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Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. Further, this makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
Modeling the Effects of Microwave Heating on Densification in Chemical Vapor Infiltration
Microwave heating has great potential to accelerate the synthesis of ceramic matrix composites (CMCs) by a process called Chemical Vapor Infiltration (CVI). In CVI, reactive gases ingress a porous preform and undergo chemical transformation to deposit solid ceramic phase within the pores at high temperature thus, densifying the preform. However, the competing effects of chemical kinetics and gas transport are known to result in non-uniform densification as the outer surfaces of the preform experience faster depositions compared to the core. Achieving spatial temperature control plays a key role improving the densification quality. Microwave heating can potentially create temperature inversion such that the core of the preform is hotter than the outer surface and subsequently, lead to improved densification while keeping the manufacturing times and costs low. In the present work, a computational modeling strategy has been developed that accounts for the key physical phenomena responsible for densification of porous preforms using microwave heating. Specifically, a chemical kinetics model has been formulated for Silicon Carbide (SiC) deposition from MTS/H2 precursor. The model is implemented in a pore-resolved reactive transport solver, Quilt. The CVI simulations are performed for several conditions. Initially, parameterized temperature control is imposed to identify optimum conditions for good densification quality at a fraction of processing time. Further, simulations of microwave heating of porous SiC preforms are performed using OpenFOAM. A strategy to achieve and enhance temperature inversion is identified. The resulting temperature profiles are used in the pore-resolved densification simulations to analyze the densification behavior. It is observed that the temperature inversion achieved by microwave heating leads to significant improvements in densification quality and at the same time, keeps the manufacturing time low.
Semiconductor nanocrystal photocatalysis for the production of solar fuels
Colloidal semiconducting nanocrystals (NCs) are powerful elements of a photocatalytic system useful for enabling a variety of chemical transformations owing to their strong light-absorbing properties and high degree of size-, shape-, and composition-tunability. Key to their utility is our understanding of the photoinduced charge transfer processes required for these photochemical transformations. This Perspective will focus on the implementation of semiconductor NCs for photochemical fuel formation. Three general system designs for photocatalytic proton reduction using semiconductor NCs will be reviewed: metal–semiconductor heterostructures, NC photosensitizers with molecular catalysts, and hydrogenase-based systems. Additionally, other relevant reactions toward solar fuel targets, such as CO 2 and N 2 reductions with NCs, will also be highlighted. Illustrating the versatile roles that NCs can play in light-driven chemical reactions, advances made toward NC-catalyzed organic transformations will be discussed. Finally, we will share a few concluding thoughts and perspectives on the future of the field, with a focus on goals toward improving and implementing NC-based technologies for solar fuel development.
Tip-enhanced Raman imaging of photocatalytic reactions on thermally-reshaped gold and gold–palladium microplates
The plasmonic/photocatalytic properties of thermally-reshaped walled gold–palladium microplates (WAu@PdMPs) have been explored by TERS.
Suppressing Molecular Charging, Nanochemistry, and Optical Rectification in the Tip-Enhanced Raman Geometry
Classical vs quantum plasmons are operative in tapping vs contact mode tip-enhanced Raman scattering (TERS) and lead to distinct observables. Under otherwise identical experimental conditions, we illustrate the concept through tapping and contact mode TERS mapping of chemically functionalized silver nanocubes. Whereas molecular charging, chemical transformations, and optical rectification are prominent observables in contact mode TERS, the same effects are suppressed using tapping mode feedback. In effect, this work demonstrates that nanoscale physical and chemical processes can be accessed/suppressed on-demand in the TERS geometry. Further, the advantages of tapping mode TERS are otherwise highlighted with the latter in mind.
CSGL: chemical synthesis graph learning for molecule representation
Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.
