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At least 865 records · Page 48

High-rate and long-life lithium-ion battery performance of hierarchically hollow-structured NiCo 2 O 4 /CNT nanocomposite

In this paper, 3D-transition binary metal oxides have been considered as promising anode materials for lithium-ion batteries with improved reversible capacity, structural stability and electronic conductivity compared with single metal oxides. Here, carbon nanotube supported NiCo 2 O 4 nanoparticles (NiCo 2 O 4 /CNT) with 3D hierarchical hollow structure are fabricated via a simple one-pot method. The NiCo 2 O 4 nanoparticles with interconnected pores are consists of small nanocrystals. When used as anode material for the lithium-ion battery, NiCo 2 O 4 /CNT exhibits enhanced electrochemical performance than that of Co 3 O 4 /CNT and NiO/CNT. Moreover, ultra-high discharge/charge stability was obtained for 4000 cycles at a current density of 5 A g –1 . The superior battery performance of NiCo 2 O 4 nanoparticles is probably attributed to the special structural features and physical characteristics, including integrity, hollow structure with interconnected pores, which providing sufficient accommodation for the volume change during charge/discharge process. Besides, the consisting of ultra-small crystals enhanced the utility of active material, and intimate interaction with CNTs improved the electron-transfer rate.

36 MATERIALS SCIENCE↗

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagated to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.

60 APPLIED LIFE SCIENCES↗

Control of Ionomer Distribution and Porosity in Roll-to-Roll Coated Fuel Cell Catalyst Layers

As fuel cells transition from the laboratory to industrial production for vehicles and other applications there is a need to understand how manufacturing processes impact the properties and, ultimately, the performance of materials. Roll-to-roll coating enables processing at meters per second speeds with physics that can be quite different from common laboratory coating methods. In this work, we have focused on the coating of catalyst layers for proton exchange membrane fuel cells. Commonly, laboratory samples are prepared using spray coating or hand painting, which effectively coat many thin layers to achieve the desired catalyst layer thickness. This creates catalyst layers with uniform distributions of materials throughout the thickness of the full catalyst layer. In contrast, roll-to-roll methods, like slot die and gravure, coat the catalyst layer as a single wet film. As the film dries, ink constituents are able to segregate, leading to a heterogeneous distribution of materials through the thickness of the layer. It is also known that solvent and drying rate can affect the morphology of the catalyst layer. Thus, there is a need to understand how ink formulation and the drying process influence the distribution of materials and catalyst layer morphology. Here, we have explored the influence of solvent and drying rate on morphology of roll-to-roll coated catalyst layers. We show the development of the Kelvin probe method as a rapid screening technique for qualitative analysis of ionomer distribution. We also utilize nano X-ray computed tomography to visualize electrode structure and to quantify particle-size and pore-size distributions, thickness-dependent ionomer distribution, tortuosity, and effective transport properties. We find that solvent has a strong influence on ionomer distribution, with less of an effect on porosity. Conversely, drying temperature has a strong influence on porosity, but less influence on ionomer distribution. Finally, we utilize in situ fuel cell performance testing and other advanced diagnostics to quantify the impact of catalyst layer properties on fuel cell performance and demonstrate that roll-to-roll coating is capable of coating high performance catalyst layers in multi-meter lengths. have focused on the coating of catalyst layers for proton exchange membrane fuel cells. Commonly, laboratory samples are prepared using spray coating or hand painting, which effectively coat many thin layers to achieve the desired catalyst layer thickness. This creates catalyst layers with uniform distributions of materials throughout the thickness of the full catalyst layer. In contrast, roll-to-roll methods, like slot die and gravure, coat the catalyst layer as a single wet film. As the film dries, ink constituents are able to segregate, leading to a heterogeneous distribution of materials through the thickness of the layer. It is also known that solvent and drying rate can affect the morphology of the catalyst layer. Thus, there is a need to understand how ink formulation and the drying process influence the distribution of materials and catalyst layer morphology. Here, we have explored the influence of solvent and drying rate on morphology of roll-to-roll coated catalyst layers. We show the development of the Kelvin probe method as a rapid screening technique for qualitative analysis of ionomer distribution. We also utilize nano X-ray computed tomography to visualize electrode structure and to quantify particle-size and pore-size distributions, thickness-dependent ionomer distribution, tortuosity, and effective transport properties. We find that solvent has a strong influence on ionomer distribution, with less of an effect on porosity. Conversely, drying temperature has a strong influence on porosity, but less influence on ionomer distribution. Finally, we utilize in situ fuel cell performance testing and other advanced diagnostics to quantify the impact of catalyst layer properties on fuel cell performance and demonstrate that roll-to-roll coating is capable of coating high performance catalyst layers in multi-meter lengths.

