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

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Direct structural retrieval from gas-phase ultrafast diffraction data using a genetic algorithm

Ultrafast scattering techniques such as ultrafast electron diffraction and ultrafast x-ray diffraction have been utilized to elucidate the structural dynamics, reaction intermediates, and final products in molecular reactions following photoexcitation. The time-dependent structures are typically not directly retrieved from the experimental data, but they rely on comparison with calculations. The genetic algorithm (GA), a global optimization strategy, can be used to retrieve the molecular structures directly from diffraction patterns without any theoretical input. However, the robustness of the GA with respect to real experimental conditions such as a limited momentum transfer range, noise, and artifacts has not been studied in detail. In this work, we characterize the performance of the GA with simulated data that mimic realistic experimental conditions. We have developed and implemented a variant of the GA specific to diffraction measurements which performs better in the presence of imperfect data compared to the standard implementation of the GA. We demonstrate this method with both synthetic data and experimental ultrafast electron diffraction data on the UV-induced photodissociation of trifluoroiodomethane (C⁢F 3⁡ I) molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC-Juniata-hydroDL2) that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale hydrologic flood simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding Proton Movement in [Fe-Fe] Hydrogenases

Nature uses specialized metalloenzymes to carry out small molecule activation reactions, including CO 2 fixation, O 2 activation, and proton reduction, with unparalleled efficiency, rates, and selectivity. The latter reactions are performed by hydrogenases, protein metallo-complexes that interconverts H 2 to protons and electrons (H 2 oxidation) and the reverse reaction (H 2 production) with incredibly low energy input and amazingly fast kinetics. Reproducing both the activity and efficiency of metalloenzymes in sustainable anthropogenic systems remains one of the “holy grails” of inorganic chemistry. However, identifying the precise molecular components responsible for these desirable properties has been challenging in the natural metalloenzymes, hindering efforts to develop analogous processes in synthetic compounds. Considering the inherent complexity of a metalloenzyme and the many interactions, both strong and weak, that contribute to the function of an enzyme, we have elected to model natural metalloenzymes on a biochemical platform. Towards this end, we have a developed structural, functional, and mechanistic mimic of the [Ni-Fe] hydrogenases within a robust protein scaffold, rubredoxin, to understand the influence of the secondary coordination environment on the metal center. This involved preparing a series of three rubredoxin constructs containing a single point mutation at Val positions and making the NMR chemical shift assignments for the paramagnetic (nickel-substituted) and non-paramagnetic (zinc-substituted) form. These physical studies were complemented with in silico molecular dynamic studies on proteins with [Fe-S] clusters to engineer new proton channels to test in vitro . To assist our search for new natural metalloprotein scaffolds within the vast number of sequenced genomes, we created a neural network-based program to identify proteins with specific metal-binding sites. These computational and physical studies with metalloenzymes provide direct insight into the fundamental chemical principles driving the natural systems and offer design principles for developing catalysts that utilize analogous principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2020

Hybrid Ceramic-CMC Vane with EBC for Future Coal Derived Syngas Fired 65% Efficient Turbine Combined Cycle

