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At least 343 records · Page 19

Stereolithographic Fabrication of a Flow Cell For Improved Neurochemical Sensor Testing

The human brain remains a mystery to researchers. Scientists at Lawrence Livermore National Laboratory (LLNL) have developed highly sensitive and flexible multimodal multielectrode arrays (MEAs) with the potential to be both powerful diagnostic and therapeutic tools when dealing with neurological ailments such as Alzheimers, Parkinson’s, Post Traumatic Stress Disorder, and Traumatic Brain Injuries. These MEAs include biosensors in addition to electrodes for electrical stimulation and recording. The transition from the traditional rigid neural probes to the flexible MEAs has led LLNL researchers on the search for alternative testing and characterization methods for flexible devices in vitro. The rigid devices have been shown to be more abrasive and damaging to the brain when implanted, but due to their rigidity – are more robust and resilient to noise artifacts in in vitro testing. The flexible MEAs, similar in size and flexibility to a human hair, cause much less damage in the brain but have had a difficult time maintaining their integrity and collecting data without high-levels of noise due to the formation of inertial eddies (due to increased turbulent flow) moving the flexible MEA during in vitro testing. The goal of this project was to design a fluidic cell to allow for a more constant electrochemical testing & characterization of biosensors on these flexible MEAs. Design and fabrication of the fluidic cell was through the use of CAD software and a stereolithography printer using clear photopolymer resin (appropriate for biomedical use and quality resolution). Flow cells facilitate analyte delivery to biosensor surfaces in a more constant and controlled fashion, allowing for –in this case –a better electrodeposition of thin-film Platinum-Iridium (Pt-Ir) on MEAs of 16 Platinum disc electrodes 50µm in diameter. Platinum-Iridium alloy electrodes have been widely used in biomedical and industrial applications. Iridium adds mechanical stiffness to platinum. PtIr is a FDA approved electrode material for human implantable devices.

36 MATERIALS SCIENCE↗

Impedance-Based Detection of NO 2 Using Ni-MOF-74: Influence of Competitive Gas Adsorption

Chemically robust, low-power sensors are needed for the direct electrical detection of toxic gases. Metal–organic frameworks (MOFs) offer exceptional chemical and structural tunability to meet this challenge, though further understanding is needed regarding how coadsorbed gases influence or interfere with the electrical response. To probe the influence of competitive gases on trace NO 2 detection in a simulated flue gas stream, a combined structure–property study integrating synchrotron powder diffraction and pair distribution function analyses was undertaken, to elucidate how structural changes associated with gas binding inside Ni-MOF-74 pores correlate with the electrical response from Ni-MOF-74-based sensors. Data were evaluated for 16 gas combinations of N 2 , NO 2 , SO 2 , CO 2 , and H 2 O at 50 °C. Fourier difference maps from a rigid-body Rietveld analysis showed that additional electron density localized around the Ni-MOF-74 lattice correlated with large decreases in Ni-MOF-74 film resistance of up to a factor of 6 × 10 3 , observed only when NO 2 was present. These changes in resistance were significantly amplified by the presence of competing gases, except for CO 2 . Without NO 2 , H 2 O rapidly (<120 s) produced small (1–3×) decreases in resistance, though this effect could be differentiated from the slower adsorption of NO 2 by the evaluation of the MOF’s capacitance. Furthermore, samples exposed to H 2 O displayed a significant shift in lattice parameters toward a larger lattice and more diffuse charge density in the MOF pore. Evaluating the Ni-MOF-74 impedance in real time, NO 2 adsorption was associated with two electrically distinct processes, the faster of which was inhibited by competitive adsorption of CO 2 . Together, this work points to the unique interaction of NO 2 and other specific gases (e.g., H 2 O, SO 2 ) with the MOF’s surface, leading to orders of magnitude decrease in MOF resistance and enhanced NO2 detection. Finally, understanding and leveraging these coadsorbed gases will further improve the gas detection properties of MOF materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optical Waveguide Lightmode Spectroscopy (OWLS) as a Sensor for Thin Film and Quantum Dot Corrosion

