A Low-Swelling Polymeric Mixed Conductor Operating in Aqueous Electrolytes
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99 GENERAL AND MISCELLANEOUS↗
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Assessment of Demographic, Genetic, and Imaging Variables Associated With Brain Resilience and Cognitive Resilience to Pathological Tau in Patients With Alzheimer Disease
Importance: Better understanding is needed of the degree to which individuals tolerate Alzheimer disease (AD)-like pathological tau with respect to brain structure (brain resilience) and cognition (cognitive resilience). Objective: To examine the demographic (age, sex, and educational level), genetic (APOE-ε4 status), and neuroimaging (white matter hyperintensities and cortical thickness) factors associated with interindividual differences in brain and cognitive resilience to tau positron emission tomography (PET) load and to changes in global cognition over time. Design, setting, an participants: In this cross-sectional, longitudinal study, tau PET was performed from June 1, 2014, to November 30, 2017, and global cognition monitored for a mean [SD] interval of 2.0 [1.8] years at 3 dementia centers in South Korea, Sweden, and the United States. The study included amyloid-β-positive participants with mild cognitive impairment or AD dementia. Data analysis was performed from October 26, 2018, to December 11, 2019. Exposures: Standard dementia screening, cognitive testing, brain magnetic resonance imaging, amyloid-β PET and cerebrospinal fluid analysis, and flortaucipir (tau) labeled with fluor-18 (18F) PET. Main outcomes and measures: Separate linear regression models were performed between whole cortex [18F]flortaucipir uptake and cortical thickness, and standardized residuals were used to obtain a measure of brain resilience. The same procedure was performed for whole cortex [18F]flortaucipir uptake vs Mini-Mental State Examination (MMSE) as a measure of cognitive resilience. Bivariate and multivariable linear regression models were conducted with age, sex, educational level, APOE-ε4 status, white matter hyperintensity volumes, and cortical thickness as independent variables and brain and cognitive resilience measures as dependent variables. Linear mixed models were performed to examine whether changes in MMSE scores over time differed as a function of a combined brain and cognitive resilience variable. Results: A total of 260 participants (145 [55.8%] female; mean [SD] age, 69.2 [9.5] years; mean [SD] MMSE score, 21.9 [5.5]) were included in the study. In multivariable models, women (standardized β = -0.15, P = .02) and young patients (standardized β = -0.20, P = .006) had greater brain resilience to pathological tau. Higher educational level (standardized β = 0.23, P < .001) and global cortical thickness (standardized β = 0.23, P < .001) were associated with greater cognitive resilience to pathological tau. Linear mixed models indicated a significant interaction of brain resilience × cognitive resilience × time on MMSE (β [SE] = -0.235 [0.111], P = .03), with steepest slopes for individuals with both low brain and cognitive resilience. Conclusions and relevance: Results of this study suggest that women and young patients with AD have relative preservation of brain structure when exposed to neocortical pathological tau. Interindividual differences in resilience to pathological tau may be important to disease progression because participants with both low brain and cognitive resilience had the most rapid cognitive decline over time.
60 APPLIED LIFE SCIENCES↗
Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics
Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers' thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Additionally, extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.
14 SOLAR ENERGY↗
Comparing plant litter molecular diversity assessed from proximate analysis and 13 C NMR spectroscopy
Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13 C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.
59 BASIC BIOLOGICAL SCIENCES↗
Towards Precision-Aware Fault Tolerance Approaches for Mixed-Precision Applications
Graphics Processing Units (GPUs), the dominantly adopted accelerators in HPC systems, are susceptible to transient hardware fault. New generation of GPUs feature mixed-precision architectures such as NVIDIA Tensor Cores to accelerate matrix multiplications. While being widely adapted, how would they behave under transient hardware faults remain unclear. In this study, we conduct a large-scale fault injection experiments on GEMM kernels implemented with different floating-point data types on the V100 and A100 Tensor Cores, and show distinct error resilience characteristics for the GEMMS with different formats. In the future, we plan to explore this space by building precision-aware floating-point fault tolerance techniques for applications such as DNNs that exercise low-precision computations.
Fang, Bo↗
EGS Collab Experiment 1: SIMFIP Notch-164 GRL Paper
Characterizing the stimulation mode of a fracture is critical to assess the hydraulic efficiency and the seismic risk related to deep fluid manipulations. We have monitored the three-dimensional displacements of a fluid-driven fracture during water injections in a borehole at ~1.5 km depth in the crystalline rock of the Sanford Underground Research Facility (USA). The fracture initiates at 61% of the minimum horizontal stress by micro-shearing of the borehole on a foliation plane. As the fluid pressure increases further, borehole axial and radial displacements increase with injection time highlighting the opening and sliding of a new hydrofracture growing ~10 m away from the borehole, in accordance with the ambient normal stress regime and in alignment with the microseismicity. Our study reveals how fluid-driven fracture stimulation can be facilitated by a mixed-mode process controlled by the complex hydromechanical evolution of the growing fracture. The data presented in this submission refer to the SIMFIP measurements and analyses of the stimulation tests conducted on the 164 ft (50 m) notch of the Sanford Underground Research Facility (SURF), during the EGS-Collab test 1. In addition to the datafiles, there is the draft of a manuscript submitted to Geophysical Research Letters (GRL).