The efficiency of both simple cycle and combined cycle power generation systems scale with the peak temperature at which the gas exits the combustor to drive the turbine. In conventional systems, a substantial fraction of the total turbine core flow exiting the compressor is diverted downstream to cool metallic turbine hardware rather than power the turbine, much of which is used to cool the first-stage turbine vane. The use of coal derived syngas fuels provides an additional challenge to the lifetime of materials utilized in the turbine, as particulate byproducts created in the coal gasification process melt in the combustion gas, and can subsequently deposit and interact with the turbine hardware. The development of durable hot-section materials capable of operating at temperature well above that of single crystal superalloy airfoil/zirconia based thermal barrier coatings is critical to realizing 65% efficient coal derived syngas fired gas turbine based power systems. To enable higher turbine inlet temperatures while lowering cooling air requirements, United Technologies Research Center (UTRC), the central R&D laboratory supporting UT Pratt & Whitney, led the conceptual design of a new type of ceramic composite turbine hot section materials system. The design focused on a novel hybrid monolithic ceramic-fiber reinforced ceramic matrix composite (CMC) first stage turbine vane having an environmental barrier coating. By utilizing ceramic construction in the turbine hot-section, the core flow normally used to cool metallic components will be substantially reduced, increasing efficiency and reducing emissions. To provide the framework for future demonstration testing, UTRC partnered with University of North Dakota Energy and Environmental Research Center (UNDEERC) to provide a conceptual design for a gasified coal fed high-pressure turbine combustor system designed to mimic the conditions expected in a future 65% fuel to busbar efficient syngas fueled gas turbine based combined cycle. The UNDEERC and UTRC collaborated on characterizing dusts from coal gasifier filtration systems.

10 SYNTHETIC FUELS↗
DOE OSTI2023

nautilus : boosting Bayesian importance nested sampling with deep learning

ABSTRACT We introduce a novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Unlike rejection-based sampling methods such as vanilla nested sampling (NS) or Markov chain Monte Carlo (MCMC) algorithms, importance sampling techniques can use all likelihood evaluations for posterior and evidence estimation. However, for efficient importance sampling, one needs proposal distributions that closely mimic the posterior distributions. We show how to combine INS with deep learning via neural network regression to accomplish this task. We also introduce nautilus, a reference open-source python implementation of this technique for Bayesian posterior and evidence estimation. We compare nautilus against popular NS and MCMC packages, including emcee, dynesty, ultranest, and pocomc, on a variety of challenging synthetic problems and real-world applications in exoplanet detection, galaxy SED fitting and cosmology. In all applications, the sampling efficiency of nautilus is substantially higher than that of all other samplers, often by more than an order of magnitude. Simultaneously, nautilus delivers highly accurate results and needs fewer likelihood evaluations than all other samplers tested. We also show that nautilus has good scaling with the dimensionality of the likelihood and is easily parallelizable to many CPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Trigonal Bipyramidal V 3+ Complex as an Optically Addressable Molecular Qubit Candidate

Synthetic chemistry enables a bottom-up approach to quantum information science, where atoms can be deterministically positioned in a quantum bit or qubit. Two key requirements to realize quantum technologies are qubit initialization and read-out. By imbuing molecular spins with optical initialization and readout mechanisms, analogous to solid-state defects, molecules could be integrated into existing quantum infrastructure. To mimic the electronic structure of optically addressable defect sites, we designed the spin-triplet, V 3+ complex, (C 6 F 5 ) 3 trenVCN t Bu (1).We measured the static spin properties as well as the spin coherence time of1demonstrating coherent control of this spin qubit with a 240 GHz electron paramagnetic resonance spectrometer powered by a free electron laser. We found that1exhibited narrow, near-infrared photoluminescence (PL) from a spin-singlet excited state. Using variable magnetic field PL spectroscopy, we resolved emission into each of the ground-state spin sublevels, a crucial component for spin-selective optical initialization and readout. This work demonstrates that trigonally symmetric, heteroleptic V 3+ complexes are candidates for optical spin addressability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Deconstruction of plant biomass by a Cellulomonas strain isolated from an ultra-basic (lignin-stripping) spring

