Search NASA⌕ Search

SEARCH · Search NASA

Results for “chemicals”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Engineering Challenges of Solution and Slurry-Phase Chemical Hydrogen Storage Materials for Automotive Fuel Cell Applications

In this work, we present the research findings of the DOE-funded Hydrogen Storage Engineering Center of Excellence (HSECoE) related to liquid-phase and slurry-phase chemical hydrogen storage media and their potential as future hydrogen storage media for automotive applications. Chemical hydrogen storage media other than neat liquid compositions will prove difficult to meet the DOE system level targets. Solid- and slurry-phase chemical hydrogen storage media requiring off-board regeneration are impractical and highly unlikely to be implemented for automotive applications because of the formidable task of developing solid- or slurry-phase transport systems that are commercially reliable and economical throughout the entire life cycle of the fuel. Additionally, the regeneration cost and efficiency of chemical hydrogen storage media is currently the single most prohibitive barrier to implementing chemical hydrogen storage media. Ideally, neat liquid-phase chemical hydrogen storage media with net-usable gravimetric hydrogen capacities of greater than 7.8 wt% are projected to meet the 2017 DOE system level gravimetric and volumetric targets. The research presented herein is a collection of research findings that do not in and of themselves warrant a dedicated manuscript. However, the collection of results do, in fact, highlight the engineering challenges and short-comings in scaling up and demonstrating fluid-phase ammonia borane and alane compositions that all future materials researchers working in hydrogen storage should be aware of.

25 ENERGY STORAGE↗
DOE OSTI2022

The Chemical Composition of Extreme-velocity Stars* †

Abstract Little is known about the origin of the fastest stars in the Galaxy. Our understanding of the chemical evolution history of the Milky Way and surrounding dwarf galaxies allows us to use the chemical composition of a star to investigate its origin and to say whether it was formed in situ or was accreted. However, the fastest stars, the hypervelocity stars, are young and massive and their chemical composition has not yet been analyzed. Though it is difficult to analyze the chemical composition of a massive young star, we are well versed in the analysis of late-type stars. We have used high-resolution ARCES/3.5 m Apache Point Observatory, MIKE/Magellan spectra to study the chemical details of 15 late-type hypervelocity star candidates. With Gaia EDR3 astrometry and spectroscopically determined radial velocities we found total velocities with a range of 274–520 km s −1 and mean value of 381 km s −1 . Therefore, our sample stars are not fast enough to be classified as hypervelocity stars, and are what is known as extreme-velocity stars. Our sample has a wide iron abundance range of −2.5 ≤ [Fe/H] ≤ −0.9. Their chemistry indicates that at least 50% of them are accreted extragalactic stars, with iron-peak elements consistent with prior enrichment by sub-Chandrasekhar mass Type Ia supernovae. Without indication of binary companions, their chemical abundances and orbital parameters indicate that they are the accelerated tidal debris of disrupted dwarf galaxies.

Reggiani, Henrique (ORCID:0000000165336179)↗
DOE OSTI2024

A chemical kinetics theory for interpreting the non-monotonic temperature dependence of enzymatic reactions

Abstract. One notable observation of enzymatic chemical reactions is that, for a given abundance of enzymes and substrates, temperature increases cause reaction rates to first increase consistent with the Arrhenius relationship, then plateau, and finally fall off quickly to zero at high temperatures. While many mathematical functions have been used to describe this pattern, we here propose a chemical kinetics theory which successfully replicates this observation and provides insights into the processes responsible for these dynamics. The chemical kinetics theory combines the law of mass action, von Smoluchowski's diffusion-limited chemical reaction theory, and Eyring's transition state theory. This new theory reveals that the thermally reversible enzyme denaturation ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution explains the plateau and subsequent decrease in chemical reaction rates with increasing temperature. The temperature-dependent affinity parameter (K) that relates enzymes and substrates through their binding also affects the shape of the emergent temperature response. We demonstrate that with an increase in substrate availability, K shifts the optimal temperature, where reaction rates plateau, towards higher values. Further, we show that the chemical kinetics theory accurately represents 12 sets of published enzyme assay data and includes the popular mechanistic model by Ratkowsky et al. (2005) as a special case. Given its good performance and solid theoretical underpinning, we believe this new theory will facilitate the construction of more mechanistic-based environmental biogeochemical models.

