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DOE OSTI2021

Strategies for Post-Synthetic Functionalization of Mesoporous Carbon Nanomaterial Surfaces

Most widely used carbon-based materials feature a bulk framework tolerant of extreme pH and temperature conditions while maintaining physical robustness, electrical conductivity, and biological relevance due to its inherent nontoxicity. At the nanoscale, the study of porous carbons has been of interest because of the enhanced accessible surface area relative to nonporous analogues. The evolution of synthetic techniques has developed structural allotropes with tunable internal pore diameter averages spanning from sub-nanometer to micron-plus sizes. Within the mesopore range, surface chemical phenomena abide by a kinetic-limited ruleset (ignoring the effects of bulk diffusion, as they do not pertain to this size regime). Further, mesopores provide sufficient volume and surface area for hosting guest species, including metal nanoparticles (MNP), polymers, small organics, and enzymes. These two unique features of mesoporous carbon (MC) drive interest in surface modifications to create new physicochemical functionality to these systems and their hosted species. As a result, the evolution of surface modification techniques has been scattered to disparate niches across the literature. This review compiles the array of elegant methods by which these carbon systems are topically engineered.

functionalization↗

DOE OSTI2023

Synthetic Access to a Framework-Stabilized and Fully Sulfided Analogue of an Anderson Polyoxometalate that is Catalytically Competent for Reduction Reactions

Polyoxometalates (POMs) featuring 7, 12, 18, or more redox-accessible transition metal ions are ubiquitous as selective catalysts, especially for oxidation reactions. The corresponding synthetic and catalytic chemistry of stable, discrete, capping-ligand-free polythiometalates (PTMs), which could be especially attractive for reduction reactions, is much less well developed. Among the challenges are the propensity of PTMs to agglomerate and the tendency for agglomeration to block reactant access of catalyst active sites. Nevertheless, the pervasive presence of transition metal sulfur clusters metalloenzymes or cofactors that catalyze reduction reactions and the justifiable proliferation of studies of two-dimensional (2D) metal-chalcogenides as reduction catalysts point to the promise of well-defined and controllable PTMs as reduction catalysts. Here, we report the fabrication of agglomeration-immune, reactant-accessible, capping-ligand-free Co II Mo 6 IV S 24 n– clusters as periodic arrays in a water-stable, hierarchically porous Zr-metal–organic framework (MOF; NU1K) by first installing a disk-like Anderson polyoxometalate, Co III Mo 6 VI O 24 m– , in size-matched micropores where the siting is established via difference electron density (DED) X-ray diffraction (XRD) experiments. Flowing H 2 S, while heating, reduces molybdenum(VI) ions to Mo(IV) and quantitatively replaces oxygen anions with sulfur anions (S 2– , HS – , S 2 2– ). DED maps show that MOF-templated POM-to-PTM conversion leaves clusters individually isolated in open-channel-connected micropores. Importantly, the structure of the immobilized cluster as determined, in part, by X-ray photoelectron spectroscopy (XPS), X-ray absorption fine structure (XAFS) analysis, and pair distribution function (PDF) analysis of total X-ray scattering agrees well with the theoretically simulated structure. PTM@MOF displays both electrocatalytic and photocatalytic competency for hydrogen evolution. Nevertheless, the initially installed PTM appears to be a precatalyst, gaining competency only after the loss of ~3 to 6 sulfurs and exposure to hydride-forming metal ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

“Multiagent” Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Data Science-Driven Analysis of Substrate-Permissive Diketopiperazine Reverse Prenyltransferase NotF: Applications in Protein Engineering and Cascade Biocatalytic Synthesis of (-)-Eurotiumin A

Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to > 99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, in vitro biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Inference of phase field fracture models