Inorganometallic Catalyst Design Center (ICDC) EFRC Accomplishments
The ICDC focused largely on heterogeneous catalysis of oxidative chemical transformations, especially those that came to prominence as natural gas (wet shale gas) supplanted crude oil as the primary feedstock in North America for carbon-based manufacturing of commodity chemicals. Driven by fracking-enabled access to enormous stores of shale-trapped wet gas, the feedstock change from oil to gas enabled a shift away from high-temperature catalytic breakdown of large hydrocarbons to catalytic build-up of C4, C3, C2, and C1 into desired intermediates, but now at much more modest temperatures. In turn, the much lower thermal force accompanying these kinds of reactions facilitates catalyst stabilization and characterization, including operando characterization, while slowing on-stream catalyst evolution (i.e., preserving catalyst structure), accommodating catalyst complexity, and enabling creation of uniform arrays of heterogeneous catalysts with single-atom or near-single-atom precision—at least for model systems. Together these features have proven to be a boon to hypothesis-driven catalysis science and to experimentally testable computation-based design advances and mechanism explication. The ICDC has greatly facilitated the integration of catalyst synthesis, advanced characterization, performance assessment, and computational elucidation—with advances in each of these realms spurring achievements in catalysis science and complementary realms.
Chemical Reactivity Through Adaptive Quantum Mechanics/Many-Body Representations: Theoretical Development, Software Implementation, and Applications (Final Report)
The main objective of this research project was the development and application of a new theoretical/computational framework to model chemical transformations and electronic excitations in fluid mixtures across different phases. Our theoretical/computational framework combines our data-driven many-body (DD-MB) potentials representing molecular interactions with adaptive schemes for modeling chemical reactions in solution. The combination of these two components resulted in an adaptive quantum mechanics/many-body (adQM/MB) method that largely suppresses discontinuities between QM and MM regions, which affect existing QM/MM methods, and thus provides an accurate representation of both quantum mechanical and environmental effects through a rigorous description of mutual polarization between QM and MM regions. The implementation of our DD-MB potentials and adQM/MB method in popular software enabled computer simulations of solvation phenomena, reactive processes, and electronic excitations in fluid mixtures with chemical and spectroscopic accuracy, representing a major step toward realistic computer simulations of a wide range of molecular systems relevant to the DOE mission.
Bioinspiration in light harvesting and catalysis
Capturing and converting solar energy into fuels and feedstocks is a global challenge that spans numerous disciplines and fields of research. Billions of years of evolution have allowed natural organisms to hone strategies for harvesting light from the sun and storing energy in the form of carbon–carbon and carbon–hydrogen bonds. Photosynthetic antenna proteins capture solar photons and funnel photoexcitations to reaction centres with high yields, and enzymes catalyze multi-electron reactions, facilitating chemical transformations not yet efficiently implemented using artificially engineered catalysts. Researchers in renewable energy often look to nature to understand the mechanisms at work and, if possible, to explore their translation into artificial systems. Here, we review advances in bioinspiration across the fields of biological light harvesting and chemical energy conversion. We examine how multi-photon and multi-electron reactions in biology can inspire new methods in photoredox chemistry to achieve novel, selective and complex organic transformations; how carbonic-dehydrogenase-inspired design principles enable catalytic reactions such as the conversion of CO 2 into useful products such as fuels; and how concepts from photosynthetic antenna complexes and reaction centres can benefit artificial light-harvesting materials. We then consider areas in which bioinspiration could enable advances in the rational design of molecules and materials, the expansion of the synthetic capabilities of catalysts and the valorization of molecular building blocks. Here, we highlight the challenges that must be overcome to realize these advances and propose new directions that may use bioinspiration to achieve them.
