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Vlachos, Dionisios G.

Publications and source records attributed to Vlachos, Dionisios G..

161 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Thiol-promoted catalytic synthesis of high-performance furan-containing lubricant base oils from biomass derived 2-alkylfurans and ketones

About 97% of lubricant base oils are currently sourced from petroleum and the majority of the rest is produced from vegetable oils. We demonstrate a promising catalytic route to produce base oils from lignocellulosic biomass-derived 2-alkylfurans and ketones via carbon–carbon coupling in neat conditions. Among several homogeneous and heterogeneous acid catalysts tested, a perfluorinated sulfonic acid (Aquivion PW79S) exhibits the best catalytic performance and yields up to 90% renewable furan-containing base oils with the use of a thiol promotor. The effects of the acid strength of the catalysts, molecular size and structure of thiols and ketones, and fraction of thiols are studied. Electronic structure calculations elucidate the reaction pathway and indicate that the thiol reduces the barrier of the rate-determining dehydration step. The structure and properties of base oils can be tuned by using different synthons. Here, these base oils have excellent properties and can be competitive with or surpass the commercial synthetic alkylnaphthalene and furan-containing bio-ester base oils.

09 BIOMASS FUELS↗
DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Stability of heterogeneous single-atom catalysts: a scaling law mapping thermodynamics to kinetics

Heterogeneous single-atom catalysts (SACs) hold the promise of combining high catalytic performance with maximum utilization of often precious metals. We extend the current thermodynamic view of SAC stability in terms of the binding energy (E bind ) of single-metal atoms on a support to a kinetic (transport) one by considering the activation barrier for metal atom diffusion. A rapid computational screening approach allows predicting diffusion barriers for metal–support pairs based on E bind of a metal atom to the support and the cohesive energy of the bulk metal (E c ). Metal–support combinations relevant to contemporary catalysis are explored by density functional theory. Assisted by machine-learning methods, we find that the diffusion activation barrier correlates with (E bind ) 2 /E c in the physical descriptor space. This diffusion scaling-law provides a simple model for screening thermodynamics to kinetics of metal adatom on a support.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermochemical Data Fusion Using Graph Representation Learning

Large databases are required for “Big Data” applications in catalysis and materials science. Thermochemical databases can be created by combining data from various sources and by correcting low-fidelity datasets to higher accuracy with minimal computation. To achieve this “data fusion”, thermochemical quantities of interest, calculated at various levels of density functional theory (DFT), need to be mapped to the same, high levels of theory. In this work, a graph theoretical, statistical framework is proposed for such tasks. Subgraph frequencies are shown to provide a natural representation for learning these fusion maps. The maps are linear and are learnt with automated descriptor selection. Using a dataset of as few as ~1% from the QM9 database of 133,885 molecules, these models can predict multiple thermochemical quantities at a higher level of theory with an accuracy of 1 kcal/mol. Here, the method is explainable, generalizable, and provides a diagnostic tool for outlier identification

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reversible Formation of Silanol Groups in Two-Dimensional Siliceous Nanomaterials under Mild Hydrothermal Conditions

Monitoring the effects of mild hydrothermal conditions, in situ, on siliceous materials remains challenging using surface science techniques, which often require electrically conductive substrates. The emergence of two-dimensional (2-D) siliceous nanomaterials deposited on metal single crystals overcomes this limitation. Here, we use infrared reflection absorption spectroscopy (IRRAS) to study the effects of mild hydrothermal conditions, in situ, on 2-D model systems, namely, all-Si MFI nanosheets supported on Au(111) and a polymorphous bilayer silicate supported on Ru(0001). We find that the formation of silanol groups (SiOH) occurs at 473 and 573 K under a H 2 O pressure of 3 mbar in the MFI nanosheets, but not in the polymorphous bilayer silicate. The effects of mild hydrothermal conditions are reversible in the MFI nanosheets and do not result in framework degradation. Implications shown here provide a fundamental understanding of the impact of mild hydrothermal conditions on the 2-D siliceous nanomaterials and serve as a starting point when considering these effects on three-dimensional (3-D) ones.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Phosphonate‐Modified UiO‐66 Brønsted Acid Catalyst and Its Use in Dehydra‐Decyclization of 2‐Methyltetrahydrofuran to Pentadienes

