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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SchrödingerNet: A Universal Neural Network Solver for the Schrödinger Equation

Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schrödinger equation (SE) by integrating various neural network (NN)-based wave function ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born–Oppenheimer approximation (BOA) and require computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, SchrödingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system. This approach is based on a translationally, rotationally and permutationally symmetry-adapted total wave function ansatz that includes both nuclear and electronic coordinates. Furthermore, this strategy not only allows for an efficient and accurate generation of a continuous potential energy surface at any geometry within the well-sampled nuclear configuration space, but also incorporates non-BOA corrections, through a single training process. Comparison with benchmarks of atomic and small molecular systems demonstrates its accuracy and efficiency.

Chemical calculations↗
DOE OSTI2022

Controlling Structural, Electronic, and Energy Flow Dynamics of Catalytic Processes through Tailored Nanostructures

MoS 2 (molybdenum disulfide) is a highly-versatile catalyst material for support of numerous reactions from hydrodesulfurization and denitrogenation to the focus of this renewal proposal: hydrogenation of CO/CO 2 towards (higher) alcohols. At the same time, MoS 2 is a non-toxic, environmentally-benign and rather inert material- which under ambient conditions for some time has served as a lubricant and, more recently, as next-generation electronic material. The apparent contrast between inertness and stability in ambient, and catalytic activity under reactive conditions is puzzling and calls for a synergistic theoretical and experimental investigation with the long-term objective of enabling the rational design of MoS 2 -based catalysts for alcohol-formation reactions by providing a microscopic understanding of the environmental factors that determines site activity and selectivity. Our research project seeks answers to the questions (a) what conformation does MoS 2 adopt under reaction conditions (as opposed to that under ultrahigh vacuum and low temperatures)?; (b) what reaction pathways exist on such a material?; (c) how can the local environment of the active sites be manipulated so as to make MoS 2 an efficient catalyst for production of higher alcohol from syngas? In particular, research strategies will explore how the basal plane composed of sulfur atoms can be activated so as to exhibit a reactivity of its own, by addressing three research targets and building on extensive preliminary and enabling work: (1) vacancies and vacancy aggregates on the basal plane; (2) non-local catalyst transformation through alkali doping, hydrogenation and phase transition; (3) fabrication of a metal-nanoparticle-activated MoS 2 system, in which particle anchoring, reactive sites and pathways as well as selectivity are controlled by design, as an example of predictive development of a catalyst material,. All strategies are directed to improve the efficacy of the key reactive sites and selectivity of chemical pathways by design, to replace the inefficient methodology of trial and error in catalyst development. This research project represents a synergistic combination of computational guidance, foundational surface-science-based experiments and validation under reactive conditions that aims at transformative new insights into the working of MoS 2 -based hydrogenation-catalysts, a topic squarely at the center of the interest of DOE BES. Guided and led by Talat Rahman, a computational physicist, this project will apply density functional theory to understand structure, reaction pathways and chemical potentials associated with MoS 2 -based CO/CO 2 hydrogenation, augmented by kinetic Monte Carlo methods for reaction rates and prefactors as well as ab-initio molecular dynamics for evaluation of thermal stability. Complementary experimental input and validation will originate from co-PI Ludwig Bartels, a physical chemist and materials scientist, whose group focuses on local imaging and preparation of MoS 2 materials, from co-PI Peter Dowben, an experimental physicist, whose group is expert in the spectroscopy of occupied and unoccupied electronic states, and from senior collaborator Michael White of Brookhaven National Laboratory, whose group generates high-resolution electronic and activity information on size-selected well-defined metal chalcogenide clusters. This collaborative effort will enable a comprehensive understanding of the correlation of structural integrity and catalytic activity of MoS 2 in forms ranging from extended films to individual particles with known geometries and binding sites. Alcohol formation from syngas is a rapidly emerging application that has great potential through facile, economic, and decentralized biomass gasification. CO 2 activation is one of the most pressing concerns of our time: increasing CO 2 levels in the atmosphere change the climate and expose the globe to environmental transformations with the potential for enormous economic and societal impact. We will investigate CO/CO 2 hydrogenation via an interdisciplinary research collaboration with established synergy – one that, in accordance with the mission of the DOE, involves accredited Hispanic-Serving Institutions and that, through student exchange with international collaborators and National Labs, will directly benefit a broad spectrum of communities and generate human resources in sciences essential to their future.

