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DOE OSTI2022

Modeling the partitioning of amphiphilic molecules and co-solvents in biomembranes

We report amphiphilic co-solvents can have a significant impact on the structure, organization and physical properties of lipid bilayers. Describing the mutual impact of partitioning and induced structure changes is therefore a crucial consideration for a range of topics such as anesthesia and other pharmacokinetic effects, as well as microbial solvent tolerance in the production of biofuels and other fermentation products, where molecules such as ethanol, butanol or acetic acid might be generated. Small-angle neutron scattering (SANS) is a key method for studying lipid and polymer bilayer structures, with many models for extracting bilayer structure (thickness, area per lipid etc.) from scattering data in use today. However, the molecular details of co-solvent partitioning are conflated with induced changes to bilayer structure, making interpretation and modeling of the scattering curves a challenge with the existing set of models. To address this, a model of a bilayer structure is presented which invokes a two-term partition constant accounting for the localization of the co-solvent within the bilayer. This model was validated using a series of SANS measurements of lipid vesicles in the presence of the co-solvent tetrahydrofuran (THF), showing several strategies of how to deploy the two-parameter partition constant model to describe scattering data and extract both structure and partitioning information from the data. Molecular dynamics simulations are then used to evaluate assumptions of the model, provide additional molecular scale details and illustrate its complementary nature to the data fitting procedure. This approach results in estimates of the partition coefficient for THF in 1,2-dimyristoyl-sn-glycero-3-phosphocholine at 35°C, along with an estimate of the fraction of THF residing in the hydrophobic core of the membrane. The authors envision that this model will be applicable to a wide range of other bilayer/amphiphile interactions and provide the associated code needed to implement this model as a fitting algorithm for scattering data in the SasView suite.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the “feature importance” approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

Chemistry↗

DOE OSTI2020

Solvent Selection for the Separation of Lignin-Derived Monomers Using the Conductor-like Screening Model for Real Solvents

The separation of desired monomers from a liquid-phase mixture of lignin depolymerization products is necessary to facilitate their upscaling and upgrading for industrial applications. One effective method to separate multiple liquid-phase products is countercurrent chromatography (CCC), which is a common liquid chromatography technique that separates target solutes based on differences in their partitioning in a biphasic solvent system. Effective CCC separation requires the selection of solvent compositions to tune solute partition coefficients. Here, to alleviate the experimental burden of selecting optimal solvent systems, we apply the conductor-like screening model for real solvents (COSMO-RS) method to compute partition coefficients of representative lignin monomers from four currently used depolymerization strategies in standard solvent systems. We further design new ternary and quaternary solvent systems that are predicted to further improve separation efficacy. On the basis of these predicted partition coefficients and empirical solvent selection criteria for CCC measurements, we suggest a range of solvent systems that would be suitable for the effective separation of aromatic lignin-derived products via CCC or similar liquid–liquid extraction methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2006

Analysis of N-Nitrosodimethylamine and N-Nitrodimethylamine in Groundwater

A method for the analytical determination of N-nitrosodimethylamine (NDMA) and N-nitrodimethylamine (DMN) at parts-per-trillion (ppt) concentrations in groundwater is reported. The method uses a solid phase extraction (SPE) cartridge containing 2 g of activated coconut charcoal to extract a 500-mL water sample. NDMA and DMN are eluted from the SPE cartridge using acetone. The acetone is concentrated and brought to a final volume of 1.0 mL, which results in a theoretical 500-fold concentration of the analytes. The extracts are analyzed by gas chromatography (GC) with a nitrogenphosphorous detector (NPD), which is a highly sensitive and relatively inexpensive technique. The measured extraction efficiencies averaged 61 percent for NDMA and 74 percent for DMN. Extraction efficiencies were independent of NDMA and DMN concentrations from 40 to 2000 ppt. Several samples could be extracted then analyzed in a single day with the use of an extraction manifold and GC autosampler. A reporting limit of 10 ppt for NDMA and DMN was achieved. The MDLs for NDMA and DMN were 6.4 and 5.8 ppt, respectively. A typical turn-around time from beginning of extraction to reporting was 4 h. The method avoids the use of halogenated solvents, such as dichloromethane, and subsequent solvent exchange procedures necessary for use of the NPD detector.

