Engineering topics
Vlachos, Dionisios G.
Publications and source records attributed to Vlachos, Dionisios G..
Scale-up of microwave-assisted, continuous flow, liquid phase reactors: Application to 5-Hydroxymethylfurfural production
Microwave (MW) technology can be powerful for the electrification and intensification of chemical manufacturing. However, very few examples of scaled MW processes exist. In this work, we build a scaled, MW-assisted, continuous flow reactor for the processing of liquid phase chemistries with specific application to 5-hydroxymethylfurfural (HMF) production. Here, we demonstrate a corresponding computational fluid dynamics model to simulate the reactor’s temperature profile and performance, both of which are in good agreement with experiments. We construct a surrogate model to relate operating parameters to performance for guiding experiments, without demanding simulations, via active learning. Heat recirculation is demonstrated in this scaled reactor, further extending its scale and reducing the energy demand. An HMF yield of ~55 % and a productivity of 0.1 kg/hr, 8x higher than any other reactor, at a flowrate >20x than prior work, are demonstrated while maintaining energy efficiency of >98 %. A basic economic analysis estimates a $1.85/kg cost of HMF from fructose and >60 % reduction in CO 2 emissions than a conventional system without considering the impact of green electricity. We demonstrate that MW technology is well-applied to systems larger than the laboratory scale, and strategies to further reduction in cost and CO 2 are discussed.
Differentiating supported platinum single atoms, clusters and nanoparticles by styrene hydrogenation
Supported metal catalysts often consist of metal sites ranging from nanoparticles to subnanometer clusters and single atoms. It remains a necessity to differentiate these sites to guide design of optimal catalysts. Here we report a simple method to assess the distribution of metal active sites in catalyst samples. The method takes the advantage of the structure sensitivity of styrene hydrogenation over titania supported platinum (Pt) catalysts with Pt aggregates varied from single atom to ~1.40 nm nanoparticles. The physicochemical properties were characterized by STEM, XPS, XANES, H2-TPR and CO-chemisorption measurements. The reactivity of Pt sites was quantified by styrene hydrogenation at ambient conditions. The nanometer-sized Pt clusters have significantly higher activity than Pt nanoparticles, sub-nanometer clusters or isolated single atoms. Additionally, the relationship between activity and structural/electronic properties of Pt sites influenced by particle sizes was discussed. Similar relationship was found in the carbon supported Pt catalysts.
Microflow chemistry and its electrification for sustainable chemical manufacturing
This review presents the recent advancements in utilizing microflow systems combined with plasma and microwave heating for sustainable manufacturing. The technology holds promise in increasing process efficiency while minimizing carbon footprint.
High-Temperature Pretreatment Effect on Co/SiO 2 Active Sites and Ethane Dehydrogenation
Here, we report the synthesis, optimization, and characterization of Co/SiO 2 for ethane nonoxidative dehydrogenation. Co/SiO 2 is synthesized via strong electrostatic adsorption using the widely available Co(NO 3 ) 2 as the precursor. We demonstrate that high-temperature pretreatment (900 °C) in an inert atmosphere can significantly enhance the initial activity of the Co/SiO 2 catalyst. X-ray absorption near-edge spectroscopy (XANES), temperature-programmed reduction (TPR), and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) suggest that highly dispersed Co(II) clusters are more active than Co 0 or CoO x nanoparticles. Fourier transform infrared (FTIR) and isopropanol (IPA) temperature-programmed desorption and density functional theory (DFT) calculations suggest that high-temperature treatment significantly increases the density of active Lewis acid sites, possibly via surface dehydroxylation of the catalyst.
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. Here 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.
A general strategy and a consolidated mechanism for low-methane hydrogenolysis of polyethylene over ruthenium
Polyethylene (PE) is one of the most environment-threatening plastic waste. Its Ru-catalyzed hydrogenolysis is rapid but produces too much methane. Here, in this study, low-density PE hydrogenolysis is performed at mild conditions and short times over Ru-doped zirconia catalysts (Ru-XZr, X = Ti, Nb, Ce, W, V, Mo, Fe) to determine low-methane catalysts. Methane is produced via direct terminal C-C scission and surface cascade of consecutive C-C scissions, with the latter sensitive to hydrogen availability and dominant in hydrogen-lean conditions. Reactivity studies, characterization and theory reveal that the most effective dopant oxides (W, V, and Mo) are intermediately reducible, as they store and supply extra hydrogen to Ru via reverse hydrogen spillover. This hydrogen readily hydrogenates and desorbs long alkyl surface intermediates that would otherwise produce cascade methane. Our proposed mechanism reconciles the low methane production achieved by increasing the hydrogen pressure or reducing the particle size and exposes the key selectivity descriptors.
Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity
Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.
Direct non-oxidative methane coupling on vitreous silica supported iron catalysts
Direct non-oxidative methane coupling (NMC) is one of the promising pathways for methane upgrading into value-added olefin and aromatic hydrocarbons. The silica-supported iron (i.e., Fe/SiO 2 ) catalyst has been reported effective for NMC, but the effects of silica support on the catalyst property and NMC performance have rarely been explored. Here, in this work, we prepared a vitreous silica-supported iron (Fe/SiO 2 -V) catalyst by flame fusion of a mixture of quartz silica and fayalite. The physicochemical properties and NMC performance of the as-prepared catalysts were measured. Compared to crystalline cristobalite support in Fe/SiO 2 catalyst that has been studied previously, vitreous silica support has disordered Si-O bonds and structural defects, enabling better iron dispersion and more vital metal-support interaction. The as-prepared Fe/SiO 2 -V catalyst had a shorter induction period in methane activation and lower coke yield in NMC. The increase in iron concentration in Fe/SiO 2 -V catalysts elongated the catalyst induction period and promoted aromatics and coke formation. The coke type of the spent Fe/SiO 2 -V catalyst is more uniform than the cristobalite-supported iron catalyst.
