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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

We report surrogate mixtures are routinely used for understanding gasoline fuel combustion in engine simulations. The general trend in surrogate formulation has been to increase the number of fuel components in a mixture to better emulate real fuel properties. Recently, a new surrogate design strategy based on functional group analysis of real gasolines was proposed using a minimal number of species [minimalist functional group (MFG)-approach]. MFG surrogates (having just one or two components) could experimentally capture the ignition delay time (IDT), threshold sooting index, and smoke point of different gasoline fuels with hundreds of components. However, other combustion characteristics were not explored, and kinetic modeling of MFG surrogates was not reported. These aspects are addressed in this paper, where the combustion behavior of MFG surrogates for various gasolines was assessed by simulating IDT, jet-stirred reactor oxidation, and premixed laminar flame speeds using chemical kinetic modeling. MFG simulations were compared with experimental data of the real gasolines as well as with the more complex multicomponent (five to nine species) surrogates. This study reveals that binary MFG surrogate mixtures are capable of accurately simulating the combustion behavior of more complex gasoline fuels with hundreds of components.

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

Surveying Phase Modifier Functional Groups for Applications to Ln(III) Separations

The application of N,N,N',N'-tetraoctyl diglycolamide (TODGA) in solvent extraction systems for lanthanide (Ln) separations is well understood. In these systems, the formation of a third phase has motivated the use of phase modifiers to enable higher concentrations of H + and Ln common to industrial processes. Several different phase modifiers with applications to diglycolamide (DGA) systems have previously been reported, with a focus on tri-n-butyl phosphate (TBP), N,N'-dihexylactanamide (DHOA), N,N-dioctyl-2-hydroxyacetamide (DOHyA), N,N'-dimethyl-N,N'-dioctylhexylethoxy malonamide (DMDOHEMA), and octanol. While the primary utility of phase modifiers is the increased metal loading, they can have significant effects on the metal distribution ratios, which are well described by the energetics of the extraction process itself. However, the mechanisms by which phase modifiers impact distribution ratios are not generally understood. This work considers the ability of phase modifiers to affect Ln distribution ratios by using phase modifiers with two different functional groups (–Cl and –C≡N) and an octyl alkyl chain in a TODGA and n-dodecane system. Determining the effect of chlorooctane and octane nitrile is important for understanding how phase modifier functional groups and their hydrogen bonding interactions affect Ln extraction. Through combining distribution ratio measurements with organic phase spectroscopic investigations, the impact of chlorooctane and octane nitrile on Ln extraction and their inner-sphere complexes is reported. The addition of either chlorooctane or octane nitrile to TODGA in n-dodecane decreases Ln extraction while maintaining the same inner-sphere Ln complex. The lack of change in inner-sphere Ln-TODGA coordination upon incorporation of phase modifiers and the significant impact of these phase modifiers on distribution ratios suggest the importance of a supramolecular structure. Understanding the role of chlorooctane and octane nitrile on the organic phase structure at longer length scales has been identified as an avenue for future investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Influence of functional groups on low-temperature combustion chemistry of biofuels

Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, ; (ii) competing unimolecular decomposition of and bimolecular reaction of with O2; (iii) decomposition mechanisms of peroxy radical adducts (RO), including isomerization via RO ⇌ OOH. Furthermore, all three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms.

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

Electrocatalytic Hydrotreatment of Bio-Oil: Exploring Interactions Between Functional Groups

Electrocatalytic hydrotreatment (ECH) is being explored as a sustainable route for upgrading bio-oil to renewable fuels and chemicals. Bio-oil, produced by the fast pyrolysis of lignocellulosic biomass, is a complex mixture of compounds with various oxygen-containing functional groups, such as anhydrosugars, carboxylic acids, ketones, aldehydes, furans, phenols and alcohols. The ECH of several bio-oil model compound binary mixtures was conducted to investigate the interactions between these functional groups. Notably, phenolic compound reduction was significantly inhibited in the presence of aldehydes, particularly furfural. A strategy involving the reagent-based reduction of the aldehyde to an alcohol prior to ECH was shown to partially mitigate this inhibitory effect. Additionally, qualitative studies on the ECH of catalytic fast pyrolysis (CFP) oil with low aldehyde content showed promising results. These studies achieved the conversion of cyclopentenones and phenolic compounds present in the CFP oil to cyclopentanols and cyclohexanols, respectively.