Greene, Ben↗

NASA NTRS2015

Synthesis and Properties of Cross-Linked Polyamide Aerogels

We report the first synthesis of cross-linked polyamide aerogels through step growth polymerization using a combination of diamines, diacid chloride and triacid chloride. Polyamide oligomers endcapped with amines are prepared as stable solutions in N-methylpyrrolidinone from several different diamine precursors and 1,3-benzenedicarbonyl dichloride. Addition of 1,3,5-benzenetricarbonyl trichloride yields gels which form in under five minutes according to the scheme shown. Solvent exchange of the gels into ethanol, followed by drying using supercritical CO2 extraction gives colorless aerogels with densities around 0.1 to 0.2 gcm3. Thicker monolithes of the polyamide aerogels are stiff and strong, while thin films of certain formulations are highly flexible, durable, and even translucent. These materials may have use as insulation for deployable space structures, rovers, habitats or extravehicular activity suits as well as in many terrestrial applications. Strucure property relationships of the aerogels, including surface area, mechanical properties, and thermal conductivity will be discussed.

Aerogel↗

DOE OSTI2022

A Top-Down Strategy to Engineer ActiveLayer Morphology for Highly Efficient and Stable All-Polymer Solar Cells

A major challenge hindering the further development of all-polymer solar cells (all-PSCs) employing polymerized small-molecule acceptors is the relatively low fill factor (FF) due to the difficulty in controlling the active-layer morphology. The issues typically arise from oversized phase separation resulting from the thermodynamically unfavorable mixing between two macromolecular species, and disordered molecular orientation/packing of highly anisotropic polymer chains. Herein, a facile top-down controlling strategy to engineer the morphology of all-polymer blends is developed by leveraging the layer-by-layer (LBL) deposition. Optimal intermixing of polymer components can be achieved in the two-step process by tuning the bottom-layer polymer swelling during top-layer deposition. Consequently, both the molecular orientation/packing of the bottom layer and the molecular ordering of the top layer can be optimized with a suitable top-layer processing solvent. A favorable morphology with gradient vertical composition distribution for efficient charge transport and extraction is therefore realized, affording a high all-PSC efficiency of 17.0% with a FF of 76.1%. The derived devices also possess excellent long-term thermal stability and can retain >90% of their initial efficiencies after being annealed at 65 °C for 1300 h. In conclusion, these results validate the distinct advantages of employing an LBL processing protocol to fabricate high-performance all-PSCs.

14 SOLAR ENERGY↗

DOE OSTI2025

PNNL-Predictive-Phenomics/ProCaliper

ProCaliper is a Python library that curates, organizes, and computes protein structure features in a way that easily interfaces with user-provided experimental data. It extracts or computes protein binding site, active site, charge, pLDDT (order/disorder), acid dissociation, protonation, solvent accessible surface area, disulfide bond distance, and protein secondary structure data using precomputed protein structures and publicly available databases. It provides a unified API for integrating additional residue-level data and for visualizing residue features in 3D.

Rozum, Jordan [Pacific Northwest National Lab]↗

DOE OSTI2025

Using Separation-Enhanced Isotope Ratio Mass Spectrometry to Enable Increased Renewable Carbon Content in Transportation Fuels (CRADA 525)