Optical waveguide lightmode spectroscopy (OWLS) is usually applied as a biosensor system to the sorption-desorption of proteins to waveguide surfaces. Here, we show that OWLS can be used to monitor the quality of oxide thin film materials and of coatings of pulsed laser deposition synthesized CdSe quantum dots (QDs) intended for solar energy applications. In addition to changes in data treatment and experimental procedure, oxide- or QD-coated waveguide sensors must be synthesized. We synthesized zinc stannate (Zn2SnO4) coated (Si,Ti)O2 waveguide sensors, and used OWLS to monitor the relative mass of the film over time. Films lost mass over time, though at different rates due to variation in fluid flow and its physical effect on removal of film material. The Pulsed Laser Deposition (PLD) technique was used to deposit CdSe QD coatings on waveguides. Sensors exposed to pH 2 solution lost mass over time in an expected, roughly exponential manner. Sensors at pH 10, in contrast, were stable over time. Results were confirmed with atomic force microscopy imaging. Limiting factors in the use of OWLS in this manner include limitations on the annealing temperature that maybe used to synthesize the oxide film, and limitations on the thickness of the film to be studied. Nevertheless, the technique overcomes a number of difficulties in monitoring the quality of thin films in-situ in liquid environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Laboratory-Scale Coal-Derived Graphene Process (Final Report)