2D materials↗
DOE OSTI2024

Electron-Withdrawing meso -Substituents Turn On Magneto-Optical Activity in Porphyrins

A series of square planar metalloporphyrins (M(TPP), TPP is 5,10,15,20-tetraphenylporphyrin and M(TPFPP), TPFPP is 5,10,15,20-tetrapentafluorophenylporphyrin; M is Zn 2+ , Ni 2+ , Pd 2+ , or Pt 2+ ) with distinct meso-substituents were prepared, and their magneto-optical activity (MOA) was characterized by magnetic circular dichroism (MCD) and magneto-optical rotary dispersion spectroscopy (MORD; also known as Faraday rotation spectroscopy). MOA is crucial in the development of next-generation magneto-optical devices and quantum computing. Here, the data show that the presence of meso-pentafluorophenyl substituents results in significant increase in MOA in comparison to the homologous phenyl group. Differences in the MOA of these metalloporphyrins are rationalized using the Gouterman four-orbital model and pave the way for rational design of improved and tailorable magneto-optical materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Molecular Mechanisms Underlying Surfactant-Based Plastics De-Inking

Surfactant-mediated binder removal is critical for de-inking pretreatment in plastics recycling, yet the molecular mechanisms governing surfactant performance remain poorly understood. We used all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations alongside alkaline surfactant washing experiments to investigate interactions between a series of surfactants and a polyether urethane (PEU) binder in solution and on a polyethylene (PE) surface. Experiments reveal a range of de-inking efficiencies, ranging from <25 to >95% depending upon surfactant headgroup charge and tail length. AA simulations reveal that charged surfactants reach stable levels of surfactant coverage, while nonionic surfactants aggregate on the binder. CG umbrella sampling calculations quantify the thermodynamics of binder desorption in water. In ∼0.25 M surfactant solutions, up to a 52% reduction in the free energy barrier is computed, with trends in good agreement (R 2 = 0.92, Pearson’s r = –0.96, Spearman’s ρ = –0.83) with experimental de-inking efficiencies. We find that charged surfactants are more effective than nonionic surfactants for de-inking and propose three regimes of surfactant de-inking processes: good de-inking occurs in surfactants that promote PEU desorption with a low radius of gyration (R g ); moderate de-inking occurs when surfactants stabilize PEU but increase R g ; poor de-inking occurs in surfactants that aggregate on the binder and promote extensive anchoring to the surface. Overall, these molecular-level insights have the potential to guide the design of surfactant formulations for plastics recycling applications.

ink removal↗
DOE OSTI2021

Multiphase tin equation of state using density functional theory

In this work, we perform density functional theory (DFT) calculations of five solid phases and the liquid phase of tin. The calculations include cold curves of the five solid phases, phonon calculations in the quasiharmonic approximation over a range of volumes for each solid phase, and DFT-based molecular dynamics (DFT-MD) simulations of the liquid phase, including those of the melt curve using the Z method. Using the DFT results, we construct a tabular multiphase sesame equation of state for tin, referred to as sesame 2162. Comparisons to experimental data are made and show a high level of agreement in isobaric data, isothermal data, shock data, and phase boundary measurements, including measurements of the melt curve. The 2162 EOS will be useful for hydrodynamics simulations and has been designed with an eye toward hydrodynamics simulations that incorporate materials strength models and allow for modeling of the kinetics of phase transitions.

3-dimensional systems↗
DOE OSTI2025

Pore2Chip: All-in-one python tool for soil microstructure analysis and micromodel design

The Pore2Chip Python package is designed to create 2D micromodels using extracted data from 3D X-ray computed tomography (XCT) images. This package helps analyze soil structure and function, allowing for the investigation of hydro-biogeochemical processes that impact mineral extraction and reactivity, oxygen concentrations, and nutrient availability in disturbed or managed soils. Key metrics encompass pore size distributions, pore throat size distributions, and connectivity (pore coordination numbers). The final output is a 2D scalable SVG design representing a core or aggregate. Designs can be fabricated with methods such as laser etching, 3D printing, and photolithography.