Stable isotope ratio measurements of carbon atoms using isotope ratio mass spectrometry (IRMS) can be an effective tool for quantifying biogenic carbon in co-processed fuels, with results approaching the precision and accuracy of accelerator mass spectrometry (AMS). The lower cost of an IRMS may enable deployment to refineries, improving access and analysis turnaround times (≤2 hours), and, by extension, provide data that can allow process optimization to maximize renewable carbon in desired refinery products. This project explored the integration of chemical separation with IRMS analyses to enable highly detailed tracking of biogenic carbon into fuel product streams separated by boiling point range, chemical class, or specific compound. Forty-nine fuels and fuel components of fossil and biogenic origin, spanning gasoline and diesel boiling point ranges, were received from three refiners and were analyzed for their δ 13 C values via IRMS. Results spanned a 13 C range from ca. 10‰ to 44‰ and reflect materials derived from sustainable sources (e.g., C4 or C3 plants, animal-based pathways, syngas) or from fossil-derived fuels. Common ranges are approximately 18‰ to 9‰ and approximately 30‰ to 20‰ for C4 and C3 plants, respectively, and approximately 34‰ to 24‰ and approximately 70‰ to 33‰ for petroleum-derived fuels and methane, respectively. Fuel-like standards were developed and tested using direct-injection elemental analyzer (EA) IRMS for liquid fuels. This method was compared with the published methods, yielding statistically similar results. Four blend curve sets were produced ranging from 0% to 100% of a fuel containing biogenic carbon, focusing on 0% to 10% biogenic carbon. Linear fits were the most applicable for two of the four blend curve sets; however, two sets were found to exhibit slightly quadratic behavior, which was more pronounced in low biogenic blend samples, necessitating second-order fits. The origin of the slight quadratic behavior remains unclear; however, the discussion points to possible interpretations. CanmetENERGY thoroughly characterized a majority of the samples using one- and two-dimensional gas chromatography (GC and GC×GC, respectively) and other analyses. Selected samples were subjected to solid phase extraction (SPE) for saturate, olefin, aromatic, and polar (SOAP) analysis, and the resulting solvent-diluted fractions containing saturates and aromatics were returned to Pacific Northwest National Laboratory (PNNL), where the solvent was removed via evaporation or physical separation using GC techniques. Characterization and separations provided an understanding of saturate and aromatic content, as well as boiling point ranges for each sample and sample fraction. Samples resulting from SPE were examined using EA-IRMS and gas chromatography combustion IRMS (GC-C-IRMS) analyses. Both approaches suggest that the range in values between end-members can be increased by selecting the paraffinic or aromatic fraction of the end-member or by selecting among individual compounds resulting from GC separation of the paraffinic fractions. Considerable work remains to put these approaches into practice and statistically validate the benefit for using a fraction or individual compound over bulk analysis of a sample. However, initial results suggest that separations provide advantages for samples having blend ratios of less than 10% biogenic blendstocks. 13 C results showed statistically similar biofuel blend results to those obtained at PNNL, although additional work is needed to obtain better reproducibility. Select samples were sent to Los Alamos National Laboratory (LANL) for IRMS measurements and Beta Analytics for AMS measurements. This work suggests that IRMS and AMS yield closely comparable results and in some circumstances, IRMS could serve as a surrogate for AMS. While additional work is needed to better resolve statistical advantages for separations and better show the comparable nature of IRMS and AMS in both the biogenic carbon analysis of bulk chemical classes, initial results from this study suggest that these should be pursued in order to proliferate this approach for quantifying biogenic carbon in transportation fuels to the refinery level, thereby potentially enabling process optimization in co-processing scenarios.

09 BIOMASS FUELS↗

DOE OSTI2020

Actinide Separation Inspired by Self-Assembled Metal–Polyphenolic Nanocages

The separation of actinides has a vital place in nuclear fuel reprocessing, recovery of radionuclides, and remediation of environmental contamination. Here we propose a new paradigm of nanocluster-based actinide separation, namely, nanoextraction, that can achieve efficient sequestration of uranium in an unprecedented form of giant coordination nanocages using a cone-shaped macrocyclic pyrogallol[4]arene as the extractant. The U 24 -based hexameric pyrogallol[4]arene nanocages with distinctive [U 2 (PG) 2 ] binuclear units (PG = pyrogallol) that rapidly assembled in situ in monophasic solvent were identified by single-crystal X-ray diffraction, MALDI-TOF mass spectrometry, NMR spectroscopy, and small-angle X-ray and neutron scattering. Furthermore, comprehensive biphasic extraction studies showed that this novel separation strategy has enticing advantages such as fast kinetics, high efficiency, and good selectivity over lanthanides, thereby demonstrating its potential for efficient separation of actinide ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Preparation of ternary hydrophobic magnetic deep eutectic solvents and an investigation into their physicochemical properties