The phase field approach to modeling fracture uses a diffuse damage field to represent cracks. This representation mollifies singularities that arise in computations with sharp interface models and some of the resultant difficulties in the mathematical and numerical treatment of fracture. Phase field fracture models have proven effective at representing crack propagation, branching, and merging. Specific formulations, beginning with brittle fracture, have also been shown to converge to classical solutions. Extensions to cover the range of material failure, including ductile and cohesive fracture, lead to an array of possible models. There exists a large body of literature focusing on this class of models and on the impact of model form on the predicted crack evolution. However, there have not been systematic studies into how optimal models may be chosen. Here, we take a first step in this direction by developing formal methods for identification of the best parsimonious model of phase field fracture given full-field data on the damage and deformation fields. We consider some of the main models that have been used for the degradation of elastic response due to damage and its propagation. Our approach builds upon Variational System Identification (VSI), a weak form variant of the Sparse Identification of Nonlinear Dynamics (SINDy). Furthermore, in this first communication we focus on synthetically generated data but we also consider central issues associated with the use of experimental full-field data, such as data sparsity and noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Locating the missing large-scale emission in the jet of M87* with short EHT baselines

In very-long baseline interferometric arrays, nearly co-located stations probe the largest scales and typically cannot resolve the observed source. In the absence of a large-scale structure, closure phases constructed with these stations are zero and, since they are independent of station-based errors, they can be used to probe data issues. Here, we show how these trivial closure phases become nonzero with a brightness distribution on smaller scales than their short baseline would suggest. When applied to sources that are made up of a bright compact and large-scale diffuse component, the trivial closure phases directly measure the centroid relative to the compact source and higher-order image moments. We present a technique to measure these image moments with minimal model assumptions and validate it on synthetic Event Horizon Telescope (EHT) data. We then apply this technique to 2017 and 2018 EHT observations of M87* and find a weak preference for extended emission in the direction of the large-scale jet. We also apply it to 2021 EHT data and measure the source centroid about 1 mas northwest of the compact ring, which is consistent with the jet observed at lower frequencies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. Here this paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

14 SOLAR ENERGY↗

DOE OSTI2020

A Review of Microwave-Assisted Synthesis-Based Approaches to Reduce Pd-Content in Catalysts

This review article focuses on the latest advances in the synthesis of inorganic nano-catalysts using microwave heating, which has progressed significantly since its initial implementation in the mid-1980s. Over the years, nanoparticles (NPs), which inherently offer better surface accessibility for heterogeneous catalysis, have been synthesized using a wide array of heating methods. Microwave heating is one such method and employs a unique heating mechanism that can have several benefits for catalysis. When compared to conventional form of heating which relies on inter-layer mixing via convection, microwave heating operates through the chemical polarity in the target chemicals leading to an “inside-out” mode of heating. This heating mechanism is more targeted and therefore results in rapid synthesis of catalytically active NPs. Platinum group metals (PGM) have classically been the focus of nano-catalysis; however, recent efforts have also applied non-PGM group metals with the goals of lower costs, and ideally, improved catalytic reactivity and durability. This is especially of interest with respect to Pd because of its current historically high cost. Investigations into these new materials have primarily focused on new/improved synthetic methods and catalytic compositions, but it is important to note that these approaches must also be economic and scalable to attain practical relevance. With this overarching goal in mind, this review summarizes notable recent findings with a focus on Pd-dilution and microwave heating in a chronological fashion.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

N -Arylimide Molecular Balances: A Comprehensive Platform for Studying Aromatic Interactions in Solution