lab-on-chip↗
DOE OSTI2024

FPGA-Accelerated Range-Limited Molecular Dynamics

Long timescale Molecular Dynamics (MD) simulation of small molecules is crucial in drug design and basic science. To accelerate a small data set that is executed for a large number of iterations, high-efficiency is required. Recent work in this domain has demonstrated that among COTS devices only FPGA-centric clusters can scale beyond a few processors. The problem addressed here is that, as the number of on-chip processors has increased from fewer than 10 into the hundreds, previous intra-chip routing solutions are no longer viable. We find, however, that through various design innovations, high efficiency can be maintained. These include replacing the previous broadcast networks with ring-routing and then augmenting the rings with out-of-order and caching mechanisms. Others are adding a level of hierarchical filtering and memory recycling. Two novel optimized architectures emerge, together with a number of variations. These are validated, analyzed, and evaluated. We find that in the domain of interest speed-ups over GPUs are achieved. Finally, the potential impact is that this system promises to be the basis for scalable long timescale MD with commodity clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electric-field-induced crossover of polarization reversal mechanisms in Al 1–x Sc x N ferroelectrics

Scandium-doped aluminum nitride, Al 1–x Sc x N, represents a new class of displacive ferroelectric materials with high polarization and sharp hysteresis along with high-temperature resilience, facile synthesizability and compatibility with standard CMOS fabrication techniques. The fundamental physics behind the transformation of unswitchable piezoelectric AlN into switchable Al–Sc–N ferroelectrics depends upon important atomic properties such as local structure, dopant distributions and the presence of competing mechanism of polarization switching in the presence of an applied electric-field that have not been understood. We computationally synthesize Al 1–x Sc x N to quantify the inhomogeneity of Sc distribution and phase segregation, and characterize its crystal and electronic structure as a function of Sc-doping. Nudged elastic band calculations of the potential energy surface and quantum molecular dynamics simulations of direct electric-field-driven ferroelectric switching reveal a crossover between two polarization reversal mechanisms—inhomogeneous nucleation-and-growth mechanism originating near Sc-rich regions in the limit of low applied fields and nucleation-limited-switching in the high-field regime. Finally, understanding polarization reversal pathways for these two mechanisms as well as the role of local Sc concentration on activation barriers provides design rules to identify other combinations of dopant elements, such as Zr, Mg etc. to synthesize superior AlN-based ferroelectric materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Effect of Mixed-Solvent Environments on the Selectivity of Acid-Catalyzed Dehydration Reactions

The composition of the liquid phase can alter the rates of individual reaction steps and thus alter the selectivity of acid-catalyzed reactions, but these solvent effects are difficult to anticipate for design purposes. Herein, we report the kinetics and selectivity of Brønsted acid-catalyzed 1,2-propanediol dehydration in pure water and in aqueous mixtures of the polar aprotic cosolvents γ-valerolactone, 1,4-dioxane, tetrahydrofuran, N-methyl-2-pyrrolidone, tetramethylene sulfoxide, and dimethyl sulfoxide at 433 K. We find that the major product of 1,2-propanediol dehydration is propanal in most mixed-solvent environments with selectivities between 1 and 68 mol %. In contrast, 1,2-propanediol dehydration in aqueous mixtures of dimethyl sulfoxide affords acetone as the major product with up to 48% selectivity with minimal propanal formation. We use classical molecular dynamics simulations to probe these solvent effects by computing the difference between the solvation free energies of 1,2-propanediol and propanal in aqueous mixtures of polar aprotic cosolvents and in pure water. We find that the difference in the solvation free energies is correlated with the rates of propanal formation in all mixed-solvent environments, indicating that the solvent-mediated stabilization of the product state relative to the reactant state translates to increased selectivity toward the same product. Similar agreement between simulated solvation free energies and experimental reaction rates/selectivities is established for the acid-catalyzed dehydration of cis- and trans-1,2-cyclohexanediol and 1,3-cyclohexanediol. Finally, analysis of the solvation environment around 1,2-propanediol shows that dimethyl sulfoxide uniquely competes against water to solvate reactive hydroxyl groups, which causes a change in reaction mechanism in this solvent system that leads to the formation of acetone rather than propanal. Here, these results represent a step toward the computationally efficient screening of solvent systems for acid-catalyzed, liquid-phase processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Influence of misfit dislocations on ionic conductivity at oxide interfaces

