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At least 703 records · Page 39

A pyrolysis study of allylic hydrocarbon fuels

The pyrolysis of selected C 3 –C 5 allylic hydrocarbons has been studied using a single-pulse shock tube. A new single-pulse shock tube has been designed and constructed by recommissioning an existing conventional shock tube. This facility enables the investigation of high-temperature chemical kinetics with an emphasis on combustion chemistry. The modifications performed on the existing shock tube are described, and the details of the sampling system to analyze the species concentration using a gas chromatography-mass spectrometry-flame ionization detection (GC-MS with a flame ionization detector) system are also provided. This facility is characterized and validated by performing cyclohexene pyrolysis experiments. Furthermore, the performance of the shock tube is demonstrated by reproducing previous literature measurements on the pyrolysis of isobutene. Postvalidation, this setup is used to study the pyrolysis of trans-2-butene and 2-methyl-2-butene (2M2B). A newly developed mechanism, NUIGMech1.0, is used to simulate the experimental data of propene, isobutene, 2-butene, and 2M2B, allylic hydrocarbon fuels. A description using two different kinetic simulation approaches is provided using our isobutene experiments as a reference. We found no significant differences between the two methods. Additionally, the contribution of different reaction classes on fuel consumption is detailed and the influence of geometry on fuel consumption and first aromatic ring: benzene is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Resolving femtosecond photoinduced energy flow: capture of nonadiabatic reaction pathway topography and wavepacket dynamics from photoexcitation through the conical intersection seam (Final Technical Report)

The dynamics that take place within just tens to hundreds of femtoseconds following the absorption of light by a molecule can play a critical role in how the absorbed energy is directed, allowing it to be used for a specific function or dissipated harmlessly. The form of chemical change that occurs rapidly in these molecules is called a “nonadiabatic electronic transition.” Such transitions are known to mediate energy flow in natural biological systems such as the ultraviolet photoprotection mechanism of DNA and the first step of the human vision response. Understanding how these mechanisms work precisely may help scientists achieve controlled manipulation of solar energy or optical control of a wide range of energy management functions in artificial systems. Experimental methods, however, have not yet allowed a precisely resolved and complete measurement of nonadiabatic electronic transitions. This constitutes a major obstacle to progress in the field. For progress to occur that would inform a wide body of research aiming to efficiently harness the energy of light for practical purposes, it is especially important to benchmark computational models of the molecules undergoing these rapid changes with experimental measurements, in order to learn which models are accurate. With Dept. of Energy funding, we have made strong progress towards establishing a new optical method for experimentally detecting the full nonadiabatic electronic transition. This requires having coordinated pulses of light covering the visible through the mid-infrared range of the electromagnetic spectrum that last only ten femtoseconds. We have developed a new, relatively simple approach for generating such pulses of laser light, and have incorporated them into a time-resolved spectrometer for measuring rapid changes in molecules. These tools can provide the greater precision and new types of data that are needed to benchmark computational models of molecular change and thus to make progress in the field. Our tools were tested on graphene, an excellent solid-state sample for verifying the capabilities and limitations of our instrumentation. The investment made in these tools by the Dept. of Energy Office of Science will allow new fundamental scientific understanding of energy dynamics in molecules in future studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predicting Global Ground Geoelectric Field With Coupled Geospace and Three-Dimensional Geomagnetic Induction Models

We forecast the global effects of space weather on the geoelectric and geomagnetic fields using a novel combination of methods. We use a realistic three-dimensional (3-D) model of Earth's electrical conductivity and a realistic representation of magnetospheric and ionospheric current systems. Our scheme involves the following steps: (1) We run a global magnetohydrodynamic model of the magnetosphere coupled to an electrostatic model of the ionosphere. (2) We calculate a global time series of the ground magnetic field resulting from the ionospheric, field-aligned, and magnetospheric currents of the global magnetohydrodynamic model. (3) We approximate this external field by an equivalent source current flowing in a thin shell above Earth. (4) We calculate a global time series of geoelectric and geomagnetic fields from the equivalent current and a 3-D conductivity model of Earth that also takes into account the coast effect due to large horizontal conductivity gradient. We verify our implementation by comparing the results against known analytic and numeric solutions, and then apply our scheme to the geomagnetic storm of 14 and 15 December 2006. In particular, we show that accounting for 3-D structure of Earth's conductivity results in significantly enhanced geoelectric field at large lateral gradients of conductivity, especially in coastal regions, both at middle and high latitudes. In the studied geomagnetic storm the largest values of 3-D geoelectric field are detected at high latitudes reaching 2.5 volts per kilometer and the 3-D effect extends inland by a few hundred kilometers.