Hydrophobic magnetic deep eutectic solvents (HMDESs) are considered a subclass of DESs comprised of a hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and a metal halide salt. These DESs can be easily manipulated by an external magnet and employed in numerous applications including liquid–liquid extractions and catalysis. In this work, a total of forty-six (46) HMDESs were prepared and their physicochemical properties studied. Six structurally unique ionic and non-ionic HBAs were combined with lanthanide and transition metal halide salts and twelve different HBDs to prepare room temperature eutectic solvents. The viscosities of HMDESs ranged from 46 centipoise (cP) to 124,600 cP at 23 °C. Their magnetic susceptibilities, studied by superconducting quantum interference device (SQUID) magnetometry and a magnetic susceptibility balance, varied from 3.03 Bohr magnetons (μB) to 9.32 μB when the type of metal halide salt and its molar ratio were varied. Thermal stabilities ranged from 151.1 °C to 308.5 °C at 5% mass loss. Further, all HMDESs comprised of ionic HBA were observed to be insoluble in hexane. This study demonstrates that the physicochemical properties of HMDESs can easily be tailored by varying the DES constituents as well as their molar ratio. The structural tunability of these solvents make them highly attractive candidates for a plethora of applications ranging from extractions to electrochromic applications, where HMDESs can be easily manipulated by an external magnetic field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Amidoxime-functionalized materials and their use in extracting metal ions from liquid solutions

A method of producing a functionalized material that extracts metal ions from solution, the method comprising: (i) providing a precursor material having nitrile groups appended to its surface; and (ii) reacting said nitrile groups with hydroxylamine or a derivative thereof in the presence of a polar aprotic solvent at a temperature of 60-80° C. for at least 1 hour, to convert at least a portion of said nitrile groups to amidoxime and imide dioxime groups, followed by reaction with a base capable of hydrolyzing any remaining nitrile groups to carboxylic acid groups; wherein said functionalized material has a higher uranium absorption capacity than a functionalized material produced under same conditions except that the nitrile groups are reacted with hydroxylamine in only a protic solvent. The invention is also directed to functionalized materials produced by the above-described method, and methods for using the functionalized material for extracting metal ions from metal-containing solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Amidoxime-functionalized materials and their use in extracting metal ions from liquid solutions

Janke, Christopher J.↗

DOE OSTI2022

Determination of the Structures of Lignin Subunits and Nanoparticles in Solution by Small‐Angle Neutron Scattering: Towards Improving Lignin Valorization

Abstract Lignin nanoparticles (LNPs) are usually produced from lignin solution through supersaturation. The structure of the lignin in solution is still poorly understood due to structural variability of isolated lignins. Here, lignins were extracted from different plants to establish a general pattern of their structure in several lignin solvents. Lignin molecules (lignin subunits) and larger aggregates were observed in dimethyl sulfoxide (DMSO), ethylene glycol (EG) and 0.1 N NaOD solutions by small‐angle neutron scattering (SANS). It was proposed that the aggregates were composed of lignin subunits with a higher molecular weight and a higher ratio of the aliphatic to phenolic hydroxyl groups. The size, shape, and compactness are important factors that affect the uses of the LNPs, which were obtained from the SANS data for the first time. A discrepancy in the radius between SANS and DLS was discovered, pointing to a large hydration shell around the LNPs in aqueous solutions. The cytotoxicity of the corncob lignin, kraft lignin, and their LNPs were measured and compared.

Zhang, Xin↗

NASA NTRS2014

Alternative Solvents through Green Chemistry Project

Components in the aerospace industry must perform with accuracy and precision under extreme conditions, and surface contamination can be detrimental to the desired performance, especially in cases when the components come into contact with strong oxidizers such as liquid oxygen. Therefore, precision cleaning is an important part of a components preparation prior to utilization in aerospace applications. Current cleaning technologies employ a variety of cleaning agents, many of which are halogenated solvents that are either toxic or cause environmental damage. Thus, this project seeks to identify alternative precision cleaning solvents and technologies, including use of less harmful cleaning solvents, ultrasonic and megasonic agitation, low-pressure plasma cleaning techniques, and supercritical carbon dioxide extraction. Please review all data content found in the Public Data tab located at: https:techport.nasa.govview11697public