Mismatched complex oxide thin films and heterostructures have gained significant traction for use as electrolytes in intermediate temperature solid oxide fuel cells, wherein interfaces exhibit variation in ionic conductivity as compared to the bulk. Although misfit dislocations present at interfaces in these structures impact ionic conductivity, the fundamental mechanisms responsible for this effect are not well understood. To this end, a kinetic lattice Monte Carlo (KLMC) model was developed to trace oxygen vacancy diffusion at misfit dislocations in SrTiO 3 /BaZrO 3 heterostructures and elucidate the atomistic mechanisms governing ionic diffusion at oxide interfaces. The KLMC model utilized oxygen vacancy migration energy barriers computed using molecular statics. While some interfaces promote oxygen vacancy diffusion, others impede their transport. Fundamental factors such as interface layer chemistry, misfit dislocation structure, and starting and ending sites of migrating ions play a crucial role in oxygen diffusivity. Molecular dynamics (MD) simulations were further performed to support qualitative trends for oxygen vacancy diffusion. Overall, the agreement between KLMC and MD is quite good, though MD tends to predict slightly higher conductivities, perhaps a reflection of nuanced structural relaxations that are not captured by KLMC. The current framework comprising KLMC modeling integrated with molecular statics offers a powerful tool to perform mechanistic studies focused on ionic transport in thin film oxide electrolytes and facilitate their rational design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Progress Report for W911NF-23-1-0323: Development of Scalable Three-Dimensional Ion Traps for Quantum Information Processing

Trapped atomic ions are a promising platform for scalable quantum information processing. They lead in many key metrics such as single and two qubit gate fidelities, as well as quantum volume. We recently realized novel 3D ion traps with a high resolution 3D-printing process. The traps combine the scalability potential of surface chip traps with the superior trap performance of macroscopic 3D ion traps. Like photolithography, 3D-printing is a digitally defined process and hence allows for rapid iteration between design and fabrication. In contrast to photolithography, it also allows for fully defined 3D structures and, thus, is an ideal process to develop large, well-defined trap arrays with complex 3D electrode structures. Our current goal is to implement key procedures and perform critical measurements that justify incorporation of 3D-printed traps into large-scale quantum computing efforts. En route, we recently realized trapping in horizontal traps that are operating at cryogenic temperatures.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

FY20 Proxy App Suite Release: Report for ECP Proxy App Project Milestone ADCD-504-10

Version 4.0 of the ECP Proxy App Suite is practically unchanged from the previous release. The current set of proxies has proven useful for many aspects of benchmarking and co-design and we see little reason to alter the suite. Although there have been few changes to the ECP suite, the team has been hard at work in other areas. In the area of Machine Learning (ML) we have now created a separate proxy suite dedicated to this scientific applications of ML. The suite includes: miniGAN (Generative Adversarial Networks), miniRL (Reinforcement Learning), CRADL (inline inference), Cosmoflow-Benchmark (Convolutional Neural Network), and MLPerf-DeepCam (Climate Segmentation Benchmark). Section 3 contains more information about these proxies as well as the principles that are guiding the development of the suite. We have surveyed available proxies for several application domains including Computational Fluid Dynamics, Quantum Chemistry, Quantum Computing Simulation, Molecular Dynamics, Monte Carlo Transport, and Density Functional Theory to identify gaps in proxy coverage. Several new proxy apps are either already available or will be released soon to fill these gaps. Section 4 provides full details. Finally, section 5 reports on our continued collaboration with the ECP Continuous Integration (CI) effort to use proxy apps to help identify problems and roadblocks to cross-lab CI. We have also assisted the El Capitan Center of Excellence (COE) to stand up a CI system that will be used to test releases of HPE and AMD software stacks. We hope that the COE effort can serve as a model for ECP by showing how the Proxy App Team can work with AD and ST teams identify critical features, kernels, patterns, etc. and incorporate them into a CI system that will help ensure that Frontier and Aurora will provide those needed capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (M pro ) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC 50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for M pro -ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to M pro . Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hierarchical Self-Assembly Pathways of Peptoid Helices and Sheets

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers. Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with applications in catalysis and molecular separations. Assembly is induced in an organic solvent/water mixture by evaporating the organic phase, but the assembly pathways remain uncharacterized. We conduct all-atom molecular dynamics simulations of Nbrpe6Nc6 as a prototypical amphiphilic diblock peptoid comprising an NH 2 -capped block of six hydrophobic N-((4-bromophenyl)ethyl)glycine residues conjugated to a polar NH 3 (CH 2 ) 5 CO tail. We identify a thermodynamically controlled assembly mechanism by which monomers assemble into disordered aggregates that self-order into 1D chiral helical rods then 2D achiral crystalline sheets. We support our computational predictions with experimental observations of 1D rods using small-angle X-ray scattering, circular dichroism, and atomic force microscopy and 2D crystalline sheets using X-ray diffraction and atomic force microscopy. Furthermore, this work establishes a new understanding of hierarchical peptoid assembly and principles for the design of peptoid-based nanomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrating Experiments and Simulations to Reveal Anisotropic Growth Mechanisms and Interfaces of a One-Dimensional Zeolite