Probing atom dynamics of excited Co-Mo-S nanocrystals in 3D
Advances in electron microscopy have enabled visualizations of the three-dimensional (3D) atom arrangements in nano-scale objects. The observations are, however, prone to electron-beam-induced object alterations, so tracking of single atoms in space and time becomes key to unravel inherent structures and properties. Here, we introduce an analytical approach to quantitatively account for atom dynamics in 3D atomic-resolution imaging. The approach is showcased for a Co-Mo-S nanocrystal by analysis of time-resolved in-line holograms achieving ~1.5 Å resolution in 3D. The analysis reveals a decay of phase image contrast towards the nanocrystal edges and meta-stable edge motifs with crystallographic dependence. These findings are explained by beam-stimulated vibrations that exceed Debye-Waller factors and cause chemical transformations at catalytically relevant edges. This ability to simultaneously probe atom vibrations and displacements enables a recovery of the pristine Co-Mo-S structure and establishes, in turn, a foundation to understand heterogeneous chemical functionality of nanostructures, surfaces and molecules.
Mechanistic insights into the colloidal assembly of mesoporous silica using in-operando cross-scale X-ray scattering and spectroscopic measurements
Advancing a mechanistic understanding of the colloidal to solid-phase transformations of silica allows us to tune these meso-scale structures. In this study, we probe the structural transformations associated with the formation of mesoporous silica particles, SBA-15 using in-operando Small Angle X-ray Scattering (SAXS) and Grazing Incidence-Small Angle X-ray Scattering (GI-SAXS). The chemical transformations associated with the hydrolysis and condensation of silica particles is investigated using Attenuated Total Reflection – Fourier Transform Infrared Spectroscopy (ATR-FTIR) measurements. Fast polymerization of Si-O-Si species to form mesoporous silica is noted when SBA-15 is synthesized in the presence of nitrate salts, due to the “salting-in” effect or enhanced solubility of polymers. However, aging the silica particles resulted in plate-like morphologies in the absence of the nitrate salt and spherical morphologies in the presence of nitrate salt. Early onset of nucleation of silica particles in the presence of nitrate salts was captured using GI-SAXS measurements. These studies show that the rational basis for developing new synthesis routes for accelerated kinetics or specific meso-scale structures can be informed by detailed characterization of the evolution in chemistry and meso-scale structure of these materials.
Understanding the electron-water interaction at the molecular level: Integrating theory and experiment in the cluster regime
In this final technical report, we summarize our major accomplishments resulting from a combined experimental-theoretical approach that exploits the unique properties of molecular clusters to elucidate how local interactions at the molecular level drive the macroscopic behavior of materials and chemical transformations in liquids. The specific targets for our efforts have need to elucidate how charged entities are accommodated by hydrogen-bonding networks and to establish how the behavior of room temperature ionic liquids can be tailored for specific chemical applications. Because of their central importance in aqueous electrolyte chemistry, we have placed particular emphasis understanding the accommodation of excess electrons and protons by water. These surprisingly complex fundamental electrical charges play a fundamental role in a wide range of chemical and biological processes, and yet even a qualitative picture of their speciation in water and at the air-water interface has proven elusive. The main experimental diagnostic in our approach is to measure and analyze vibrational spectra of temperature and composition controlled molecular aggregates that isolate and amplify the spectral markers for local interactions that are often obscured in measurement on bulk or interfacial water. Our combined experimental-theoretical program has led to a greatly enhanced understanding of the nature of excess electrons and protons in water and in the process developed new and powerful methods of chemical analysis that are currently in use across the chemical sciences.