Noncovalent interactions of aromatic surfaces play a key role in many biological processes and in determining the properties and utility of synthetic materials, sensors, and catalysts. However, the study of aromatic interactions has been challenging because these interactions are usually very weak and their trends are modulated by many factors such as structural, electronic, steric, and solvent effects. Recently, N-arylimide molecular balances have emerged as highly versatile and effective platforms for studying aromatic interactions in solution. These molecular balances can accurately measure weak noncovalent interactions in solution via their influence on the folded–unfolded conformational equilibrium. The structure (i.e., size, shape, π-conjugation, and substitution) and nature (i.e., element, charge, and polarity) of the π-surfaces and interacting groups can be readily varied, enabling the study of a wide range of aromatic interactions. These include aromatic stacking, heterocyclic aromatic stacking, and alkyl–π, chalcogen–π, silver–π, halogen–π, substituent–π, and solvent–π interactions. The ability to measure a diverse array of aromatic interactions within a single model system provides a unique perspective and insights as the interaction energies, stability trends, and solvent effects for different types of interactions can be directly compared. Some broad conclusions that have emerged from this comprehensive analysis include: (1) The strongest aromatic interactions involve groups with positive charges such as pyridinium and metal ions which interact with the electrostatically negative π-face of the aromatic surface via cation–π or metal–π interactions. Attractive electrostatic interactions can also form between aromatic surfaces and groups with partial positive charges. (2) Electrostatic interactions involving aromatic surfaces can be switched from repulsive to attractive using electron-withdrawing substituents or heterocycles. These electrostatic trends appear to span many types of aromatic interactions involving a polar group interacting with a π-surface such as halogen–π, chalcogen–π, and carbonyl–π. (3) Nonpolar groups form weak but measurable stabilizing interactions with aromatic surfaces in organic solvents due to favorable dispersion and/or solvophobic effects. Furthermore, a good predictor of the interaction strength is provided by the change in solvent-accessible surface area. (4) Solvent effects modulate the aromatic interactions in the forms of solvophobic effects and competitive solvation, which can be modeled using solvent cohesion density and specific solvent–solute interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases

MoorDyn, an open-source mooring dynamics model, is being expanded with capabilities for additional mooring system features and load cases. As floating wind turbine technology matures, mooring systems are becoming more sophisticated and more complex scenarios need to be considered in the design process. Mooring systems may have synthetic line materials, ballast/buoyancy bodies along the lines, or interconnections between platforms. Failure modes may involve multiple cascading line failures that depend on mooring system dynamics. Features recently added to MoorDyn aim to address these emerging needs. MoorDyn’s linear elasticity model has been supplemented to support user-defined stress-strain curves, which can be adjusted to represent synthetic mooring materials. Rigid six-degree-of-freedom bodies in the mooring system can now be modeled using two new model objects. “Rod” objects provide an option for rigid cylindrical bodies. They use the existing Morison equation-based hydrodynamics model and can be connected to mooring lines at either end. “Body” objects provide a generic six-degree-of-freedom rigid-body representation based on a lumped-parameter model of translational and rotational properties. Rod objects can be added to Body objects and mooring lines can be attached at any location, allowing a wide variety of submerged structures to be integrated into the mooring system. Lastly, a means of dynamically simulating mooring line failures has been implemented. These new features, currently in the C++ version of MoorDyn, are described and then demonstrated on a two-turbine shared-mooring array. A qualitative view of the results suggests the new features are functioning as expected.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2020

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases: Preprint

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2021

DNTTD (Distributed Non-Negative Tensor Train Decomposition)

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exa flops also come the extra-large high-dimensional data generated by high performance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and real world big datasets.

Bhattarai, Manish↗

DOE OSTI2022

A Unified Approach to Decarboxylative Halogenation of (Hetero)aryl Carboxylic Acids

Aryl halides are a fundamental motif in synthetic chemistry, playing a critical role in metal-mediated cross-coupling reactions and serving as important scaffolds in drug discovery. Although thermal decarboxylative functionalization of aryl carboxylic acids has been extensively explored, the scope of existing halodecarboxylation methods remains limited, and there currently exists no unified strategy that provides access to any type of aryl halide from an aryl carboxylic acid precursor. Herein, we report a general catalytic method for direct decarboxylative halogenation of (hetero)aryl carboxylic acids via ligand-to-metal charge transfer. This strategy accommodates an exceptionally broad scope of substrates. Here, we leverage an aryl radical intermediate toward divergent functionalization pathways: (1) atom transfer to access bromo- or iodo(hetero)arenes or (2) radical capture by copper and subsequent reductive elimination to generate chloro- or fluoro(hetero)arenes. The proposed ligand-to-metal charge transfer mechanism is supported through an array of spectroscopic studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Meso bromination and derivatization of synthetic bacteriochlorins