Honkonen, I.↗

An objective inter-comparison of trash mark constellations generated by manual and automated detection methods

Trash marks are unintentional markings observed on printed, scanned, or photocopied documents that result from permanent defects or transient material in office machines and can be used for source attribution of questioned documents. Trash mark examinations have been in use in forensic laboratories for decades, yet the method remains relatively untested and relies on training, experience, and anecdotal information to support its validity. Herein this study generated and harnessed objective data to empirically test one of the foundational theories for assessing the origin of photocopied documents: provided trash marks are present in sufficient quantity and/or quality, no two machines will exhibit a constellation of trash marks that is indistinguishable from another. In this project, objective trash mark location and size data was generated for 50 known photocopiers using both a traditional and a novel, automated method. Inter-machine comparisons were conducted using a novel variant of the Hausdorff distance algorithm to generate a quantitative assessment of how similar or different the 2450 pairs of trash mark constellations were from one another. This study found that each of the machines bearing one or more trash marks exhibited objective differences in their trash mark constellations, ultimately providing support for the tested hypothesis

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Model-Based Method for Sensor Validation

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Vatan, Farrokh↗

Predictable Patterns in Planetary Transit Timing Variations and Transit Duration Variations Due to Exomoons

We present new ways to identify single and multiple moons around extrasolar planets using planetary transit timing variations (TTVs) and transit duration variations (TDVs). For planets with one moon, measurements from successive transits exhibit a hitherto undescribed pattern in the TTV-TDV diagram, originating from the stroboscopic sampling of the planet's orbit around the planet-moon barycenter. This pattern is fully determined and analytically predictable after three consecutive transits. The more measurements become available, the more the TTV-TDV diagram approaches an ellipse. For planets with multiple moons in orbital mean motion resonance (MMR), like the Galilean moon system, the pattern is much more complex and addressed numerically in this report. Exomoons in MMR can also form closed, predictable TTV-TDV figures, as long as the drift of the moons' pericenters is suciently slow.We find that MMR exomoons produce loops in the TTV-TDV diagram and that the number of these loops is equal to the order of the MMR, or the largest integer in the MMR ratio.We use a Bayesian model and Monte Carlo simulations to test the discoverability of exomoons using TTV-TDV diagrams with current and near-future technology. In a blind test, two of us (BP, DA) successfully retrieved a large moon from simulated TTV-TDV by co-authors MH and RH, which resembled data from a known Kepler planet candidate. Single exomoons with a 10 percent moon-to-planet mass ratio, like to Pluto-Charon binary, can be detectable in the archival data of the Kepler primary mission. Multi-exomoon systems, however, require either larger telescopes or brighter target stars. Complementary detection methods invoking a moon's own photometric transit or its orbital sampling effect can be used for validation or falsification. A combination of TESS, CHEOPS, and PLATO data would offer a compelling opportunity for an exomoon discovery around a bright star.

stroboscopic sampling↗

Development of carbon nanosensor for vinpocetine at zinc oxide modified carbon matrix with anionic surfactant for clinical application

A nano-level sensing method was formulated for the trace-level detection and determination of vinpocetine (VIN) at a zinc oxide nanomaterial-based carbon matrix with immobilised anionic surfactant sodium dodecyl sulfate (ZnO-SDS/CPE) using cyclic voltammetry (CV) and square wave voltammetry (SWV) approaches. The catalytic properties of ZnO impact the electron rate of VIN activity, resulting in a threefold increase in the VIN peak response with the ZnO-SDS-modified sensor. Here, the effects of several factors, including scan rate, pH, accumulation length, modifier amount, and concentration, were investigated on the VIN peak current. The electro-oxidation of VIN by pH study involves one proton and one electron. The CV method also investigated the effect of scan rate. The charge transfer coefficient (α) is obtained to be 0.58, and the heterogeneous rate constant (k°) is estimated to be 4.46 s⁻¹. The concentration effect of VIN was studied using the SWV method. Specifically, the SWV methodology achieved the lowest detection limit compared to previously published approaches, with estimated values of the Limit of Detection (LOD) and Limit of Quantification (LOQ) being 2.2 × 10⁻⁸ M and 7.7 × 10⁻⁸ M, respectively. The trace level of VIN in tablet and urine samples was determined using the modified sensor. Moreover, the sensor demonstrates particular reproducibility and long-term stability, making it suitable for real-time applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detection of Foreign Materials on Broiler Breast Meat Using a Fusion of Visible Near-Infrared and Short-Wave Infrared Hyperspectral Imaging