09 BIOMASS FUELS↗
DOE OSTI2021

Determination of Carbon Functional Groups in Pyrolysis Bio-Oils using 13 C NMR: Laboratory Analytical Procedure (LAP)

Pyrolysis is a process that can be used to convert biomass to solid, liquid and gaseous products for use as renewable chemicals and fuels. The liquid fraction, known as “bio-oil” is complex and challenging to characterize, particularly by means of GC/MS, GPC, LC and FT-IR. NMR is capable of analyzing whole bio-oil samples and can provide quantitative results to characterize different functional groups or types of carbon present in bio-oil. This Laboratory Analytical Procedure (LAP) quantifies different carbon functional groups in whole bio-oil samples. This information can be used for comparisons between different pyrolysis experimental conditions or different upgrading processes and catalysts, and also allow for comparisons between bio-oils produced at different facilities.

09 BIOMASS FUELS↗
DOE OSTI2025

The impact of second coordination sphere functional group extension on product selectivity for manganese bipyridyl CO 2 reduction electrocatalysts

Utilizing the well established manganese bipyridyl class of homogeneous electrocatalyst, four new ligands are studied to probe the influence of distal, outer coordination sphere, H-bonding and steric effects on product selectivity for proton-coupled electrocatalytic CO 2 reduction. The presence of a simple acetate functional group in the second coordination sphere provides a high selectivy for CO 2 -to-CO conversion irrespective of proton source (H 2 O vs. PhOH) or applied potential. The o-(methoxybenzoate)phenyl second/outer coordination sphere at the bipyridyl 6,6′-positions imparts poor product selectivity. In contrast, upon conjugation of the acetate functional group with the N-Boc-alanine moiety, a CO : HCO 2 − product selectivity of ∼1 : 1 is observed at the high overpotential catalytic wave (for both H 2 O and PhOH acids). Computed enthalpy and free energy of activation parameters suggest that selective CO 2 insertion at the manganese hydride transition state is favored, over protonation, consistent with negligible hydrogen production during controlled potential electrolysis studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Han, Ming [Univ. of Chicago, IL (United States)]↗
DOE OSTI2025

Unravelling the Glycan Code: Molecular Dynamics and Quantum Chemistry Reveal How O-Glycan Functional Groups Govern OgpA Selectivity in Mucin Degradation by Akkermansia muciniphila

Mucins, heavily O-glycosylated glycoproteins, are a key component of mucus, and certain gut microbiota, including Akkermansia muciniphila , can utilise mucin glycans as a carbon source. Akkermansia muciniphila produces the O-glycopeptidase enzyme OgpA, which cleaves peptide bonds at the N-terminus of serine (Ser) or threonine (Thr) residues carrying O-glycan substitutions, with selectivity influenced by the O-glycan functional groups. Using molecular dynamics (MD) simulations and quantum chemistry calculations, we explored how different O-glycan groups affect OgpA's selectivity. Our results show that peptides bind to the enzyme via hydrogen bonds, π–π interactions, van der Waals forces and electrostatic interactions, with key residues, including Tyr90, Val138, Gly176, Tyr210 and Glu91, playing important roles. The primary determinant of selectivity is the interaction between the peptide's functional group and the enzyme's binding cavity, while peptide–enzyme interface interactions are secondary. Quantum chemistry calculations reveal that OgpA prefers peptides with a lower electrophilic character. This study provides new insights into mucin degradation by gut microbiota enzymes, advancing our understanding of this critical biological process.

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

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗
NASA NTRS2023

Capitol Reef Ecological Conservation: Mapping Vegetation Functional Groups to Inform Invasive Vegetation Management, Ecological Conservation and Restoration in Capitol Reef National Park

Invasive exotic plant (IEP) species have been found within the park boundaries of Capitol Reef National Park (CARE) in Utah. Currently, remotely sensed datasets such as the Rangeland Analysis Platform (RAP) from the United States Department of Agriculture (USDA) have been used to investigate IEP species within the park, but validation of the national RAP program is necessary for informing decisions at a local scale. CARE seeks a remote monitoring solution that can precisely target managerial efforts within the park’s challenging terrain and hard-to-reach locations. To fulfill this objective, we harnessed Landsat 8 Operational Land Imager (OLI) imagery and leveraged Random Forest (RF) modeling to generate classification maps characterizing vegetation functional groups for 2013 and 2022 within the park. Subsequently, the Land Change Modeler (LCM) in Idrisi TerrSet facilitated the production of a predicted classification map for 2033. The team also devised an annual grass probability map to accentuate areas impacted by exotic grasses. A comparative assessment between the RF classification map and the RAP map for 2022 revealed an overall agreement of 47.41%, with disparities primarily arising from differences in bare soil and shrub areas. Significantly, the 2022 RF-generated classification map showcased an impressive overall accuracy of 92.17%. In short, the probability map, the land cover change detection spanning 2013 to 2022, and the forecasting of observed trends into the future aids in the evaluation of invasive plant impacts and facilitation of CARE’s preparedness for potential ecological disturbances. Notably, in comparison to the RAP, the RF classification method generates functional group maps that are more representative of the study area.