Tang, Jinyun↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2020

Evaluation of a New Chemical Mechanism for 2-Amino-2-methyl-1-propanol in a Reactive Environment from CSIRO Smog Chamber Experiments

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO 2 from flue gas streams in amine-based post-combustion CO 2 capture plants, and it is expected to result in AMP emission and secondary products formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. As such, in this work we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of VOCs–NOx surrogate mixtures over a range of initial amine concentrations. O 3 formation was significantly inhibited when AMP was added to the surrogate VOCs–NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e. ratio of aerosol formed to reacted AMP) of 1.06±0.20. Based on updated knowledge of its kinetics, oxidation pathways and product yields, we have developed a new mechanism (designated as CSIAMP-19) and integrated into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP–VOCs–NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. The updated AMP chemical reactions scheme can be further embedded into the chemical transport model for regional modelling scenarios where AMP-related emissions are of concern.

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

Toho-1 β-lactamase: backbone chemical shift assignments and changes in dynamics upon binding with avibactam

Backbone chemical shift assignments for the Toho-1 β-lactamase (263 amino acids, 28.9 kDa) are reported based on triple resonance solution-state NMR experiments performed on a uniformly 2 H, 13 C, 15 N-labeled sample. These assignments allow for subsequent site-specific characterization at the chemical, structural, and dynamical levels. At the chemical level, titration with the non-β-lactam β-lactamase inhibitor avibactam is found to give chemical shift perturbations indicative of tight covalent binding that allow for mapping of the inhibitor binding site. At the structural level, protein secondary structure is predicted based on the backbone chemical shifts and protein residue sequence using TALOS-N and found to agree well with structural characterization from X-ray crystallography. At the dynamical level, model-free analysis of 15 N relaxation data at a single field of 16.4 T reveals well-ordered structures for the ligand-free and avibactam-bound enzymes with generalized order parameters of ~0.85. Complementary relaxation dispersion experiments indicate that there is an escalation in motions on the millisecond timescale in the vicinity of the active site upon substrate binding. The combination of high rigidity on short timescales and active site flexibility on longer timescales is consistent with hypotheses for achieving both high catalytic efficiency and broad substrate specificity: the induced active site dynamics allows variously sized substrates to be accommodated and increases the probability that the optimal conformation for catalysis will be sampled.

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

Carbon-Assisted, Continuous Syngas Production in a Chemical Looping Scheme

In the current energy and environment scenario, it is imperative to develop energy efficient routes for chemical manufacturing that also pave the way for mitigation of greenhouse gas emissions. This work presents an efficient pathway for continuous syngas production via a chemical looping conversion of the two most potent greenhouse gases—CH 4 , and CO 2 . The well-known dry-reforming process of converting CH 4 , and CO 2 to syngas is energy-intensive and suffers from catalyst deactivation. The chemical looping approach, on the other hand, provides avenues for mitigating catalyst deactivation and enabling improved energy efficiency. The key to such process enhancements lies in the intricate structure–function relationships of the catalyst and its correlation to the process variables. We present the reduction and oxidation characteristics of 5 wt.% Ni/Ce 1-x Zr x O 2 -based catalysts (x = 0, 0.4, and 0.625). We demonstrate low temperature CH4 activation over Ni-promoted samples as opposed to pure Ce 1-x Zr x O 2 . Moreover, our results depict an optimum regeneration of these catalysts when oxidized by CO 2 , and H 2 O, which allows for chemical looping operation of steam reforming of methane as well. Process variables were tuned to optimize the CH 4 conversion (over 80%), and H 2 /CO ratio at 650 °C. The critical surface reactions—carbon accumulation and gasification, and thermocatalytic CO 2 splitting were investigated to elucidate the dynamic nature of the catalyst surface. In conclusion, the impact of this work lies in showcasing the opportunities to design chemical looping reactors for energy efficient syngas production from waste greenhouse gases.

25 ENERGY STORAGE↗
DOE OSTI2022

Effects of chemical segregation on ductility-anisotropy in high strength Fe-Mn-Al-C lightweight austenitic steels

Ductile Fe-Mn-Al-C lightweight steels offer great potential as high specific strength materials for weight critical applications. In the present work, very high yield (~1 GPa) and ultimate tensile strength levels (~1.5 GPa) were obtained along the rolling and transverse directions of Fe-30Mn-8.5Al-0.9Si-0.9C-0.5Mo (wt.%), with high tensile elongation to failure of ~35%. This excellent mechanical behavior was attributed to the nanoscale, ordered κ-carbide precipitates formed during aging after rolling. However, tensile ductility was not isotropic through all directions of the rolled plate. In particular, the ductility was consistently lower than 5% true strain along the normal direction. Upon detailed microstructural investigations, micro-scale lamellar chemical micro-segregation bands were detected, which caused the observed anisotropic embrittlement along certain plate directions. These bands resulted in low failure strains along the normal direction since it is oriented perpendicular to these chemical segregation bands, resulting in a failure type reminiscent of delamination-induced cleavage fracture. Chemical segregation causes an inhomogeneous κ-carbide precipitate distribution upon aging, producing more pronounced mechanical anisotropy after peak-aging as compared to the solution heat treated samples. Surprisingly, deformation twinning was observed after tensile deformation of solution-heat treated specimens via electron-backscatter diffraction experiments, which is anomalous for a high stacking faulty energy (SFE) material. Further, it was proposed that chemical micro-segregation promotes locally different SFE regions, leading to deformation twinning in this nominally high SFE material. Overall, this work demonstrates the effects of chemical segregation on mechanical properties and microstructural evolution in a high performance, lightweight Fe-Mn-Al-C steel.