30 DIRECT ENERGY CONVERSION↗

Long-Term Heating to Improve Receiver Performance

The buildup of hydrogen in the heat transfer fluid (HTF) that circulates through components of parabolic trough power plants decreases receiver thermal efficiency, and ultimately, it decreases plant performance and electricity output. The generation and occurrence of hydrogen in the HTF provides the driving force for hydrogen to permeate from the HTF through the absorber tube wall and into the receiver annulus. Getters adsorb hydrogen from the annulus volume until they saturate and are no longer able to maintain low hydrogen pressure. The increase in hydrogen pressure within the annulus significantly degrades thermal performance of the receiver and decreases overall power-plant efficiency. NREL and Acciona Energy North America (Acciona) are developing a method to control the levels of dissolved hydrogen in the circulating HTF. The basic approach is to remove hydrogen from the expansion tanks of the HTF subsystem at a rate that maintains hydrogen in the circulating HTF to a target level. Full-plant steady-state models developed by the National Renewable Energy Laboratory (NREL) predict that if hydrogen is removed from the HTF within the expansion tanks, the HTF that circulates through the collector field remains essentially free of hydrogen until the HTF returns to the power block in the hot headers. One of the key findings of our modeling is the prediction that hydrogen will reverse-permeate out of the receiver annulus if dissolved hydrogen in the HTF is kept sufficiently low. To test this prediction, we performed extended heating of an in-service receiver that initially had high levels of hydrogen in its annulus. The heating was performed using NREL's receiver test stand. Results of our testing showed that receiver heat loss steadily decreased with daily heating, resulting in a corresponding improvement in receiver thermal efficiency.

14 SOLAR ENERGY↗

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Soils Activity Mobility Study: Methodology and Application

This report presents a three-level approach for estimation of sediment transport to provide an assessment of potential erosion risk for sites at the Nevada National Security Site (NNSS) that are posted for radiological purposes and where migration is suspected or known to occur due to storm runoff. Based on the assessed risk, the appropriate level of effort can be determined for analysis of radiological surveys, field experiments to quantify erosion and transport rates, and long-term monitoring. The method is demonstrated at contaminated sites, including Plutonium Valley, Shasta, Smoky, and T-1. The Pacific Southwest Interagency Committee (PSIAC) procedure is selected as the Level 1 analysis tool. The PSIAC method provides an estimation of the total annual sediment yield based on factors derived from the climatic and physical characteristics of a watershed. If the results indicate low risk, then further analysis is not warranted. If the Level 1 analysis indicates high risk or is deemed uncertain, a Level 2 analysis using the Modified Universal Soil Loss Equation (MUSLE) is proposed. In addition, if a sediment yield for a storm event rather than an annual sediment yield is needed, then the proposed Level 2 analysis should be performed. MUSLE only provides sheet and rill erosion estimates. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) provides storm peak runoff rate and storm volumes, the inputs necessary for MUSLE. Channel Sediment Transport (CHAN-SED) I and II models are proposed for estimating sediment deposition or erosion in a channel reach from a storm event. These models require storm hydrograph associated sediment concentration and bed load particle size distribution data. When the Level 2 analysis indicates high risk for sediment yield and associated contaminant migration or when there is high uncertainty in the Level 2 results, the sites can be further evaluated with a Level 3 analysis using more complex and labor- and data-intensive methods. For the watersheds analyzed in this report using the Level 1 PSIAC method, the risk of erosion is low. The field reconnaissance surveys of these watersheds confirm the conclusion that the sediment yield of undisturbed areas at the NNSS would be low. The climate, geology, soils, ground cover, land use, and runoff potential are similar among these watersheds. There are no well-defined ephemeral channels except at the Smoky and Plutonium Valley sites. Topography seems to have the strongest influence on sediment yields, as sediment yields are higher on the steeper hill slopes. Lack of measured sediment yield data at the NNSS does not allow for a direct evaluation of the yield estimates by the PSIAC method. Level 2 MUSLE estimates in all the analyzed watersheds except Shasta are a small percentage of the estimates from PSIAC because MUSLE is not inclusive of channel erosion. This indicates that channel erosion dominates the total sediment yield in these watersheds. Annual sediment yields for these watersheds are estimated using the CHAN-SEDI and CHAN-SEDII channel sediment transport models. Both transport models give similar results and exceed the estimates obtained from PSIAC and MUSLE. It is recommended that the total watershed sediment yield of watersheds at the NNSS with flow channels be obtained by adding the washload estimate (rill and inter-rill erosion) from MUSLE to that obtained from channel transport models (bed load and suspended sediment). PSIAC will give comparable results if factor scores for channel erosion are revised towards the high erosion level. Application of the Level 3 process-based models to estimate sediment yields at the NNSS cannot be recommended at this time. Increased model complexity alone will not improve the certainty of the sediment yield estimates. Models must be calibrated against measured data before model results are accepted as certain. Because no measurements of sediment yields at the NNSS are available, model validation cannot be performed. This is also true for the models used in the Level 2 analyses presented in this study. The need to calibrate MUSLE to local conditions has been discussed. Likewise, the transport equations of CHAN-SEDI and CHAN-SEDII need to be calibrated against local data to assess their applicability under semi-arid conditions and for the ephemeral channels at the NNSS. Before these validations and calibration exercises can be undertaken, a long-term measured sediment yield data set must be developed. Development of long-term measured sediment yield data cannot be overemphasized. Long-term monitoring is essential for accurate characterization of watershed processes. It is recommended that a long-term monitoring program be set up to measure watershed erosion rates and channel sediment transport rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Microscopic mechanisms of graphene electrolytic delamination from metal substrates