Plant material falling into the ultra-basic (pH 11.5-11.9) springs within The Cedars, an actively serpentinizing site in Sonoma County, California, is subject to conditions that mimic the industrial pretreatment of lignocellulosic biomass for biofuel production. In this research, we sought to obtain hemicellulolytic/cellulolytic bacteria from The Cedars springs that are capable of withstanding the extreme alkaline conditions wherein calcium hydroxide-rich water removes lignin, making cell wall polysaccharides more accessible to microorganisms and their enzymes. We enriched for such bacteria by adding plant debris from the springs into a synthetic alkaline medium with ground tissue of the biofuel crop switchgrass (Panicum virgatum L.) as the sole source of carbon. From the enrichment culture we isolated the facultative anaerobic bacterium Cellulomonas sp. strain FA1 (NBRC 114238), which tolerates high pH and catabolizes the major plant cell wall-associated polysaccharides cellulose, pectin, and hemicellulose. Strain FA1 in monoculture colonized the plant material and degraded switchgrass at a faster rate than the community from which it was derived. Cells of strain FA1 could be acclimated through subculturing to grow at a maximal concentration of 13.4% ethanol. A strain FA1-encoded β-1, 4-endoxylanase expressed in E. coli was active at a broad pH range, displaying near maximal activity at pH 6-9. Discovery of this bacterium illustrates the value of extreme alkaline springs in the search for microorganisms with potential for consolidated bioprocessing of plant biomass to biofuels and other valuable bio-inspired products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

DISCo-microbe: design of an identifiable synthetic community of microbes

Microbiomes are extremely important for their host organisms, providing many vital functions and extending their hosts’ phenotypes. Natural studies of host-associated microbiomes can be difficult to interpret due to the high complexity of microbial communities, which hinders our ability to track and identify individual members along with the many factors that structure or perturb those communities. For this reason, researchers have turned to synthetic or constructed communities in which the identities of all members are known. However, due to the lack of tracking methods and the difficulty of creating a more diverse and identifiable community that can be distinguished through next-generation sequencing, most such in vivo studies have used only a few strains. To address this issue, we developed DISCo-microbe, a program for the design of an identifiable synthetic community of microbes for use in in vivo experimentation. The program is composed of two modules; (1) create, which allows the user to generate a highly diverse community list from an input DNA sequence alignment using a custom nucleotide distance algorithm, and (2) subsample, which subsamples the community list to either represent a number of grouping variables, including taxonomic proportions, or to reach a user-specified maximum number of community members. As an example, we demonstrate the generation of a synthetic microbial community that can be distinguished through amplicon sequencing. The synthetic microbial community in this example consisted of 2,122 members from a starting DNA sequence alignment of 10,000 16S rRNA sequences from the Ribosomal Database Project. We generated simulated Illumina sequencing data from the constructed community and demonstrate that DISCo-microbe is capable of designing diverse communities with members distinguishable by amplicon sequencing. Using the simulated data we were able to recover sequences from between 97–100% of community members using two different post-processing workflows. Furthermore, 97–99% of sequences were assigned to a community member with zero sequences being misidentified. We then subsampled the community list using taxonomic proportions to mimic a natural plant host–associated microbiome, ultimately yielding a diverse community of 784 members. DISCo-microbe can create a highly diverse community list of microbes that can be distinguished through 16S rRNA gene sequencing, and has the ability to subsample (i.e., design) the community for the desired number of members and taxonomic proportions. Although developed for bacteria, the program allows for any alignment input from any taxonomic group, making it broadly applicable. The software and data are freely available from GitHub and Python Package Index (PYPI).

16S rRNA↗
DOE OSTI2023

Non-Fermi Liquids from Kinetic Constraints in Tilted Optical Lattices

Here we study Fermi-Hubbard models with kinetically constrained dynamics that conserves both total particle number and total center of mass, a situation that arises when interacting fermions are placed in strongly tilted optical lattices. Through a combination of analytics and numerics, we show how the kinetic constraints stabilize an exotic non-Fermi liquid phase described by fermions coupled to a gapless bosonic field, which in several respects mimics a dynamical gauge field. This offers a novel route towards the study of non-Fermi liquid phases in the precision environments afforded by ultracold atom platforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as pro- teins, for growth optimization. Recent experiments have begun to reveal molecular com- ponents unpinning the partition; however, it remains unclear quantitatively how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation, and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that cellular ability of resource partition lies in an ultrasensitive, negative feedback control- ling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism which mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations, and is also efficient for biomass production over time. Furthermore, this work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bimetallics in a Nutshell: Complexes Supported by Chelating Naphthyridine-Based Ligands