The Energy & Environmental Research Center (EERC) conducted a laboratory-scale coal-derived graphene (CDG) project focused on developing a technological process for making graphene from four U.S. domestic coal or coal wastes, including lignite from North Dakota, subbituminous coal from Wyoming, bituminous coal from Utah, and anthracite from Pennsylvania. The project was divided into two performance or budget periods (BPs), with BP1 comprising the up-front laboratory experiments to make graphene materials from coal beginning on May 1, 2020, to April 30, 2022. BP2 was conducted from May 1, 2022, to April 30, 2023, and was focused on analyzing the CDG process economic feasibility and the technical gaps for technological scale-up and commercialization. During this project, a few different coal-derived high-value products have been demonstrated, including graphite, graphene oxide (GO), reduced graphene oxide (rGO), and graphene quantum dots (GQDs). A new graphite microstructure was discovered and named “croissant graphite” because of the exterior morphological and textural resemblance to croissant food items sold in commercial groceries stores. The new graphite structure and the associated preparation from coal or coal waste feedstocks has been the subject of a U.S. patent application. The systematic experimental processes involving coal cleaning, upgrading, and conversion to high-value carbon products culminated into a developed upgraded coal-to-products (UCP) technology that is being pursued for potential fast-track commercialization, if funding is available. It is envisioned that commercialization of the UCP technology would increase consumption of U.S. domestic coals or coal wastes to make environmentally sustainable high-value products for the electronics industry, high-energy-storage applications, and clean energy technologies such as electric vehicle (EV) lithium-ion batteries (LIBs), for which graphite has become a critical mineral commodity. Croissant graphite microstructures, when observed by field emission scanning electron microscopy (FESEM), display wavy surface morphology and often grow from a base that is made of graphitized particles with honeycomb-like layers, which are believed to be graphene layers. While more studies are needed to fully ascertain the mechanisms of the croissant graphite microstructure formation, it is postulated that their growth may begin from curling of the graphene sheets into ribbon-like structures, and continuous growth and densification of the ribbon-like structures forms croissant microstructures. Additional studies are ongoing to evaluate the electrochemical performance of croissant graphite for LIB applications and to determine the experimental conditions necessary to tune on/off croissant formation so that it can be either optimized or suppressed depending on performance evaluation results. In addition to the discovery of croissant graphite, the graphitization process from the four coal ranks in general was successful. X-ray diffraction (XRD) analysis showed that the degree of graphitization (DoG) ranged from 12% to 80% in an early sample set, and further optimization on lignite coal produces a DoG of about 92%, which was spectacular to see as lignite is the lowest-rank coal. Thus, it is expected that the graphitization performance for higher-rank coals will be similar or better when optimized as well. The coal-derived graphite was used to make GO and rGO. Analytical characterization, e.g., by methods such as Raman spectroscopy, XRD, Fourier transform infrared (FTIR) spectroscopy and FESEM, showed that the sequence of converting the coal to graphite, exfoliating it to GO, and then chemically reducing the GO to rGO was successful. Although coal naturally contains aromatic compounds and some relatively small-sized condensed aromatic units, it does not contain graphene sheets. In the UCP process, the aromatic domains in the coals, particularly low-rank coals, are concentrated and condensed further into graphene sheets, which are ordered into a 3D stack during graphitization. The synthesized graphite is then unpacked by methods such as exfoliation to various graphene products. GQDs were synthesized from all four coal types, and their optical properties were demonstrated to be tunable by the coal precursor preprocessing treatments. In all four coal types, enhanced optical properties were observed for the produced GDQs with incremental improvements made to the coal precursors. GQDs produced from raw coal samples displayed lower ultraviolet–visible (UV–Vis) spectroscopy absorbance intensity compared to those obtained from cleaned and upgraded coal residues. The photoluminescence (PL) intensities also varied with pretreatment conditions and with the concentration of GQDs in aqueous solutions. GQDs obtained from anthracite show longer emission wavelengths and can be excited by visible light as opposed to GQDs derived from the other coal ranks. UV fluorescence 3D maps and spectra revealed that the emission wavelength at which the GQDs solutions display the highest intensity was slightly redshifted based on the coal precursor pretreatments. In low-rank coal (lignite and subbituminous) samples, two clusters were observed in the maps for GQDs, which may suggest that there are potentially two types of fluorophores in solution or two main size populations. The ability to tune the properties of GQDs based on processing methods can be exploited to make GQDs for various optical display or optoelectronics applications. The results also highlight the importance of removing coal-borne impurities to improve the quality of the coal precursor for preparation of graphene products. Coal and/or coal wastes preprocessing methods were developed and applied to clean and upgrade the coal precursors prior to graphitization and subsequent conversion to graphene products. The preprocessing methods involve high specific-gravity separations, mineral acid cleaning (no hydrofluoric acid), and subsequent upgrading by reducing the coal-borne heteroatom (nitrogen, sulfur, and oxygen) content using proprietary chemical agents. Analytical characterization revealed that the preprocessing steps were successful, with ash reductions that range from 38% to 80% and residual ash content that was below the 5 wt% initial target. Based on proximate and ultimate analysis, the heteroatom reduction reactions produced upgraded coal residues with the oxygen content reduced by 8% to 24%, with additional reductions in the nitrogen and sulfur contents. An initial assessment of the waste streams from the UCP process shows very small to negligible environmental impact due to CO 2 , NO x , and SO x because most process steps are performed under inert atmosphere with argon. Consequently, reactive oxygen environments that tend to create these species are avoided. The inorganic and potentially hazardous species are released into aqueous waste streams that are easy to handle for proper disposal. The liquid waste streams were found to contain low-level concentrations of rare-earth elements (REEs), which could be concentrated and recovered as value-added by-products. Additionally, the volatile and gaseous fractions from carbonization and heat treatment contain useful organic compounds that can also be recovered as potential valuable by-products. Thus, the UCP technology is considered an environmentally sustainable and promising emerging technology for making high-value products from coal and coal wastes, with potential additional value-added by-products. Analysis of potential markets for the coal-derived carbon products shows a strong demand in both niche market sectors and across a wide variety of other industrial sectors. Graphite is currently considered a critical mineral commodity that has a large and growing demand in the LIB industry for EV applications. Based on data from Fortune Business Insights (2022) and Marketwatch (2023) reports, the average global graphite market is projected to reach about 33 billion by 2028, growing at a compound annual growth rate (CAGR) of about 7%, with much of this growth expected to be in the LIB industry. GO and rGO have strong market potentials in various application areas, such as coatings for anticorrosion, anti-icing, and antimicrobial protection, thermal barriers, wear resistance, sensors, additive manufacturing such as 3D inks, and others. GQDs are the emerging key player in the bioimaging, photovoltaics, and light-emitting diodes (LEDs) applications, with the potential to replace traditional semiconductor quantum dots (SQDs), which are based on metallic systems that are more toxic and more expensive. Biomedical applications of GQDs are becoming more attractive because of low to no toxicity and extremely low cost compared to SQDs. The major challenges for scale-up and commercialization of coal-derived carbon products such as graphene vary from the inherent attributes of graphene itself to reluctance to accept graphene in new manufacturing processes because of the uncertainty of the unknown. The 2D nature of graphene materials with a thickness of one atom presents significant challenges to proper handling/processing, and process scale-up becomes difficult because it requires high-end, expensive equipment, even for routine handling and analysis for quality assurance and control. Pristine graphene can also be extremely difficult to work into other matrices, thus hindering downstream processibility, especially at large scale. Currently, the cost of graphene and graphene products is still high and presents an economic risk that tends to slow down investment in scaling up emerging technologies. The lack of a standard for graphene materials for quality assurance and quality control poses a great challenge not only for the markets but also for commercialization efforts. A first-look economic feasibility analysis of the UCP technology provided valuable information that suggests the UCP process would be feasible, especially when it is scaled to a pilot scale and could be more competitive at the full scale. Graphitization was found to be the most energy-consuming and most capital-intensive step in the overall process. In small laboratory- and bench-scale experiments, labor is a significant contributor to the total process costs. Although these energy, capital, and labor constraints contribute to a higher selling price for the product, a preliminary economic model suggests that the process would be feasible at large scale when the process is fully integrated, optimized, and automated.