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

New insights into fuel blending effects: Intermolecular chemical kinetic interactions affecting autoignition times and intermediate-temperature heat release

Fuel blending effects on chemically-dominated fuel properties, such as gasoline anti-knock quality, are influenced by fundamental chemical kinetic interactions between the blending agent and the base fuel. Historically, quantification of such interactions has focused on changes to the radical pool, including $\dot{O}$H and HO 2 , while intermolecular interactions pertaining to carbonated, non-fuel-specific intermediates are typically overlooked. In this regard, this work aims to derive new insight into intermolecular chemical kinetic interactions that are intrinsic to fuel blending effects via a case study on blends of 0–30% ethanol (by volume) into FGF-LLNL (a multi-component gasoline surrogate for FACE-F research gasoline) using a rapid compression machine at a diluted/stoichiometric fuel loading, compressed pressure of 40 bar and low- to intermediate-temperature regimes that are representative of boosted SI engine operation. Ethanol blending effects on the intermediate temperature heat release (ITHR) of FGF-LLNL are characterized using experimental measurements, where ethanol is found to promote the extent of ITHR and suppress the transition from ITHR to main ignition. Chemical kinetic modeling is undertaken using a recently updated gasoline surrogate model. Sensitivity analyses on ITHR characteristics further corroborate the ethanol blending effects, and highlight the significant dependence of ITHR on both fuel-specific and non-fuel-specific reactions. An approach allowing comprehensive characterization of the complex intermolecular chemical kinetic interactions between constitutes in a fuel blend is then proposed. Application of the approach to FGF-LLNL/E0–E30 reveals that ethanol perturbs the heat release and autoignition characteristics of FGF-LLNL not only by directly changing the $\dot{O}$H and HO 2 radical pools via fuel-specific reactions, but also through intermolecular interactions where participating intermediates can be produced and consumed by various sub-chemistries. Disabling the intermolecular interactions in carbonated species between ethanol and FGF-LLNL sub-chemistries leads to somewhat slower ITHR evolution and lower ignition reactivity. The role of the individual intermolecular interaction is also characterized using the proposed approach. Lastly, implications of intermolecular interactions for future studies that aim to improve model performance are highlighted, where it is found that further investigations on the core C0-C4 chemistry are needed for developing highly accurate chemistry models for complex fuel blends.

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

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗
DOE OSTI2026

Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Navigating and analyzing large chemical spaces are necessary to accelerate the design and discovery of new molecules and chemical processes. In this work, we introduce a computational framework that integrates graph theory and integer programming to enable the efficient navigation of large chemical spaces. Our framework represents the chemical space as a graph, wherein nodes represent molecules and edges represent the degree of similarity or connectivity based on domain-specific information. Using the graph representation, we identify representative molecules by computing the so-called minimum dominating set (MDS), which in our context is the minimum set of molecules that is connected to all other molecules. We present a suite of solution strategies for the MDS problem including heuristic and rigorous integer programming (IP) approaches. We show that these approaches allow us to capture physicochemical properties and domain-specific logic and constraints, facilitating the identification of molecules with the target properties. We demonstrate the effectiveness of the proposed approach by navigating the chemical space of per- and polyfluoroalkyl substances (PFAS); this comprises approximately 15,000 molecular structures. We compare our framework against traditional dimensionality reduction and clustering methods such as t-SNE and K-means clustering.

Chemical structure↗
DOE OSTI2023

Chemical and Physical Drivers for Improvement in Permeance and Stability of Linker-Free Graphene Oxide Membranes

Graphene oxide (GO) is a promising membrane material for chemical separations, including water treatment. However, GO has often required postsynthesis chemical modifications, such as linkers or intercalants, to improve either the permeability, performance, or mechanical integrity of GO membranes. In this work, we explore two different feedstocks of GO to investigate chemical and physical differences, where we observe up to a 100× discrepancy in the permeability–mass loading trade-off while maintaining nanofiltration capacity. GO membranes also show structural stability and chemical resilience to harsh pH conditions and bleach treatment. Here we probe GO and the resulting assembled membranes through a variety of characterization approaches, including a novel scanning-transmission-electron-microscopy-based visualization approach, to connect differences in sheet stacking and oxide functional groups to significant improvements in permeability and chemical stability.