In this paper, hydrogen bubbling delamination of graphene (Gr) from copper using a strong electrolyte (KOH) water solution was performed, focusing on the effect of the KOH concentration (C{sub KOH}) on the Gr delamination rate. A factor of ∼10 decrease in the time required for the complete Gr delamination from Cu cathodes with the same geometry was found increasing C{sub KOH} from ∼0.05 M to ∼0.60 M. After transfer of the separated Gr membranes to SiO{sub 2} substrates by a highly reproducible thermo-compression printing method, an accurate atomic force microscopy investigation of the changes in Gr morphology as a function of C{sub KOH} was performed. Supported by these analyses, a microscopic model of the delamination process has been proposed, where a key role is played by graphene wrinkles acting as nucleation sites for H{sub 2} bubbles at the cathode perimeter. With this approach, the H{sub 2} supersaturation generated at the electrode for different electrolyte concentrations was estimated and the inverse dependence of t{sub d} on C{sub KOH} was quantitatively explained. Although developed in the case of Cu, this analysis is generally valid and can be applied to describe the electrolytic delamination of graphene from several metal substrates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Electrochemical Performance of NaFeFe(CN) 6 Prepared by Solid Reaction for Sodium Ion Batteries

Commercially available Na 4 Fe(CN) 6 and Fe 4 [Fe(CN) 6 ] 3 are two cheap compounds that have ever been investigated as positive electrode materials for sodium-ion batteries. However, poor electronic conductivity of Na 4 Fe(CN) 6 and sodium deficiency of Fe 4 [Fe(CN) 6 ] 3 prevent these two materials from being used in practical rechargeable sodium-ion batteries. In this paper, a NaFeFe(CN) 6 cathode material was synthesized by ball milling the Fe 4 [Fe(CN) 6 ] 3 / Na 4 Fe(CN) 6 mixture. The obtained NaFeFe(CN) 6 demonstrated a single cubic phase indexed to Fm3m space group similar to Fe 4 [Fe(CN) 6 ] 3 but with larger lattice parameter due to the existence of Na + in the lattice framework. The NaFeFe(CN) 6 electrode delivered first desodiation capacity of 119.4 mAh g -1 and first sodiation capacity of 153.6 mAh g -1 at 0.05C rate. The NaFeFe(CN) 6 showed excellent cycling stability with reversible capacities of 118.2 mAh g -1 and 96.8 mAh g -1 at 0.1C and 1C rate, respectively. In-situ XRD analyses demonstrated a single cubic phase process during charge-discharge of NaFeFe(CN) 6 electrode. Low water content benefited from the solid reaction method and the homogenous single phase process during charge/discharge assured the stable long term cycling performance of the NaFeFe(CN) 6 product.

25 ENERGY STORAGE↗

Unfolding quantum computer readout noise

Abstract In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Load Forecasting in Electric Utility Integrated Resource Planning

Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗

Automated processing of environmental transmission electron microscopy images for quantification of thin film dewetting and carbon nanotube nucleation dynamics

We report scalable production of carbon nanotubes (CNTs) requires catalysts and reaction conditions that provide high nucleation efficiency. In situ characterization methods such as environmental transmission electron microscopy (ETEM) can reveal fundamental mechanisms of synthesis, but to date have primarily provided qualitative observations on small sample sizes. Here, quantitative analysis is performed using high-resolution, high-rate video capture of ETEM experimentation coupled with automated image processing, involving computer vision algorithms and convolutional neural networks. By this approach, we detect distinct nanoparticle formation from an alumina-supported iron thin film and subsequent CNT nucleation from the nanoparticles. The statistical summary of particles in each video shows that, compared to a H 2 -only atmosphere, pretreatment of the catalyst with carbon added to the H 2 atmosphere results in a smaller average particle diameter, a 2-fold increase in particle density (to 5300 particles/μm 2 ), a 3-fold increase in CNT nucleation efficiency (to 92%), and more than a 5-fold increase in CNT density (to 4800/μm 2 ). Addition of carbon during exposure to H 2 is also more effective than NH 3 at dewetting the catalyst film and increasing the CNT nucleation efficiency, in spite of NH 3 being a stronger reducing agent for iron. Insights from this study are applicable to improving CNT yield and productivity in both batch-style and continuous processes.