Vanchy Li↗
DOE OSTI2024

Species interactions amplify functional group responses to elevated CO 2 and N enrichment in a 24‐year grassland experiment

Abstract Plant functional groups (FGs) differ in their response to global changes, although species within those groups also vary in such responses. Both species and FG responses to global change are likely influenced by species interactions such as inter‐specific competition and facilitation, which are prevalent in species mixtures but not monocultures. As most studies focus on responses of plants growing in either monocultures or mixtures, but rarely both, it remains unclear how interspecific interactions in diverse ecological communities, especially among species in different FGs, modify FG responses to global changes. To address these issues, we leveraged data from a 16‐species, 24‐year perennial grassland experiment to examine plant FG biomass responses to atmospheric CO 2 , and N inputs at different planted diversity. FGs differed in their responses to N and CO 2 treatments in monocultures. Such differences were amplified in mixtures, where N enrichment strongly increased C3 grass success at ambient CO 2 and C4 grass success at elevated CO 2 . Legumes declined with N enrichment in mixtures at both CO 2 levels and increased with elevated CO 2 in the initial years of the experiment. Our results suggest that previous studies that considered responses to global changes in monocultures may underestimate biomass changes in diverse communities where interspecific interactions can amplify responses. Such effects of interspecific interactions on responses of FGs to global change may impact community composition over time and consequently influence ecosystem functions.

Mohanbabu, Neha↗
DOE OSTI2021

Peatland microbial community responses to plant functional group and drought are depth-dependent

Peatlands store one-third of Earth's soil carbon, the stability of which is uncertain due to climate change-driven shifts in hydrology and vegetation, and consequent impacts on microbial communities that mediate decomposition. Peatland carbon cycling varies over steep physicochemical gradients characterizing vertical peat profiles. However, it is unclear how drought-mediated changes in plant functional groups (PFGs) and water table (WT) levels affect microbial communities at different depths. We combined a multiyear mesocosm experiment with community sequencing across a 70-cm depth gradient, to test the hypotheses that vascular PFGs (Ericaceae vs. sedges) and WT (high vs. low) structure peatland microbial communities in depth-dependent ways. Several key results emerged. (i) Both fungal and prokaryote (bacteria and archaea) community structure shifted with WT and PFG manipulation, but fungi were much more sensitive to PFG whereas prokaryotes were much more sensitive to WT. (ii) PFG effects were largely driven by Ericaceae, although sedge effects were evident in specific cases (e.g., methanotrophs). (iii) Treatment effects varied with depth: the influence of PFG was strongest in shallow peat (0-10, 10-20 cm), whereas WT effects were strongest at the surface and middle depths (0-10, 30-40 cm), and all treatment effects waned in the deepest peat (60-70 cm). Furthermore, our results underline the depth-dependent and taxon-specific ways that plant communities and hydrologic variability shape peatland microbial communities, pointing to the importance of understanding how these factors integrate across soil profiles when examining peatland responses to climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

On ambiguities and divergences in perturbative renormalization group functions

There is an ambiguity in choosing field-strength renormalization factors in the $ \overline{\mathrm{MS}} $ scheme starting from the 3-loop order in perturbation theory. More concerning, trivially choosing Hermitian factors has been shown to produce divergent renormalization group functions, which are commonly understood to be finite quantities. We demonstrate that the divergences of the RG functions are such that they vanish in the RG equation due to the Ward identity associated with the flavor symmetry. It turns out that any such divergences can be removed using the renormalization ambiguity and that the use of the flavor-improved β-function is preferred. We show how our observations resolve the issue of divergences appearing in previous calculations of the 3-loop SM Yukawa β-functions and provide the first calculation of the flavor-improved 3-loop SM β-functions in the gaugeless limit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Electron-donating functional groups strengthen ligand-induced chiral imprinting on CsPbBr3 quantum dots

Abstract Chiral perovskite nanoparticles and films are promising for integration in emerging spintronic and optoelectronic technologies, yet few design rules exist to guide the development of chiral material properties. The chemical space of potential building blocks for these nanostructures is vast, and the mechanisms through which organic ligands can impart chirality to the inorganic perovskite lattice are not well understood. In this work, we investigate how the properties of chiral ammonium ligands, the most common organic ligand type used with perovskites, affect the circular dichroism of strongly quantum confined CsPbBr 3 nanocrystals. We show that aromatic ammonium ligands with stronger electron-donating groups lead to higher-intensity circular dichroism associated with the lowest-energy excitonic transition of the perovskite nanocrystal. We argue that this behavior is best explained by a modulation of the exciton wavefunction overlap between the nanocrystal and the organic ligand, as the functional groups on the ligand can shift electron density toward the organic species-perovskite lattice interface to increase the imprinting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Functional group effect of chemically modified microcrystalline methyl cellulose on thermoplastic polyurethane composites