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

Laponite-Incorporated UiO-66-NH2-Polyethylene Oxide Composite Membranes for Protection against Chemical Warfare Agent Simulants

A strategy is developed to enhance the barrier protection of polyethylene oxide (PEO)-metal–organic framework (MOF) composite films against chemical warfare agent simulants. To achieve enhanced protection, an impermeable high-aspect-ratio filler in the form of Laponite RD (LRD) clay platelets was incorporated into a composite PEO film containing MOF UiO-66-NH 2 . The inclusion of the platelets aids in mitigating permeation of inert hydrocarbons (octane) and toxic chemicals (2-chloroethyl ethyl sulfide, 2-CEES) of dimensions/chemistry similar to prominent vesicant threats while still maintaining high water vapor transport rates (WVTR). By utilizing small-angle neutron scattering, small-angle X-ray scattering, and wide-angle X-ray scattering, the LRD platelet alignment of the films was determined, and the structure of the films was correlated with performance as a barrier material. Performance of the membranes against toxic chemical threats was assessed using permeation testing of octane and 2-CEES, a common simulant for the vesicant mustard gas, and breathability of the membranes was assessed using WVTR measurements. To assess their robustness, chemical exposure (in situ diffuse reflectance infrared Fourier transform spectroscopy) and mechanical (tensile strength) measurements were also performed. It was demonstrated that the barrier performance of the film upon inclusion of the LRD platelets exceeds that of other MOF-polymer composites found in the literature and that this approach establishes a new path for improving permselective materials for chemical protection applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Facile Interfacial Reduction Suppresses Redox Chemical Expansion and Promotes the Polaronic to Ionic Transition in Mixed Conducting (Pr,Ce)O 2−δ Nanoparticles

Mixed ionic/electronic conductors (MIECs) are essential components of solid-state electrochemical devices, such as solid oxide fuel/electrolysis cells. For efficient performance, MIECs are typically nanostructured, to enhance the reaction kinetics. However, the effect of nanostructuring on MIEC chemo-mechanical coupling and transport properties, which also impact cell durability and efficiency, has not yet been well understood. Here, in this work, Pr 0.2 Ce 0.8 O 2−δ (PCO20) nanopowders were prepared by coprecipitation, then sintered in a modified dilatometer at three different temperatures (600, 725, and 850 °C) for microstructure evolution, resulting in three samples with different average particle sizes (23, 30, and 53 nm). The chemical strain and electronic/ionic conductivity were then measured simultaneously on stable nanostructures in four isotherms from 550 to 400 °C with steps in pO 2 (1 to 10 –4 atm O 2 ). A microcrystalline bar was prepared and measured for comparison. Particle size reduction led to a monotonically decreasing isothermal redox chemical strain, confirmed by in situ high-temperature, controlled-atmosphere XRD measurements. The corresponding conductivity measurements provided defect chemical insight into the particle size-dependent chemical expansion behavior. The significant weakening of the pO 2 dependence and decreased activation energy for electrical conduction with decreasing particle size indicated a decrease in the reduction enthalpy of PCO, shifting the transition from (Pr) polaronic to ionic behavior to higher pO 2 . STEM-EELS measurements confirmed the majority of Pr was reduced to 3+ in the nanoparticles, while Ce remained 4+. These results demonstrate suppression of deleterious chemical expansion and tailoring of the dominant charge carrier simply through controlling the particle size, providing insights for MIEC microstructural design.

ceria↗
DOE OSTI2021

Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally “innocent” Na 2 CO 3 precursor to enable the metathesis synthesis of single-phase Y 2 Mn 2 O 7 : an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li 2 CO 3 and K 2 CO 3 under identical conditions. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y 2 Mn 2 O 7 , thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. Finally, this approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

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

Relative Source Time Functions, Spectral Ratios, and Near‐Source Spallation in the Source Physics Experiment Phase I Chemical Explosions

The Source Physics Experiment (SPE) Phase I was composed of six chemical explosions at the Nevada National Security Sites (NNSS) between 2011 and 2016. The experiment provided a robust set of dense, local to regional distance observations suitable for characterizing shallow chemical explosions located within the same borehole. We investigate the time-dependent source processes of each chemical explosion using Relative Source Time Function (RSTF) estimation, derived from five linear seismometer profiles located between 100 and 2000 m of the shot location. The RSTF estimate provide a detailed characterization of each chemical explosion's time-history. Subsequent modeling efforts suggest these measurements may be useful for precise characterization of explosion processes and spallation. RSTF estimation allows us to expand our understanding of the SPE Phase I chemical explosion series. Application of these techniques contributes to our understanding of explosion source physics and facilitates future applications to additional seismic source experiments and characterization of explosion phenomenology.

58 GEOSCIENCES↗
DOE OSTI2022

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