Bimetallic motifs are a structural feature common to some of the most effective and synthetically useful catalysts known, including in the active sites of many metalloenzymes and on the surfaces of industrially relevant heterogeneous materials. However, the complexity of these systems often hampers detailed studies of their fundamental properties. To glean valuable mechanistic insight into how these catalysts function, this research group has prepared a family of dinucleating 1,8-naphthyridine ligands that bind two first-row transition metals in close proximity, originally designed to help mimic the proposed active site of metal oxide surfaces. Of the various bimetallic combinations examined, dicopper(I) is particularly versatile, as neutral bridging ligands adopt a variety of different binding modes depending on the configuration of frontier orbitals available to interact with the Cu centers. Organodicopper complexes are readily accessible, either through the traditional route of salt metathesis or via the activation of tetraarylborate anions through aryl group abstraction by a dicopper(I) unit. The resulting bridging aryl complexes engage in C-H bond activations, notably with terminal alkynes to afford bridging alkynyl species. In this work, the μ-hydrocarbyl complexes are surprisingly tolerant of water and elevated temperatures. This stability was leveraged to isolate a species that typically represents a fleeting intermediate in Cu-catalyzed azide-alkyne coupling (CuAAC); reaction of a bridging alkynyl complex with an organic azide afforded the first example of a well-defined, symmetrically bridged dicopper triazolide. This complex was shown to be an intermediate during CuAAC, providing support for a proposed bimetallic mechanism. These platforms are not limited to formally low oxidation states; chemical oxidation of the hydrocarbyl complexes cleanly results in formation of mixed valence Cu I Cu II complexes with varying degrees of distortion in both the bridging moiety and the dicopper core. Higher oxidation states, e.g. , dicopper(II), are easily accessed via oxidation of a dicopper(I) compound with air to give a Cu II 2 (μ-OH) 2 complex. Reduction of this compound with silanes resulted in the unexpected formation of pentametallic copper(I) dihydride clusters or trimetallic monohydride complexes, depending on the nature of the silane. Finally, development of an unsymmetrical naphthyridine ligand with mixed donor side-arms enables selective synthesis of an isostructural series of six heterobimetallic complexes, demonstrating the power of ligand design in the preparation of heterometallic assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Regio- and Enantioselective Bromocyclization of Difluoroalkenes as a Strategy to Access Tetrasubstituted Difluoromethylene-Containing Stereocenters

Difluoromethylene-containing compounds have attracted substantial research interest over the past decades for their ability to mimic biological functions of traditional functional groups while providing a wide variety of pharmacological benefits bestowed by the C–F bond. Here, we report a novel strategy to access RCF 2 Br-containing heterocycles by regio- and enantioselective bromocyclization of difluoroalkenes enabled by chiral anion phase-transfer catalysis. The utility of this methodology was highlighted through a synthesis of an analogue of efavirenz, a drug used for treating HIV. Additionally, the synthetic versatility of the CF 2 Br intermediates was showcased through functionalization to a variety of enantioenriched α,α-difluoromethylene-containing products.

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

A system identification approach for non-intrusive reduced order modeling of radiation-induced photocurrents

In this study, development of compact photocurrent models is currently dominated by analytical techniques that rely on physical assumptions to render the governing equations solvable in a closed form. Violation of these assumptions can reduce the accuracy of the models and/or limit their scope. In this paper we show that system identification of nonlinear state-space systems can serve as an alternative numerical basis for non-intrusive reduced order modeling of photocurrent effects. To that end we develop a compact gray box photocurrent model (GBPM) by using a state-space representation with a low-dimensional latent state equation that mimics a mathematical model for the response of an idealized class of devices to ionizing radiation. In so doing we obtain a model that learns the dynamics of a quantity of interest directly from its measurements without requiring snapshots of the internal device state or its discretized model, and can be inferred from very small data sets. To demonstrate the approach we train the GBPM using a small experimental data set for a Z5236 Zener diode and a small synthetic data set obtained by simulating a synthetic pn-junction device. We then compare the GBPMs with black box models trained on the same data and show that performance of the latter is limited by the size of the data set, while the former are able to achieve excellent performance in both the reproductive and the predictive regimes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synthetic Microbial Consortium for Biological Breakdown and Conversion of Lignin