Foreign material (FM) found on a poultry product lowers the quality and safety of the product. We developed a fusion method combining two hyperspectral imaging (HSI) modalities in the visible-near infrared (VNIR) range of 400–1000 nm and the short-wave infrared (SWIR) range of 1000–2500 nm for the detection of FMs on the surface of fresh raw broiler breast fillets. Thirty different types of FMs that could be commonly found in poultry processing plants were used as samples and prepared in two different sizes (5 × 5 mm 2 and 2 × 2 mm 2 ). The accuracies of the developed Fusion model for detecting 2 × 2 mm 2 pieces of polymer, wood, and metal were 95%, 95%, and 81%, respectively, while the detection accuracies of the Fusion model for detecting 5 × 5 mm 2 pieces of polymer, wood, and metal were all 100%. The performance of the Fusion model was higher than the VNIR- and SWIR-based detection models by 18% and 5%, respectively, when F1 scores were compared, and by 38% and 5%, when average detection rates were compared. The study results suggested that the fusion of two HSI modalities could detect FMs more effectively than a single HSI modality.

36 MATERIALS SCIENCE↗

Thermal Effects on Far-Field Distributed Acoustic Strain-Rate Sensors

Summary Fiber-optic cables cemented outside of the casing of an unconventional well measure crosswell strain changes during fracturing of neighboring wells with low-frequency distributed acoustic sensing (LF-DAS). As a hydraulic fracture intersects an observation well instrumented with fiber-optic cables, the fracture fluid injected at ambient temperatures can cool a section of the sensing fiber. Often, LF-DAS and distributed temperature sensing (DTS) cables are run in tandem, enabling the detection of such cooling events. The increasing use of LF-DAS for characterizing unconventional hydraulic fracture completions demands an investigation of the effects of temperature on the measured strain response by LF-DAS. Researchers have demonstrated that LF-DAS can be used to extract the temporal derivative of temperature for use as a differential-temperature-gradient sensor. However, differential-temperature-gradient sensing is predicated on the ability to filter strain components out of the optical signal. In this work, beginning with an equation for optical phase shift of LF-DAS signals, a model relating strain, temperature, and optical phase shift is explicitly developed. The formula provides insights into the relative strength of strain and temperature effects on the phase shift. The uncertainty in the strain-rate measurements due to thermal effects is estimated. The relationship can also be used to quantify uncertainties in differential-temperature-gradient sensors due to strain perturbations. Additionally, a workflow is presented to simulate the LF-DAS response accounting for both strain and temperature effects. Hydraulic fracture geometries are generated with a 3D fracture simulator for a multistage unconventional completion. The fracture width distributions are imported by a displacement discontinuity method (DDM) program to compute the strain rates along an observation well. An analytic model is used to approximate the temperature in the fracture. Using the derived formulae for optical phase shift, the model outputs are then used to compute the LF-DAS response at a fiber-optic cable, enabling the generation of waterfall plots including both strain and thermal effects. The model results suggest that before, during, and immediately following a fracture intersecting a well instrumented with fiber, the strain on the fiber drives the LF-DAS signal. However, at later times, as completion fluid cools the observation well, the temperature component of the LF-DAS signal can be equal to or exceed the strain component. The modeled results are compared to a published field case in an attempt to enhance the interpretation of LF-DAS waterfall plots. Finally, we propose a sensing configuration to identify the events when “wet fractures” (fractures with fluids) intersect the observation well.

Engineering↗

The pH-dependent orientation of N3 dye on a gold substrate is revealed using heterodyne-detected vibrational sum frequency generation spectroscopy

We report on systematic changes to the adsorption geometry of the dye N3 {[cis-bis(isothiocyanato)bis(2,2' -bipyridyl-4,4' -dicarboxylato ruthenium(II)]} on a gold substrate as the pH of the deposition environment is altered. The protonation states of the four --COOH groups of the N3 dye change according to the modified pH conditions, thus affecting the number of --COOH and --NCS functional groups that participate in the adsorption to gold. Here, we use heterodyne detected vibrational sum frequency generation (HD-VSFG) spectroscopy to obtain surface specific vibrational information on both --COOH and --NCS groups as a function of pH of the deposition conditions. Polarization-dependent HD-VSFG yields sets of complex χ (2) spectra, enabling us to perform a simultaneous fitting procedure to the polarization-dependent real and imaginary components and thus extract detailed structural information of the N3/gold interface. Overall, our results show that N3 preferentially adsorbs to gold either with two --COOH groups and one --NCS group in more acidic conditions or with one --COOH group and two --NCS groups in more basic conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Probing the Electronic Structure of [B 10 H 10 ] 2– Dianion Encapsulated by an Octamethylcalix[4]pyrrole Molecule