25 ENERGY STORAGE↗

Parallel accelerated vector similarity calculations for genomics applications

The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this study we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Finally, results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 10 15 ) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques applied to calculations of the Custom Correlation Coefficient for comparative genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗

Nature of Terminating Hydroxyl Groups and Intercalating Water in Ti 3 C 2 T x MXenes: A Study by 1 H Solid-State NMR and DFT Calculations

Since the discovery of two-dimensional transition metal carbides, referred to as MXenes, research efforts targeted their applications in energy storage, as lithium-ion batteries and supercapacitors. This interest is attributable to MXenes’ large volumetric capacitance, high rate handling capability and stable cycling performance, which largely rely on surface chemistry provided by the terminating groups, such as -OH, -O, and -F. However, the atomic-scale characterization of these surface terminations challenges the diffraction methods. Solid-state (SS)NMR spectroscopy, especially 1 H SSNMR, is a promising approach for scrutinizing the surface terminations and the intercalated water in an atomistic-scale, yet only a few SSNMR studies in MXenes have been reported to date offering conflicting results and limited understanding of -OH terminations. Here, we used 1 H SSNMR experiments in concert with the DFT calculations of NMR parameters to identify multiple types of -OH groups, residing on the external and internal surfaces, in a commonly studied MXene, Ti 3 C 2 T x . The study also identifies bulk-like water trapped between the MXene flakes and interfacial water stranded on the surface. Lastly, two-dimensional 1 H -1 H correlation spectra elucidated the water-surface interactions and the mechanism of de-intercalation of water upon annealing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Organic Modifiers Promote Furfuryl Alcohol Ring Hydrogenation via Surface Hydrogen-Bonding Interactions

Interactions between surface adsorbed species can affect catalyst reactivity, and thus, the ability to tune these interactions is of considerable importance. Deposition of organic modifiers provides one method of intentionally introducing controllable surface interactions onto catalyst surfaces. In this study, Pd/Al2O 3 catalysts were modified with either thiol or phosphonic acid (PA) ligands and tested in the hydrogenation of furanic species. The thiol modifiers were found to inhibit ring hydrogenation (RH) activity, with the degree of inhibition trending with the thiol surface coverage. This suggests that thiols do not strongly interact with the reactants and simply serve to block active sites on the Pd surface. PAs, on the other hand, were found to enhance RH when furfuryl alcohol (FA) was used as the reactant. Density functional theory calculations suggested that this enhancement was due to hydrogen-bonding interactions between FA-derived surface intermediates and PA modifiers. Here, installation of hydrogen-bonding groups on the Pd surface served to preferentially stabilize RH product states. Furthermore, the promotional effect on the RH of FA was observed to be greater when a higher-coverage PA was used, providing a rate more than twice that of the unmodified Pd/Al2O 3 . The results of this work suggest that organic ligands can be designed to impart tunable surface interactions on heterogeneous catalysts, providing an additional method of controlling catalytic performance.

09 BIOMASS FUELS↗

Spatial and Temporal Quantification of Spontaneous Imbibition

Spontaneous imbibition–the process of a wetting fluid displacing a non-wetting fluid purely by capillary forces–is a ubiquitous phenomenon in porous and fibrous materials. Here we present a new experimental method for quantification of spontaneous imbibition in geologic materials. This method makes it possible to perform spontaneous imbibition under elevated pressure conditions relevant to environmental and energy resource applications. In this work, computed tomography imaging reveals a new time-independent scaling relationship that describes local imbibition rates as a function of water saturation. Imbibition capillary pressure curves are calculated with this wetting-phase pressure and flowrate characterization, with no assumptions about the functional form, end point behavior, or scaling factors. Calculated end-point capillary pressure is nonzero, in agreement with recent pore-scale measurements of capillary pressure of trapped nonwetting phase. This work provides a new approach and insights into trapping and remobilization of non-wetting fluids in CO 2 storage reservoirs and contaminated groundwater aquifers.

58 GEOSCIENCES↗

Fault Location Using the Natural Frequency of Oscillation of Current Discharge in MTdc Networks

This paper discusses a novel offline-fault location approach using single ended measurements. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established. The hybrid dc circuit breaker (dcCB) interrupts the fault current, whereby the transmission line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be obtained. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

42 ENGINEERING↗

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