The plant polymer lignin is the most abundant renewable source of aromatics on the planet and conversion of it to valuable fuels and chemicals is critical to the economic viability of a lignocellulosic biofuels industry and to meeting the DOE’s 2022 goal of $\$2.50$/gallon mean biofuel selling price. Presently, there is no efficient way of converting lignin into valuable commodities. Current biological approaches require mixtures of expensive ligninolytic enzymes and engineered microbes. This project was aimed at circumventing these problems by discovering commensal relationships among fungi and bacteria involved in biological lignin utilization and using this knowledge to engineer microbial communities capable of converting lignin into renewable fuels and chemicals. Essentially, we aimed to learn from, mimic and improve on nature. We discovered fungi that synergistically work together to degrade lignin, engineered fungal systems to increase expression of the required enzymes and engineered organisms to produce products such as biodegradable plastics precursors.

09 BIOMASS FUELS↗
DOE OSTI2023

Sonochemical Synthesis and Ion Transport Properties of Surfactant-Stabilized Carbon Nanotube Porins

Carbon nanotube porins (CNTPs), short segments of carbon nanotubes stabilized by a lipid coating, are a promising example of artificial membrane channels that mimic a number of key behaviors of biological ion channels. While the lipid-assisted synthesis of CNTPs may facilitate their subsequent incorporation into lipid bilayers, it limits the applicability of these pores in other self-assembled membrane materials and also precludes the use of large scale purified CNT feedstocks. In this report we demonstrate that CNTPs can be synthesized by sonochemical cutting of long CNT feedstocks in the presence of different surfactants, producing CNTS with transport properties identical to those obtained by the lipid-assisted procedure. Our results open up a large variety of synthetic routes for CNTP production.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Analysis of the Source Physics Experiment Dry Alluvium Geology Ball Drop Sources

Two balls were dropped approximately 150 m down an open borehole as part of an investigation into the aftershocks from a 51 ton TNT-equivalent chemical explosion. The ball drop sources, a concrete-filled basketball and a bowling ball, were intended to mimic falling debris within an underground explosion borehole and cavity. Falling debris or sloughing of the borehole and cavity walls is thought to be one of the sources of explosion aftershocks. The signals from the ball drop sources were recorded by the same seismic network that monitored the aftershocks of the chemical explosion, so the ball drop recordings were used as templates to search through the continuous stream of aftershocks and detect correlated signals as a proportion of all aftershocks. The search resulted in only four similar event recordings out of the thousands of detected aftershocks. A synthetic test showed that the small, high-frequency ball drop templates were limited to producing highly correlated detections for events occurring in a small region around the impact location at 150 m depth. The high-frequency templates were necessary since the small ball drop signals were only above the noise at frequencies greater than 10 Hz. Unfortunately, the impact location is where the borehole was filled with sand in preparation for the subsequent chemical explosion in the test series, making it 150 m above the explosion created cavity centered at 300 m depth. This puts the ball drop templates 150 m away from the cavity, where many more events from falling debris would be expected. Further synthetic tests confirmed events emanating from the cavity 150 m below the impact site would have correlated with the lower frequency template and been detected had the template been usable below 10 Hz. Therefore, we conclude that the four detected events were most probably falling debris within the open borehole that impacted the floor at 150 m depth just as the ball drop sources had, and that this type of explosion aftershock mechanism comprises less than 1% of all explosion aftershocks.

58 GEOSCIENCES↗