01 COAL, LIGNITE, AND PEAT↗

Electrochemical Detection of Oxycodone and Its Main Metabolites with Nafion-Coated Single-Walled Carbon Nanotube Electrodes

Oxycodone is a strong opioid frequently used as an analgesic. Although proven efficacious in the management of moderate to severe acute pain and cancer pain, use of oxycodone imposes a risk of adverse effects such as addiction, overdose, and death. Fast and accurate determination of oxycodone blood concentration would enable personalized dosing and monitoring of the analgesic as well as quick diagnostics of possible overdose in emergency care. However, in addition to the parent drug, several metabolites are always present in the blood after a dose of oxycodone, and to date, there is no electrochemical data available on any of these metabolites. In this paper, a single-walled carbon nanotube (SWCNT) electrode and a Nafion-coated SWCNT electrode were used, for the first time, to study the electrochemical behavior of oxycodone and its two main metabolites, noroxycodone and oxymorphone. Both electrode types could selectively detect oxycodone in the presence of noroxycodone and oxymorphone. However, we have previously shown that addition of a Nafion coating on top of the SWCNT electrode is essential for direct measurements in complex biological matrices. Therefore, in this work, the Nafion/SWCNT electrode was further characterized and used for measuring clinically relevant concentrations of oxycodone in buffer solution. The limit of detection for oxycodone with the Nafion/SWCNT sensor was 85 nM, and the linear range was 0.5–10 μM in buffer solution. Furthermore, this study shows that the fabricated Nafion/SWCNT sensor has potential to be applied in clinical concentration measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 - ), total acid (H + ), neodymium (Nd 3+ ), sodium (Na + ), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Demonstration of VOC Fenceline Sensors and Canister Grab Sampling near Chemical Facilities in Louisville, Kentucky

Experimental fenceline sensor pods (SPods) fitted with 30 s duration canister grab sampling (CGS) systems were deployed at a site near chemical facilities in Louisville, KY, from 4 June 2018 to 5 January 2020. The objective of the study was to better understand lower cost 10.6 eV photoionization detector (PID)-based volatile organic compound (VOC) sensors and investigate their utility for near-source emissions detection applications. Prototype SPods containing PID sensor elements from two different manufacturers yielded between 78% and 86% valid data over the study, producing a dataset of over 120,000 collocated pair fenceline measurements averaged into 5-min datapoints. Ten-second time-resolved SPod data from an elevated fenceline sensor signal day are presented, illustrating source emission detections from the direction of a facility 500 m west of the monitoring site. An SPod-triggered CGS acquired in the emission plume on this day contained elevated concentrations of 1,3-butadiene and cyclohexane (36 parts per billion by volume (ppbv) and 637 ppbv, respectively), compounds known to be emitted by this facility. Elevated concentrations of these compounds were observed in a subset of the 61 manual and triggered CGS grab samples acquired during the study, with winds from the west. Using novel wind-resolved visualization and normalization approaches described herein, the collocated pair SPod datasets exhibited similarity in emission source signature. With winds from the west, approximately 50% of SPod readings were above our defined theoretical detection limit indicating persistent measurable VOC signal at this site. Overall, this 19-month study demonstrated reasonable prototype SPod operational performance indicating that improved commercial forms of lower cost PID sensors could be useful for select VOC fenceline monitoring applications.

