggest that liquid water destabilizes all adsorbed species, with the largest impact on the largest adsorbates.

Unraveling Unique Surface Chemistry of Transition Metal Nitrides in Controlling Selective C-O Bond Scission Pathways of Glycerol

Controlled C-O bond scission is an important step for upgrading glycerol, a major byproduct from the continuously increasing biodiesel production. Transition metal nitride catalysts have been identified as promising hydrodeoxygenation (HDO) catalysts, but fundamental understanding regarding the active sites of the catalysts and reaction mechanism remains unclear. This work demonstrates a fundamental surface science study of Mo₂N and Cu/Mo₂N for the selective HDO reaction of glycerol, using a combination of model surface experiments and first-principles calculations. Temperature-programmed desorption (TPD) experiments showed that clean Mo₂N cleaved two or three C-O bonds of glycerol to produce allyl alcohol, propanal, and propylene. The addition of Cu to Mo₂N changed the reaction pathway to one C-O bond scission to produce acetol. High-resolution electron energy loss spectroscopy (HREELS) results identified the surface intermediates, showing a facile C-H bond activation on Mo₂N. Density functional theory (DFT) calculations revealed that the surface N on Mo₂N interacted with the H atoms in glycerol and blocked some Mo sites to enable selective C-O bond scission. This work shows that Mo₂N and Cu/Mo₂N are active and selective for the controlled C-O bond scission of glycerol and in turn provides insights into the rational catalyst design for selective oxygen removal of relevant biomass-derived oxygenates.

Size-Controlled Nanoparticles Embedded in a Mesoporous Architecture Leading to Efficient and Selective Hydrogenolysis of Polyolefins

A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO₂/Pt-X/SiO₂; X is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C₂₃-centered distribution of hydrocarbons in high yield using very low Pt loadings (∼10^-5 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C-C bond cleavage occurs and then the smaller fragment exits the pore. mSiO₂/Pt/SiO₂ resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved. Here, we report the effects of Pt NP size on activity and selectivity in PE hydrogenolysis. Time-dependent conversion and yields and a lumped kinetics model based on the competitive adsorption of long vs short chains reveal that the activity of catalytic material is highest with the smallest NPs, consistent with a structure-sensitive reaction. Remarkably, the three mSiO₂/Pt-X/SiO₂ catalysts give equivalent selectivity. We propose that mesoscale pores in the catalytic architecture template the C₂₃-centered distribution, whereas the active Pt sites influence the carbon-carbon bond cleavage rate. This conclusion provides a framework for catalyst design by separating the C-C bond cleavage activity at catalytic sites from selectivity for chain lengths of the products influenced by the structure of the catalytic architecture. The increased activity, selectivity, efficiency, and lifetime obtained using this architecture highlight the benefits of localized and confined environments for isolated catalytic particles under condensed-phase reaction conditions.

Surface structure sensitivity of hydrodeoxygenation of biomass-derived organic acids over palladium catalysts: a microkinetic modeling approach

A microkinetic DFT model for HDO of CH3CH2COOH on Pd(100) describes experimental observations while Pd(111) is orders of magnitude less active.

Aqueous-phase effects on ethanol decomposition over Ru-based catalysts

Liquid water decelerates ethanol reforming over Ru(0001) but increases the H2 selectivity due to accelerated WGS and suppressed methanation.

Kinetic modeling of nitrous oxide decomposition on Fe-ZSM-5 in the presence of nitric oxide based on parameters obtained from first-principles calculations

A variety of experiments for the N₂O decomposition over Fe-ZSM-5 catalysts have been simulated in the presence and absence of small amounts of nitric oxide and water vapor.

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict-this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

Oxidative dehydrogenation of propane on the oxygen adsorbed edges of boron nitride nanoribbons

A N₂O/NOx-type site on the boron nitride nanoribbon edge is proposed as highly selective, active site for the oxidative dehydrogenation of propane.

Deoxydehydration of 1,4-anhydroerythritol over anatase TiO2(101)-supported ReOx and MoOx

Heterogeneously catalyzed deoxydehydration (DODH) ordinarily occurs over oxide supported ReO_x sites. A comparably high activity of MoO_x/TiO₂(101) suggests that it is a promising low-cost DODH catalyst that can replace Re-based catalysts.

Upcycling Single-Use Polyethylene into High-Quality Liquid Products

Our civilization relies on synthetic polymers for all aspects of modern life; yet, inefficient recycling and extremely slow environmental degradation of plastics are causing increasing concern about their widespread use. After a single use, many of these materials are currently treated as waste, underutilizing their inherent chemical and energy value. In this study, energy-rich polyethylene (PE) macromolecules are catalytically transformed into value-added products by hydrogenolysis using well-dispersed Pt nanoparticles (NPs) supported on SrTiO₃ perovskite nanocuboids by atomic layer deposition. Pt/SrTiO₃ completely converts PE (M _n = 8000-158,000 Da) or a single-use plastic bag (M _n = 31,000 Da) into high-quality liquid products, such as lubricants and waxes, characterized by a narrow distribution of oligomeric chains, at 170 psi H₂ and 300 °C under solvent-free conditions for reaction durations up to 96 h. The binding of PE onto the catalyst surface contributes to the number averaged molecular weight (M _n) and the narrow polydispersity (Đ) of the final liquid product. Solid-state nuclear magnetic resonance of ¹³C-enriched PE adsorption studies and density functional theory computations suggest that PE adsorption is more favorable on Pt sites than that on the SrTiO₃ support. Smaller Pt NPs with higher concentrations of undercoordinated Pt sites over-hydrogenolyzed PE to undesired light hydrocarbons.

Investigation of solvent effects on the hydrodeoxygenation of guaiacol over Ru catalysts

The effects of a liquid phase environment on the hydrodeoxygenation of guaiacol, a prototypical lignin derived compound, have been investigated over a Ru catalyst from first principles.

Controlling reaction pathways of selective C-O bond cleavage of glycerol

The selective hydrodeoxygenation (HDO) reaction is desirable to convert glycerol into various value-added products by breaking different numbers of C-O bonds while maintaining C-C bonds. Here we combine experimental and density functional theory (DFT) results to reveal that the Cu modifier can significantly reduce the oxophilicity of the molybdenum carbide (Mo2C) surface and change the product distribution. The Mo2C surface is active for breaking all C-O bonds to produce propylene. As the Cu coverage increases to 0.5 monolayer (ML), the Cu/Mo2C surface shows activity towards breaking two C-O bonds and forming ally-alcohol and propanal. As the Cu coverage further increases, the Cu/Mo2C surface cleaves one C-O bond to form acetol. DFT calculations reveal that the Mo2C surface, Cu-Mo interface, and Cu surface are distinct sites for the production of propylene, ally-alcohol, and acetol, respectively. This study explores the feasibility of tuning the glycerol HDO selectivity by modifying the surface oxophilicity.

Mechanistic study of the ceria supported, re-catalyzed deoxydehydration of vicinal OH groups

Deoxydehydration (DODH) is an emerging biomass deoxygenation process whereby vicinal OH groups are removed.

Ethylene glycol reforming on Pt(111): first-principles microkinetic modeling in vapor and aqueous phases

Reaction chemistry for vapor- and aqueous-phase reforming of ethylene glycol over Pt(111) is similar with early dehydrogenation steps being rate-controlling.