Antioxidant-induced transformations of a metal-acid hydrocracking catalyst in the deconstruction of polyethylene waste
Plastics are ideal for numerous applications, as driven by the development of complex formulations containing various additives to improve performance and processability. Unfortunately, chemical valorization strategies (e.g., catalytic deconstruction) often can be challenged by the presence of small-molecule additives, and quantification of the impact of these molecular constituents on upcycling processes remains elusive. Here, this dearth of information restricts catalyst design efforts to combine the robustness and performance necessary to improve plastics circularity. In this communication, we describe a systematic study of common plastics additives—phenolic antioxidants, and we quantify the relationship between additive content and deconstruction yields of high-density polyethylene (HDPE) over a platinum on tungstated zirconium (Pt/WO 3 /ZrO 2 ) hydrocracking catalyst. In the simplest case of a base (antioxidant- and slip agent-containing) HDPE resin versus a pure (additive-stripped) HDPE polymer, a two-fold decrease in the yield of gas and liquid products is noted for base HDPE resin. Furthermore, both antioxidant chemistry and concentration strongly impact conversion and individual product selectivities. Using infrared spectroscopy, we determine that antioxidants change the effective ratio of metal to acid sites (i.e., metal-acid balance) through reactions of phenols and/or other functional groups (e.g., acids, esters) with catalyst active sites. Overall, this work demonstrates the impact of one common set of additives on plastics deconstruction, and the analysis herein provides a blueprint for quantitatively assessing the effects of additives on plastics deconstruction processes and for evaluating the development of more robust catalytic strategies or more tolerable additive formulations.
Selective hydrodeoxygenation of 5-hydroxymethylfurfural (HMF) to 2,5-dimethylfuran (DMF) over carbon supported copper catalysts using isopropyl alcohol as a hydrogen donor
Selective hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural to 2,5-dimethylfuran is of great importance. Here, we reveal a simple pathway for green and efficient HDO using readily available copper with in-situ hydrogen generation. A highly dispersed Cu/PBSAC catalyst consisting of small metallic Cu 0 nanoparticles carries out isopropyl alcohol (IPA) dehydrogenation and subsequent HDO of HMF. Density functional theory calculations reveal that the dehydrogenation of IPA is more favorable on Cu(211) with a lower energy barrier of ~0.6 eV. This facet exists in a higher ratio on nanosized catalysts. Batch reactions using Cu/PBSAC at 190 °C exhibited 91.9% HMF conversion and 71.7% DMF selectivity in 6 hr, and > 96% DMF yield in 10 hr. The mechanical strength of the carbon support is ideal for continuous processing for increased productivity; we demonstrate a > 90% DMF yield at 1/WHSV of 2.4 hr. The process demonstrated here can be integrated with upstream HMF separation utilizing carbon adsorbents.
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.
Innovations Toward the Valorization of Plastics Waste
Plastics are an extremely important class of materials that are prevalent in all facets of society; however, their widespread use over time, combined with limited end-of-life strategies, has led to increasing levels of waste accumulation. Although currently considered a burden, plastics waste is potentially an untapped feedstock for numerous chemical and manufacturing processes. In this study, we discuss the state of the art of approaches for valorization of plastics waste from a materials research perspective, including previous efforts to utilize plastics waste and recent innovations that have opportunities to add significant value. Although additional progress is necessary, we present several diverse capabilities and strategies for valorization that, when brought together, address end-of-life challenges for plastics at every stage of design and product consumption. In short, a materials research–based framework offers a unique perspective to address the urgent issues posed by plastics, unlocking the potential of polymers and plastics waste.
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. Here 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.
Multiscale modeling of hydrogenolysis of ethane and propane on Ru(0001): Implications for plastics recycling
Plastic waste presents an environmental threat. Chemical recycling via hydrogenolysis can convert plastic waste into waxes, lubricants, and fuels. Among catalysts, Ru stands out for its superior activity and selectivity. The chemistry of light alkane hydrogenolysis can help understanding plastics deconstruction. Here, we perform first-principles calculations, develop descriptor-based relations, and conduct microkinetic modeling and analysis on ethane and propane. Predictions are in excellent agreement with experimental data. We identify a similar cracking pattern for both hydrocarbons entailing a deeply dehydrogenated species with the removal of four hydrogen atoms: CHCH*+* → 2CH* for ethane and CH 3 CCH*+2* → CH 3 C* + CH* for propane. We find that the rate-determining step is the C-C cracking for ethane and the first dehydrogenation from the terminal carbon (CH 3 CH 2 CH 3 *+* → CH 3 CH 2 CH 2 *+H*) for propane. The vinyl species CH 2 CH* produced from propane cracking is responsible for whether a single or multiple cracking events occur and effectively controls the selectivity. Specifically, ethane formation in propane hydrogenolysis is suppressed at elevated temperatures due to over-cracking via multiple (two here) cracking events being preferred over hydrogenation and desorption of ethane from the catalyst. Our workflow and models provide a baseline for future studies on heavier hydrocarbons. Insights into recent experimental studies of polyethylene over Ru-based catalysts are discussed.
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. 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 NH 3 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. Furthermore, this study establishes a new model towards highly efficient non-equilibrium thermochemical synthesis.
Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility
Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.