Despite being an important closo-borate in the condensed phase boron chemistry, isolated B 10 H 10 2- is electronically unstable and has never been detected in the gas phase. Herein, we report a successful capture of this fleeting species through binding with an octamethylcalix[4]pyrrole (omC4P) molecule to form a stable gaseous omC4P·[B 10 H 10 ] 2- complex and its characterizations utilizing negative ion photoelectron spectroscopy (NIPES). The recorded NIPE spectrum, contributed from both omC4P and [B 10 H 10 ] 2- , is deconvoluted by subtracting the omC4P contribution to yield a [B 10 H 10 ] 2- spectrum. The obtained [B 10 H 10 ] 2- spectrum consists of four major bands spanning electron binding energy (EBE) range from 1 to 5 eV with the EBE gaps matching excellently with the energy intervals of computed highly lying occupied molecular orbitals of the B 10 H 10 2- dianion. Finally, this study showcases a generic method to utilize omC4P to capture unstable multiply charged anions in the gas phase for experimental determination of their electronic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations

Abstract Background Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro - and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity ( π and θ ), and selective pressures (pN/pS and Tajima’s D ) within and fixation indices ( F ST ) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity ( π ) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences. Conclusions These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro - and microdiversity across microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗

Deciphering Reaction Mechanisms of Molecular Proton Reduction Catalysts with Cyclic Voltammetry: Kinetic vs Thermodynamic Control

The kinetics and thermodynamics of elementary reaction steps involved in the catalytic reduction of protons to hydrogen define the reaction landscape for catalysis. The mechanisms can differ in the order of the elementary proton transfer, electron transfer, and bond-forming steps and can be further differentiated by the sites at which protons and electrons localize. Access to fully elucidated mechanistic, kinetic, and thermochemical details of molecular catalysts is crucial to facilitate the development of new catalysts that operate with optimal efficiency, selectivity, and durability. The mechanism by which a catalyst operates, as well as the kinetics and thermodynamics associated with the individual steps, can often be accessed through electroanalytical studies. Here, this Account details the application of cyclic voltammetry to interrogate reaction mechanisms and quantify the kinetics and thermodynamics of elementary reaction steps for a series of molecular catalysts that mediate electrochemical proton reduction. I distinguish the limiting scenarios wherein a catalyst operates under kinetic control vs thermodynamic control, with a focus on detecting how cyclic voltammetry features shift with proton source strength and concentration, as well as scan rate. For systems that operate under kinetic control, catalytic currents are observed at, or slightly positive toward, the formal potential for the redox process that triggers catalysis. Under thermodynamic control, catalytic responses shift as a function of the proton source pKa and effective pH of the solution. After drawing this distinction, we introduce the appropriate voltammetry experiments and accompanying analytical expressions for extracting key metrics from the data. To illustrate analytical strategies to quantify elementary reaction steps of catalysts operating under kinetic control, I describe our studies of proton reduction catalysts Co(dmgBF 2 ) 2 (CH 3 CN) 2 (dmgBF 2 = difluoroboryl-dimethylglyoxime) and [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ (P 2 Ph N 2 Ph = 1,5-phenyl-3,7-phenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, peak shift analysis, foot-of-the-wave analysis, and plateau current analysis are applied to data sets wherein voltammetric response are recorded as a function of catalyst concentration, proton source concentration, proton source strength, and scan rate to quantify rate constants for elementary proton transfer and bond-forming steps in a catalytic cycle. Further, the case study of [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ illustrates how complementary spectroscopic methods can bolster the mechanistic assignment. Collectively, these two studies showcase how detailed mechanistic studies inform on rate-limiting elementary steps in catalysis and other key processes underpinning catalysis. Second, I present analytical strategies to interrogate catalysts operating under thermodynamic control, centered on the case study of [Ni II (P 2 Ph N 2 Bn ) 2 ] 2+ (P 2 Ph N 2 Bn = 1,5-dibenzyl-3,7-diphenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, the application of nonaqueous Pourbaix theory to extract thermodynamic information is introduced, and the construction of a coupled Pourbaix diagram is detailed. This study identifies ligand-based protonation as the key process that places catalysis under thermodynamic control and influences the reaction mechanism. Together, the work detailed in this Account showcases the utility of electroanalytical methods to disentangle complex reaction mechanisms and extract key thermochemical and kinetic parameters for elementary steps of catalysis. Through detailed presentation of the key analytical expressions that underpin these analyses, this Account seeks to facilitate the adoption of cyclic voltammetry by the community to fully extract kinetic, thermochemical, and mechanistic information on electrochemical small-molecule activation.