Here, the ability to prepare and tailor synthetic analogues of native bacteriochlorophylls enables diverse applications. A de novo route entails dimerization of a dihydrodipyrrin-acetal to afford the corresponding 5-methoxy and/or 5-unsubstituted bacteriochlorin, wherein each pyrroline ring contains a gem-dimethyl group to ensure stability toward adventitious dehydrogenation. The presence of a 5-methoxy group facilitates bromination at the distal meso-(15-)position. While bromination of 5-unsubstituted bacteriochlorins typically affords a mixture of brominated products, here the presence of two substitution patterns (2,12-dicarboethoxy, 2,12-diacetyl) has been found to facilitate selective meso-bromination in the absence of the methoxy substituent. The introduction of a single meso-bromine atom in a bacteriochlorin opens opportunities for Pd-mediated derivatization, which include (1) preparation of four ethynylphenyl building blocks (and two benchmark bacteriochlorins) with long-wavelength absorption bands tuned across 725–757 nm, for use in preparation of multichromophore arrays; (2) installation of a bioconjugatable group to free base bacteriochlorins or a copper bacteriochlorin, the latter for possible use in photoacoustic imaging; and (3) installation of an S-acetylthio group for surface attachment. Altogether, 25 new bacteriochlorins are described including 5 meso-bromobacteriochlorin intermediates and 12 target bacteriochlorins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Synthesis and functionalization of electron-deficient perfluoroalkyl porphyrin building blocks for supermolecular systems

Synthetic strategies for electron-deficient meso-perfluoroalkylporphyrins bearing diverse functional groups are described. Scalable and efficient syntheses for 5-triisopropylsilylethynyl-10,15,20-tris(heptafluoropropyl)porphyrin and 5-triisopropylsilylethynyl-10,20-bis(heptafluoropropyl)porphyrin that equip meso-ethynyl functional groups via the bilane route have been established, along with a refined route to [5,15-bis(heptafluoropropyl)porphinato]zinc(II). meso-Position halogenation of [5,15-bis(heptafluoropropyl)porphinato]zinc(II) was achieved by selective meso-nitration and subsequent reduction, diazonium salt formation, and iodination reactions. Computational data describe the low energy excited states of these chromophores and the electronic structural factors that control reactivity of these meso-perfluoroalkyl substituted porphyrin complexes. meso-Functionalized [5-triisopropylsilylethynyl-10,20-bis(heptafluoropropyl)porphinato]zinc(II) and [5-iodo-10,20-bis(heptafluoropropyl)porphinato]zinc(II) building blocks lay the foundation for the construction of highly conjugated multiporphyrin arrays that feature electronic structural properties important for the development of n-type materials and high potential photooxidants.

Chemistry↗

DOE OSTI2023

A Membrane-Associated Light-Harvesting Model is Enabled by Functionalized Assemblies of Gene-Doubled TMV Proteins

Photosynthetic light harvesting requires efficient energy transfer within dynamic networks of light-harvesting complexes embedded within phospholipid membranes. Artificial light-harvesting models are valuable tools for understanding the structural features underpinning energy absorption and transfer within chromophore arrays. Here, a method for attaching a protein-based light-harvesting model to a planar, fluid supported lipid bilayer (SLB) is developed. The protein model consists of the tobacco mosaic viral capsid proteins that are gene-doubled to create a tandem dimer (dTMV). Assemblies of dTMV break the facial symmetry of the double disk to allow for differentiation between the disk faces. A single reactive lysine residue is incorporated into the dTMV assemblies for the site-selective attachment of chromophores for light absorption. Further, on the opposing dTMV face, a cysteine residue is incorporated for the bioconjugation of a peptide containing a polyhistidine tag for association with SLBs. The dual-modified dTMV complexes show significant association with SLBs and exhibit mobility on the bilayer. The techniques used herein offer a new method for protein-surface attachment and provide a platform for evaluating excited state energy transfer events in a dynamic, fully synthetic artificial light-harvesting system.

59 BASIC BIOLOGICAL SCIENCES↗