Microcrystalline cellulose (MCC) is a promising bio-based filler for lightweight yet mechanically high-performance eco-polymer composites because of its low density and high biocompatibility. However, intermolecular hydrogen bonding among MCC is stronger than the polymer–filler interactions, which deteriorate the mechanical properties of the composites. Herein, we investigated the effects of functional groups on the mechanical properties of composites by scrutinizing chemically modified microcrystalline methyl cellulose (m-MMC) with three different substitution levels of hydroxyl group to hydroxypropyl (HP) group: no-, low-, and high-level substitution (HP-0, HP-low, and HP-high). The degree of HP substitution of m-MMC was quantitatively measured by CP/MAS NMR analysis. The relatively bulky HP groups interrupted the filler–filler intermolecular interactions and reduced the crystallinity and density of m-MMC, as evident from X-ray diffractometer and pycnometer data, respectively. For scalable production of the composites, the m-MMC were compounded with thermoplastic polyurethane (TPU) by a twin-screw extruder at concentrations between 0.5 and 10 wt%. Despite its low filler concentration, the toughness of m-MMC/TPU composites was remarkably enhanced, up to 28% (229.2 to 294.4 MJ/m 3 ) at 0.5 wt% loading of HP-low, owing to the enhanced polymer–filler interactions. In conclusion, the fundamental understanding on structure–property relationships will provide insights for designing of mechanically robust yet eco-friendly polymer composites.

composites↗
NASA NTRS2012

Sensitivity of Ocean Reflectance Inversion Models for Identifying and Discriminating Between Phytoplankton Functional Groups

The daily, synoptic images provided by satellite ocean color instruments provide viable data streams for observing changes in the biogeochemistrY of marine ecosystems. Ocean reflectance inversion models (ORMs) provide a common mechanism for inverting the "color" of the water observed a satellite into marine inherent optical properties (lOPs) through a combination of empiricism and radiative transfer theory. lOPs, namely the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents, describe the contents of the upper ocean, information critical for furthering scientific understanding of biogeochemical oceanic processes. Many recent studies inferred marine particle sizes and discriminated between phytoplankton functional groups using remotely-sensed lOPs. While all demonstrated the viability of their approaches, few described the vertical distributions of the water column constituents under consideration and, thus, failed to report the biophysical conditions under which their model performed (e.g., the depth and thickness of the phytoplankton bloom(s)). We developed an ORM to remotely identifY Noctiluca miliaris and other phytoplankton functional types using satellite ocean color data records collected in the northern Arabian Sea. Here, we present results from analyses designed to evaluate the applicability and sensitivity of the ORM to varied biophysical conditions. Specifically, we: (1) synthesized a series of vertical profiles of spectral inherent optical properties that represent a wide variety of bio-optical conditions for the northern Arabian Sea under aN Miliaris bloom; (2) generated spectral remote-sensing reflectances from these profiles using Hydrolight; and, (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N Miliaris for each example. By comparing the estimates from the inversion model to those from synthesized vertical profiles, we were able to identifY those bio-optical conditions under which the inversion model performs both well and poorly.

Werdell, P. Jeremy↗
NASA NTRS1999

Classification of ion mobility spectra by functional groups using neural networks

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Non-NASA Center↗
DOE OSTI2022

Establishing substitution rules of functional groups for high-capacity organic anode materials in Na-ion batteries

Tailoring molecular structures of organic electrode materials (OEMs) can enhance their performance in Na-ion batteries, however, the substitution rules and the consequent effect on the specific capacity and working potential remain elusive. Herein, by examining three sodium carboxylates with selective N substitution or extended conjugation structure, we exploited the correlation between structure and performance to establish substitution rules for high-capacity OEMs. Our results show that substitution position and types of functional groups are essential to create active centers for uptake/removal of Na+ and thermodynamically stabilize organic structures. Furthermore, rational host design and electrolytes modulation were performed to extend the cycle life to 500 cycles. A full cell based on the optimal 2,2’-bipyridine-4,4’-dicarboxylic acid disodium salt anode and the polyaniline cathode is demonstrated to confirm the feasibility of achieving all-organic batteries. Lastly, this work provides a valuable guideline for the design principle of high-capacity and stable OEMs for sustainable energy storage.

25 ENERGY STORAGE↗