catalysts↗

Abstract for CRADA between NETL and GlycoSurf, Inc.

The National Energy Technology Laboratory (NETL) and GlycoSurf, Inc. (Participant) will collaborate in the development of novel luminescent sensing materials for rare earth elements using chemically modified surfactants. Rare earth elements are economically critical metals that are used in many technologies relevant to both energy and national defense. Slow and expensive characterization methods for rare earth element analysis are a major pain point for domestic production of these metals; the development of low-cost optical sensing materials and platforms can significantly reduce the time and financial costs associated with rare earth element prospecting and process monitoring. NETL has extensive experience developing inexpensive and compact optical sensors for critical metals such as rare earths. GlycoSurf, Inc. has commercialized high performance surfactants for the selective extraction of rare earth elements in complex environments such as acid mine drainage. By modifying these surfactants with fluorescent functional groups, trace concentrations of rare earths may be detected through a process called “photosensitization,” where the sensing material induces element-specific emission bands that enable different rare earth elements to be detected and distinguished. This project will enable the development of sensing materials capable of selectively detecting trace quantities of valuable rare earths in challenging conditions, including high ionic strength, highly acidic matrices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multifrequency Models of Black Hole Photon Rings from Low-luminosity Accretion Disks

Images of black holes encode both astrophysical and gravitational properties. Detecting highly lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced from synchrotron emission from 10 to 670 GHz. As an application, we systematically study several disk models and compute their total flux density, average radii, and optical depth. The model parameters are chosen around fiducial values calibrated to general relativistic magnetohydrodynamic (GRMHD) simulations and observations of M87*. For the parameter space studied, we characterize the transition between optically thin and thick regimes and the frequency at which the first photon ring is observable. Our results highlight the need for careful definitions of photon ring radius in the image domain, as in certain models the highly lensed photon ring is dimmer than the direct emission at certain angles. We find that at low frequencies the ring radii are set by the electron temperature, while at higher frequencies the magnetic field strength plays a more significant role, demonstrating how multifrequency analysis can also be used to infer plasma parameters. Lastly, we show how our implementation can qualitatively reproduce multifrequency black hole images from GRMHD simulations when adding time variability to our disk model through Gaussian random fields. This approach provides a new method for simulating observations from the Event Horizon Telescope and the proposed Black Hole Explorer space mission.

79 ASTRONOMY AND ASTROPHYSICS↗

Manipulating cobalt oxide on N-doped aligned electrospun carbon nanofibers towards instant electrochemical detection of dopamine secreted by living cells

A demand exists for the development of miniaturized biosensors with high sensitivity and a wide dynamic range for rapid detecting dopamine (DA) levels. Here, an electrochemical sensor is created by decorating closely packed cobalt oxide (Co 3 O 4 ) nanograins on nitrogen-doped electrospun carbon nanofibers (NECNFs) via a facile electrodeposition method. The carbon nanofiber matrix provides both a scaffold with ultrahigh surface area for homogeneous dispersion of cobalt oxide nanostructures and facilitates the electrochemical conductivity for the hybrid electrode. The sensing principle is based on the redox reaction between DA and DA quinone that generates electrical signal, which is detectable via differential pulse voltammetry (DPV) and chronoamperometry (CA). Owing to a high electroactive surface area and facilitating electron transfer between electrode surface and the target molecule, the electrical sensing platform exhibits superior sensitivity and selectivity toward DA, with an excellent limit of detection (9 nM) over a wide range of concentrations (0.01 to 100 μM). Moreover, based on its excellent sensing properties and biocompatibility, the sensor has been used for the real-time monitoring exogeneous DA released from pheochromocytoma (PC12) cells. This work demonstrates promise of a novel sensor technology that may render rapid point-of-care testing of DA towards early clinical diagnosis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