1,3-butadiene↗

Development of an Attenuated Total Reflectance–Ultraviolet–Visible Probe for the Online Monitoring of Dark Solutions

Optical spectroscopy is a valuable tool for on-line monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy in particular, can monitor the concentration of analytes as well as identify speciation and oxidation state. However, it can be difficult to impossible to employ UV-vis based sensors on chemical systems that are very dark (i.e., high optical density) as exceedingly short pathlengths are required (for transmission approaches) or effective means of backscattering are needed (for reflectance approaches). Examples of processes that would benefit significantly from the use of optical sensors and encounter these challenges include used nuclear fuel recycling and molten salts with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for varied processes can be time consuming and expensive. Here, in this study, we evaluate the ability for a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible “pathlengths” by altering the exposed ATR fiber length. Also studied were approaches to loading and measuring the sensor cell. These results are compared to a traditional 1 cm cuvette measured by transmission UV-vis. It was found that the ATR-UV-vis probe was capable of measuring solutions 600 times more concentrated than the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the interrogation of dark solutions is a promising avenue for use in on-line monitoring of nuclear processes.

47 OTHER INSTRUMENTATION↗

L-edge spectroscopy of dilute, radiation-sensitive systems using a transition-edge-sensor array

Here, we present X-ray absorption spectroscopy and resonant inelastic X-ray scattering (RIXS) measurements on the iron L-edge of 0.5 mM aqueous ferricyanide. These measurements then demonstrate the ability of high-throughput transition-edge-sensor (TES) spectrometers to access the rich soft X-ray (100–2000 eV) spectroscopy regime for dilute and radiation-sensitive samples. Our low-concentration data are in agreement with high-concentration measurements recorded by grating spectrometers. These results show that soft-X-ray RIXS spectroscopy acquired by high-throughput TES spectrometers can be used to study the local electronic structure of dilute metal-centered complexes relevant to biology, chemistry, and catalysis. In particular, TES spectrometers have a unique ability to characterize frozen solutions of radiation- and temperature-sensitive samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units

Developing accurate building energy simulation models to assist energy efficiency at speed and scale is one of the research goals of the Whole-Building and Community Integration group, which is a part of Building Technologies Research and Integration Center (BTRIC) at Oak Ridge National Laboratory (ORNL). The aim of the Autotune project is to speed up the automated calibration of building energy models to match measured utility or sensor data. The workflow of this project takes input parameters and runs EnergyPlus simulations on Oak Ridge Leadership Computing Facility’s (OLCF) computing resources such as Titan, the world’s second fastest supercomputer. Multiple simulations run in parallel on nodes having 16 processors each and a Graphics Processing Unit (GPU). Each node produces a 5.7 GB output file comprising 256 files from 64 simulations. Four types of output data covering monthly, daily, hourly, and 15- minute time steps for each annual simulation is produced. A total of 270TB+ of data has been produced. In this project, the simulation data is statistically analyzed in-situ using GPUs while annual simulations are being computed on the traditional processors. Titan, with its recent addition of 18,688 Compute Unified Device Architecture (CUDA) capable NVIDIA GPUs, has greatly extended its capability for massively parallel data processing. CUDA is used along with C/MPI to calculate statistical metrics such as sum, mean, variance, and standard deviation leveraging GPU acceleration. The workflow developed in this project produces statistical summaries of the data which reduces by multiple orders of magnitude the time and amount of data that needs to be stored. These statistical capabilities are anticipated to be useful for sensitivity analysis of EnergyPlus simulations

accelerators↗

Distinct regions that control ion selectivity and calcium-dependent activation in the bestrophin ion channel