Discovery and characterization of an acridine radical photoreductant
Not provided.Photoinduced electron transfer (PET) is a phenomenon whereby the absorption of light by a chemical species provides an energetic driving force for an electron-transfer reaction. This mechanism is relevant in many areas of chemistry, including the study of natural and artificial photosynthesis, photovoltaics and photosensitive materials. In recent years, research in the area of photoredox catalysis has enabled the use of PET for the catalytic generation of both neutral and charged organic free-radical species. These technologies have enabled previously inaccessible chemical transformations and have been widely used in both academic and industrial settings. Such reactions are often catalysed by visible-light-absorbing organic molecules or transition-metal complexes of ruthenium, iridium, chromium or copper. Although various closed-shell organic molecules have been shown to behave as competent electron-transfer catalysts in photoredox reactions, there are only limited reports of PET reactions involving neutral organic radicals as excited-state donors or acceptors. This is unsurprising because the lifetimes of doublet excited states of neutral organic radicals are typically several orders of magnitude shorter than the singlet lifetimes of known transition-metal photoredox catalysts. Here we document the discovery, characterization and reactivity of a neutral acridine radical with a maximum excited-state oxidation potential of -3.36 volts versus a saturated calomel electrode, which is similarly reducing to elemental lithium, making this radical one of the most potent chemical reductants reported. Spectroscopic, computational and chemical studies indicate that the formation of a twisted intramolecular charge-transfer species enables the population of higher-energy doublet excited states, leading to the observed potent photoreducing behaviour. Finally, we demonstrate that this catalytically generated PET catalyst facilitates several chemical reactions that typically require alkali metal reductants and can be used in other organic transformations that require dissolving metal reductants.
Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale
Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.
Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops
Chemical transformations on catalyst surfaces occur through series and parallel reaction pathways. These complex networks and their behavior can be most simply evaluated through a three-species surface reaction loop (A* to B* to C* to A*) that is internal to the overall chemical reaction. Application of an oscillating dynamic catalyst to this reactive loop has been shown to exhibit one of three types of behavior: (1) a positive net flux of molecules about the loop in the clockwise direction, (2) a negative net flux of molecules about the loop in the counterclockwise direction, or (3) negligible flux of molecules about the loop at the limit cycle of reaction. Three-species surface loops were simulated with microkinetic modeling to assess the reaction loop behavior resulting from a catalytic surface oscillating between two or more catalyst surface energy states. Selected input parameters for the simulations spanned an 11-dimensional parameter space using 127 688 different parameter combinations. Their converged limit cycle solutions were analyzed for their loop turnover frequencies, the majority of which were found to be approximately zero. Classification and regression machine learning models were trained to predict the sign and magnitude of the loop turnover frequency and successfully performed above accessible baselines. Notably, the classification models exhibited a baseline weighted F1 score of 0.49, whereas trained models achieved weighted F1 scores of 0.94 and 0.96 when trained on the parameters used to define the simulations and derived rate constants, respectively. The trained models successfully predicted catalytic loop behavior, and interpretation of these models revealed all input parameters to be important for the prediction and performance of each model.
Experimental Studies of the Dynamics of Adsorbed and Condensed Phase Iodine Species Formed in Severe Reactor Accidents (Final Project Report)
We have studied the chemical transformations of semi-volatile organic iodide species (RI) in the condensed phase as well as adsorbed onto the surfaces of environmentally relevant particles, e.g., soot (carbon) and dust (silica) under real-world conditions. The combination of chosen experimental methods provide a molecular-level insight into the chemical dynamics of organic iodides. Throughout this project, we have developed new experimental, quantum chemistry, and data analysis capabilities. All experimental work was performed with 127 I.
Aligned FTICR-MS, Respiration, and Biochemical Transformation Data from Columbia River Sediment Incubations Associated with: “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism”
Columbia River hyporheic zone sediments from the Hanford Site 300 Area in eastern Washington were incubated in a full factorial design with different organic matter (OM) amendments. The aim of the experiment was to test the influence of OM thermodynamics on aerobic respiration across different organic carbon pools and concentrations. This dataset is comprised of (1) a csv with high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory), (2) a csv with sample IDs, replicates, amendments, dissolved oxygen (DO) measurements, and sampling time points, (3) a csv with sample IDs, replicates, respiration rates, FTICR summary metrics, and relative abundance of chemical classes; and (4) a csv with the number of chemical transformations in each sample replicate (microcosm). The data are associated with the publication “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism” published in ES&T Letters (Garayburu-Caruso et al., 2020). Please use the data package’s DOI to cite the data package. We ask that you acknowledge the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Subsurface Biogeochemical Research (SBR) program when using the data. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include data package authors as co-authors.