Zeolites are nanoporous crystalline materials critical for diverse industrial applications, yet their growth mechanisms are poorly understood. Here, this study presents a novel integrated framework combining experimental synthesis, high-resolution imaging, coarse-grained molecular dynamics simulations, and computer vision to uncover the mechanisms of growth of SSZ-24, a 1D channel zeolite. We demonstrate how synthesis conditions, such as temperature and reactant concentration, govern crystal anisotropy and surface roughness with growth dynamics differing markedly by crystallographic orientation. Along the channels, growth involves minimal energy barriers and rapid nucleation, resulting in rough surfaces. In contrast, growth perpendicular to the channels requires cooperative molecular organization and is highly sensitive to thermodynamic and kinetic conditions, yielding smooth anisotropic surfaces under low driving forces. By simulating transmission electron microscopy (TEM) images, we bridge molecular-scale simulations with experimental observations, identifying distinct growth mechanisms along different crystal planes. This work offers molecular-level insights into zeolite crystallization, advancing the rational design of nanoporous materials. The integration of cross-disciplinary methodologies establishes a transformative framework for optimizing zeolite synthesis, with implications for broader classes of materials.

Bertolazzo, Andressa A. [Univ. of Utah, Salt Lake ↗
DOE OSTI2021

DOE Energy Frontier Research Centers Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio)

New capabilities to predict, design and control the chemistries of carbon could answer a global imperative to transition from fossil-based to sustainable transportation fuels. While the use of inexpensive hydrocarbons has been an unparalleled achievement and enabler of economic prosperity for many nations, singular dependence upon crude oil has given rise to systemic vulnerabilities in climate, energy, economic, and national security. Lignocellulosic biomass, a renewable and carbon-neutral resource, has the potential to displace an estimated annual equivalent of three billion barrels of oil in the U.S. alone (National Research Council 2009, U. S. Department of Energy, 2011). However, biomass has only one-third the energy density of crude oil (Agrawal and Singh 2009, Richard 2010) and lacks petroleum’s versatility as a feedstock for fuels and chemicals. These limitations keep biomass conversion below the efficiency level needed for strategic impact while the scientific challenge of routing carbon from one molecular context to another remains unmet. In 2009, the Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio) recognized the potential of chemical catalysis and fast pyrolysis to overcome such limitations by transforming the main components of biomass (cellulose, xylan, and lignin) from grasses and trees directly to liquid hydrocarbons and aromatic co-products. Enabled by the EFRC high-risk, high-reward approach to grand challenge science, C3Bio researchers have been key national players in disrupting the conventional paradigm of the cellulosic biorefinery into a new future of “no carbon left behind”—the full utilization of carbon from plant cell walls in energy-dense fuels (Fig. 1). We identified catalytic and fast-pyrolytic pathways that utilize cellulose, xylan and, most significantly, lignin. We developed catalytic processes that deoxygenate and transform monomers and isolated polymers into useful products and tested their use with intact biomass. We gained control of lignin synthesis within plants and initiated tailoring biomass to its end-use through the tools of plant molecular biology and genetic engineering. C3Bio breakthroughs have increased the energy density of biomass-derived substrates via catalytic and pyrolytic conversions into products such as benzoquinones, furfural and hydroxymethylfurfural, levoglucosan and levulinic acid, methoxypropylphenols and propylbenzene. Such advances in biomass conversion would not have been possible without simultaneous advances in analytical instrumentation and methodologies, and imaging technologies and applications. The legacy science developed by C3Bio enables design and control strategies for achieving a targeted product portfolio of fuel and chemical feedstocks from a diverse range of native and tailored biomass. Our research provides the knowledge base required for a bio-economy with product streams as diverse in functionality as those of the petrochemical industry. Coupling targeted computational modeling with experimentation, we achieved: (1) fundamental understanding of biopolymers and cell wall architecture assembly, (2) discovery of new chemistries that allow the development of highly selective pathways to fuels and desirable chemicals, and (3) an integrated systems-level understanding to control catalytic and pyrolytic pathways.

09 BIOMASS FUELS↗