Cytoplasmic calcium (Ca 2+ ) activates the bestrophin anion channel, allowing chloride ions to flow down their electrochemical gradient. Mutations in bestrophin 1 (BEST1) cause macular degenerative disorders. Previously, we determined an X-ray structure of chicken BEST1 that revealed the architecture of the channel. Here, we present electrophysiological studies of purified wild-type and mutant BEST1 channels and an X-ray structure of a Ca 2+ -independent mutant. From these experiments, we identify regions of BEST1 responsible for Ca 2+ activation and ion selectivity. A “Ca 2+ clasp” within the channel’s intracellular region acts as a sensor of cytoplasmic Ca 2+ . Alanine substitutions within a hydrophobic “neck” of the pore, which widen it, cause the channel to be constitutively active, irrespective of Ca 2+ . We conclude that the primary function of the neck is as a “gate” that controls chloride permeation in a Ca 2+ -dependent manner. In contrast to what others have proposed, we find that the neck is not a major contributor to the channel’s ion selectivity. We find that mutation of a cytosolic “aperture” of the pore does not perturb the Ca 2+ dependence of the channel or its preference for anions over cations, but its mutation dramatically alters relative permeabilities among anions. The data suggest that the aperture functions as a size-selective filter that permits the passage of small entities such as partially dehydrated chloride ions while excluding larger molecules such as amino acids. Furthermore, unlike ion channels that have a single “selectivity filter,” in bestrophin, distinct regions of the pore govern anion-vs.-cation selectivity and the relative permeabilities among anions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. Here, we present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

97 MATHEMATICS AND COMPUTING↗

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replace the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.

25 ENERGY STORAGE↗

Colossal Magnetoresistance without Mixed Valence in a Layered Phosphide Crystal

Materials with strong magnetoresistive responses are the backbone of spintronic technology, magnetic sensors, and hard drives. Among them, manganese oxides with a mixed valence and a cubic perovskite structure stand out due to their colossal magnetoresistance (CMR). A double exchange interaction underlies the CMR in manganates, whereby charge transport is enhanced when the spins on neighboring Mn 3+ and Mn 4+ ions are parallel. Prior efforts to find different materials or mechanisms for CMR resulted in a much smaller effect. Here an enormous CMR at low temperatures in EuCd 2 P 2 without manganese, oxygen, mixed valence, or cubic perovskite structure is shown. EuCd 2 P 2 has a layered trigonal lattice and exhibits antiferromagnetic ordering at 11 K. The magnitude of CMR (10(4)%) in as-grown crystals of EuCd 2 P 2 rivals the magnitude in optimized thin films of manganates. In this work, the magnetization, transport, and synchrotron X-ray data suggest that strong magnetic fluctuations are responsible for this phenomenon. The realization of CMR at low temperatures without heterovalency leads to a new regime for materials and technologies related to antiferromagnetic spintronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advanced Natural Gas Reciprocating Engine(s)