Abstract Phosphorus‐modified all‐silica zeolites exhibit activity and selectivity in certain Brønsted acid catalyzed reactions for biomass conversion. In an effort to achieve similar performance with catalysts having well‐defined sites, we report the incorporation of Brønsted acidity to metal–organic frameworks with the UiO‐66 topology, achieved by attaching phosphonic acid to the 1,4‐benzenedicarboxylate ligand and using it to form UiO‐66‐PO 3 H 2 by post‐synthesis modification. Characterization reveals that UiO‐66‐PO 3 H 2 retains stability similar to UiO‐66, and exhibits weak Brønsted acidity, as demonstrated by titrations, alcohol dehydration, and dehydra‐decyclization of 2‐methyltetrahydrofuran (2‐MTHF). For the later reaction, the reported catalyst exhibits site‐time yields and selectivity approaching that of phosphoric acid on all‐silica zeolites. Using solid‐state NMR and deprotonation energy calculations, the chemical environments of P and the corresponding acidities are determined.

Dorneles de Mello, Matheus↗
DOE OSTI2020

Operation and Optimization of Microwave-Heated Continuous-Flow Microfluidics

Microwave (MW) technology can be powerful for electrification and process intensification but limited fundamental understanding of scalability and design principles hinders its effective use. In this work, we build a continuous-flow microreactor inside a commercial single-mode MW applicator and the corresponding computational fluid dynamics model to simulate the temperature profile. The model is in good agreement with experiments for various microreactor dimensions and operating conditions. The model indicates that MW heating is greatly influenced by reactor geometry as well as the operating parameters. We observe a strong correlation between parameters and develop a gradient boost regression tree model to predict the outlet temperature accurately. This model is then applied to optimize the dimensions and operating conditions to maximize the outlet temperature and energy efficiency, resulting in a Pareto optimal. We demonstrate computationally and experimentally that it is possible to surpass the Pareto optimal and achieve an energy efficiency of ~90% or greater at temperatures relevant for liquid-phase chemistry via salting of the solvent. The present methodology can be applied to other complex MW reactors. Lastly, the combined numerical and experimental approach provides insights into and a framework for scale-up and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Methods of forming aromatic containing compounds

Methods that include acylating an aromatic containing compound by reacting the aromatic containing compound with an anhydride containing compound to form an acylated aromatic containing compound.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗
DOE OSTI2020

Reductive catalytic fractionation of agricultural residue and energy crop lignin and application of lignin oil as antimicrobials

Cheap and abundant waste from bioethanol and agricultural processing industries are an alluring alternative feedstock for biorefineries. In this work, we employ reductive catalytic fractionation (RCF) to depolymerize, over Ru/C powder and Ru/Al 2 O 3 pellets, five herbaceous biomass feedstocks, namely corn stover, miscanthus, switchgrass, sugarcane bagasse, and wheat straw into phenolic monomers with high yields (~40 wt.% based on total (Klason + acid soluble) lignin and > 50 wt.% when stabilized using aldehydes), leaving behind a carbohydrate pulp residue. Interestingly, a polar solvent is sufficient to solubilize and fragment the lignin polymer into monomers without any catalyst. Here, contrary to woody biomass, where the monomer yields are positively correlated with the S-content of lignin, principal component analysis indicates that the monomer yields from herbaceous biomass depend on the content of lignin crosslinker – ferulate. Using NMR, we further identify α – 6 C-C linked oligomers formed from condensation reactions, explaining the unexpected low monomer yields of high β-O-4 herbaceous biomass. Recyclability experiments indicate that catalyst deactivation occurs through sintering, leaching, and fouling. Lignin oil from herbaceous biomass exhibits bacteriostatic effects against Staphylococcus aureus, highlighting a potential application in functional food development and as food or feed preservative.

09 BIOMASS FUELS↗