Technology Portfolio System↗

DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Analysis of Extracted Ingredients from Dairy Byproduct Using Dimethyl Ether

Whey permeate is a byproduct created in large quantities from the dairy industry with limited applications due to transportation and stability. Whey permeate is primarily comprised of water, carbohydrates, and minerals with minimal contribution of proteins. Various biomasses in food processing have utilized liquid dimethyl ether (DME) as a form of extraction for high-value ingredients and dewatering. DME is a growing interest in wastewater and biomass treatment due to it being a green solvent, ability to dissolve organic solvents, and miscibility with water. The current study is to determine if DME can effectively dewater whey permeate as well as recover components that can be further used in the food industry as a value-added ingredient. Analysis of the liquid extract and solid extract was evaluated with inductively coupled plasma-mass spectrometry (ICP-MS) and solid extracts were further characterized with the application of a scanning electron microscope (SEM).

09 - BIOMASS FUELS↗

DOE OSTI2023

Insight into the Radiolytic Degradation Mechanism of TODGA

Partitioning and transmutation schemes, where americium is separated from other components of used nuclear fuel and burned in a fast neutron reactor, offer a path to dramatically decrease the space requirements for storage of nuclear fuel cycle wastes, allowing more efficient usage of potentially scarce storage resources.[1] Improvements in the efficiency of processes for separating americium from used nuclear fuels can thus have a significant impact on the costs of future nuclear fuel cycles. Understanding separation ligand radiation chemistry is important for development of new ligands and processes, as the ligand degradation products can have a deleterious effect on a separation process. Tetraoctyl diglycolamide (TODGA) has been studied as a ligand for lanthanide/minor actinide extraction for partitioning and transmutation schemes. However, the mechanism of initial energy transfer from products of solvent radiolysis to TODGA is still under debate. One proposed mechanism is attack by a n-dodecane radical cation resulting in electron abstraction and formation of a TODGA radical cation,[2] while later work has proposed hydrogen abstraction from sites adjacent to the central ether oxygen by other radical species.[3] The latter mechanism is proposed to result in degradation products from rupture of the ether bond. However, radiolytic degradation of TODGA in n-dodecane shows products that would result from rupture of all the backbone bonds. There is little information on what degradation products would be expected to form from the TODGA radical cation. In this work, we have begun to investigate this by examining the electron impact (EI) ionization mass spectrum of TODGA, which initially produces a TODGA radical cation in the gas phase. The EI spectrum shows fragments that would result from most backbone bond cleavages, similar to what is observed in the radiolysis of TODGA in n-dodecane. As the lifetime of the solvent radical cation decreases from n-dodecane to n-hexane,[4] irradiation of TODGA in n-hexane should have less solvent radical cations available for reaction with TODGA, and instead should favor reactions with longer-lived radicals. In contrast to n-dodecane, radiolytic degradation of TODGA in n-hexane produces only N,N-dioctylacetamide, which would result from rupture of the ether bond. The n-dodecane radical cation lifetime is long enough to produce TODGA radical cations but the n-hexane radical cation is mostly consumed before reaction with TODGA, so other radical processes that abstract hydrogen from TODGA dominate. This suggests that electron abstraction by the n-dodecane radical cation is an important component of TODGA radiolysis, although it does not rule out the presence of mechanisms involving hydrogen abstraction adjacent to the ether oxygen. References [1] J. Serp et al., Energies 2017, 10 (9), DOI: 10.3390/en10091445. [2] C. Zarzana et al., Solvent Extr. Ion Exch. 2015, 33 (5), 431–447, DOI: 10.1080/07366299.2015.1012885. [3] T. Koubský et al., Prog. Nucl. Energy 2017, 94, 208–215, DOI: 10.1016/j.pnucene.2016.07.010. [4] F. Sviridenko et al., Chem. Phys. Lett. 1998, 297 (3), 343–349, DOI: 10.1016/S0009-2614(98)01099-9.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