Energy independence and fuel savings are hallmarks of the nation’s energy strategy. The advancement of natural gas reciprocating engine power generation technology is critical to the nation’s future. A new engine platform that meets the efficiency, emissions, fuel flexibility, cost and reliability/maintainability targets will enable American manufacturers to have highly competitive products that provide substantial environmental and economic benefits in the US and in international markets. Along with Cummins and Waukesha, Caterpillar participated in a multiyear cooperative agreement with the Department of Energy to create a 50% efficiency natural gas powered reciprocating engine system with a 95% reduction in NOx emissions by the year 2013. This platform developed under this agreement will be a significant contributor to the US energy strategy and will enable gas engine technology to remain a highly competitive choice, meeting customer cost of electricity targets, and regulatory environmental standard. Engine development under the Advanced Reciprocating Engine System (ARES) program was divided into phases, with the ultimate goal being approached in a series of incremental steps. This incremental approach would promote the commercialization of ARES technologies as soon as they emerged from development and would provide a technical and commercial foundation of later-developing technologies. Demonstrations of the Phase I and Phase II technology were completed in 2004 and 2008, respectively. Program tasks in Phase III included component and system development and testing from 2009-2012. Two advanced ignition technology evaluations were investigated under the ARES program: laser ignition and distributed ignition (DIGN). In collaboration with Colorado State University (CSU), a laser ignition system was developed to provide ignition at lean burn and high boost conditions. Much work has been performed in Caterpillar’s DIGN program under the ARES program. This work has consisted of both modeling and single cylinder engine experiments to quantify DIGN performance. The air handling systems of natural gas engines dissipate a percentage of available energy as a result of both flow losses and turbomachinery inefficiencies. An analytical study was initiated to increase compressor efficiency by employing a 2-stage inter-cooled compressor. Caterpillar also studied a turbo-compound system that employs a power turbine to recover energy from the exhaust gases for improved engine efficiency. Several other component and system investigations were undertaken during the final phase of the program to reach the ultimate ARES goals. An intake valve actuation system was developed and tested to improve engine efficiency, durability and load acceptance. Analytical modeling and materials testing were performed to evaluate the performance of steel pistons and compacted graphite iron cylinder head. Effort was made to improve the detonation sensing system by studying and comparing the performance of different pressure sensors. To reduce unburned hydrocarbon emissions, different camshafts were designed and built to investigate the effect of exhaust valve opening timing and value overlap. 1-D & 3-D coupled simulation was used to study intake and exhaust manifold dynamics with the goal of reducing load in-balance between cylinders. Selective catalytic reduction with on-board reductant generation to reduce NOx emissions was also engine tested. An effective mean to successfully deploy ARES technologies into the energy markets is to deploy demonstration projects in the field. In 2010, NETL and Caterpillar agreed to include a new “opportunity fuel” deliverable and two field demonstrations in the ARES program. An Organic Rankine Cycle system was designed with production intent incorporating lessons learned from the Phase II demonstration. Unfortunately, business conditions caused Caterpillar to cancel this demonstration in 2011. Nonetheless, Caterpillar partnered with a local dealer to deploy an ARES class engine using syngas from a biomass gasifier as the DE-FC26-01CH11079 primary combustion fuel in Gleason, TN. Upon the successful start-up and commissioning of the demonstration unit, ownership of the system was transferred to the dealer. In order to further our understanding of syngas combustion, a fundamental combustion study on syngas combustion at high pressure and lean condition was conducted through the collaboration with University of Southern California. A Methane program was also developed to rate engine performance for various compositions of syngas using empirical data obtained at CSU. While much work remains in terms of extending and integrating these developments into commercial products, it is evident that engine manufacturers on our own or through private consortium efforts could not have overcome the financial hurdles to drive these improvements into reciprocating engine and system capabilities, helping maintain the natural gas reciprocating engine power generation technology as a strong option for electric power markets, both in the United States and worldwide.

20 FOSSIL-FUELED POWER PLANTS↗

CW EC-QCL-based sensor for simultaneous detection of H 2 O, HDO, N 2 O and CH 4 using multi-pass absorption spectroscopy

A sensor system based on a continuous wave, external-cavity quantum-cascade laser (CW EC-QCL) was demonstrated for simultaneous detection of atmospheric H 2 O, HDO, N 2 O and CH 4 using a compact, dense pattern multi-pass gas cell with an effective path-length of 57.6 m. The EC-QCL with a mode-hop-free spectral range of 1225-1285 cm -1 operating at similar to 7.8 mu m was scanned covering four neighboring absorption lines, for H 2 O at 1281.161 cm -1 , HDO at 1281.455 cm -1 , N 2 O at 1281.53 cm -1 and CH 4 at 1281.61 cm -1 . A first-harmonic-normalized wavelength modulation spectroscopy with second-harmonic detection (WMS-2f/1f) strategy was employed for data processing. An Allan-Werle deviation analysis indicated that minimum detection limits of 1.77 ppmv for H 2 O, 3.92 ppbv for HDO, 1.43 ppbv for N 2 O, and 2.2 ppbv for CH 4 were achieved with integration times of 50-s, 50-s, 100-s and 129-s, respectively. In conclusion, experimental measurements of ambient air are also reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

In this article, we report the growth of Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