15 GEOTHERMAL ENERGY↗
Small field campaign: aerosol – ice formation closure pilot study. Final Report
Prediction of atmospheric ice formation from aerosol particles by heterogeneous nucleation represents one of the grand challenges in atmospheric science. Our insufficient predictive understanding of primary ice formation is the reason that climate models typically do not include heterogeneous ice nucleation with subsequent effects on climate uncertainty. Mixed-phase clouds, where supercooled water droplets and ice crystals coexist play globally an important role regulating climate. This is especially the case for the Arctic region that experiences the greatest warming due to climate change compared to other regions in the world. Immersion freezing initiated by ice-nucleating particles (INPs) in supercooled water droplets is recognized as the dominant primary ice formation pathway in mixed-phase cloud regimes. For this reason, it is crucial to evaluate our capability to predict immersion freezing for a given ambient aerosol population. The goal of this project is conducting a field-based pilot study at the U.S. DOE Atmospheric Radiation Measurement (ARM) user facility at Southern Great Plains (SGP) to evaluate our capability to predict the number concentration of aerosol particles that serve as INPs in the immersion freezing mode. Successful prediction of INP number concentrations is also termed “closure”. This field-observational approach represents a first-of-its kind attempt of an aerosol–ice formation closure study (AEROICESTUDY). Very few closure studies related to INPs have been conducted, and to our knowledge, none using robust size-resolved ambient aerosol composition measurements as a starting point. Achievement of aerosol–ice formation closure relies on our ability to characterize the ambient aerosol population with respect to particles size and composition and to determine INP number concentrations for specified freezing temperatures. This requires numerous online and offline instrumentation resulting in this pilot field campaign being a multi-institutional and community-collaborative effort. We chose the ARM SGP megasite for this first aerosol-ice formation closure pilot study due to its significant measurement capabilities available to obtain detailed physical characterization of the local aerosol population including size distribution, mass loading, and chemical composition of non-refractory aerosol particles. The overall objective of this project is to identify ice nucleation parameterizations that produce the most robust predictions of INP numbers and thus are best suited to be included in cloud and climate models. This objective includes the following goals for this field and laboratory-based project: i) What are the crucial aerosol physicochemical property measurements needed to accurately guide ice nucleation representations in models and long-term INP measurements? ii) What level of parameter details needs to be known to achieve aerosol–ice formation closure? iii) What are the leading causes for climate model bias in INP predictions? We found that the advances in our understanding of immersion freezing garnered over the last 20 years allowed us to yield partial and full closures of atmospheric immersion freezing from ambient aerosol particles. When the aerosol population is physicochemically complex and parameterizations for representative INP types are not yet available, we still struggle to accurately predict INP number concentrations. This project clearly demonstrates that with more laboratory and field measurements that are accompanied by particle composition analysis, the necessary datasets to achieve aerosol–ice formation closure for various locations will emerge, thus providing a robust foundation for guiding the representation of INPs in cloud and climate models.
54 ENVIRONMENTAL SCIENCES↗
Isotope effects on transport in LHD
Abstract Isotope effects are one of the most important issues for predicting future reactor operations. Large helical device (LHD) is the presently working largest stellarator/helical device using super conducting helical coils. In LHD, deuterium experiments started in 2017. Extensive studies regarding isotope effects on transport have been carried out. In this paper, the results of isotope effect studies in LHD are reported. The systematic studies were performed adjusting operational parameters and nondimensional parameters. In L mode like normal confinement plasma, where internal and edge transport barriers are not formed, the scaling of global energy confinement time ( τ E ) with operational parameters shows positive mass dependence ( M 0.27 ; where M is effective ion mass) in electron cyclotron heating plasma and no mass dependence ( M 0.0 ) in neutral beam injection heating plasma. The non-negative ion mass dependence is anti-gyro-Bohm scaling. The role of the turbulence in isotope effects was also found by turbulence measurements and gyrokinetic simulation. Better accessibility to electron and ion internal transport barrier (ITB) plasma is found in deuterium (D) plasma than in hydrogen (H). Gyro kinetic non-linear simulation shows reduced ion heat flux due to the larger generation of zonal flow in deuterium plasma. Peaked carbon density profile plays a prominent role in reducing ion energy transport in ITB plasma. This is evident only in plasma with deuterium ions. New findings on the mixing and non-mixing states of D and H particle transports are reported. In the mixing state, ion particle diffusivities are higher than electron particle diffusivities and D and H ion density profiles are almost identical. In the non-mixing state, ion particle diffusivity is much lower than electron diffusivity. Deuterium and hydrogen ion profiles are clearly different. Different turbulence structures were found in the mixing and non-mixing states suggesting different turbulence modes play a role.
Physics↗
Mixing-Controlled CI Combustion and Fuel-Effects Research
This project is focused on developing advanced combustion strategies for mixing-controlled compressionignition (i.e., diesel-cycle) engines that are synergistic with renewable and/or unconventional fuels in a manner that enhances domestic energy security, economic competitiveness, and environmental quality. During this reporting period, the focus was on ducted fuel injection (DFI), a technology that differs from conventional diesel combustion (CDC) in that it involves injecting fuel along the axis of one or more small cylindrical ducts within the combustion chamber. Each duct performs a function similar to the tube on a Bunsen burner, helping to premix the fuel with the charge-gas before ignition, creating a stable flame that forms little to no soot. The purpose of the work conducted during Fiscal Year (FY) 2019 was to begin determining the extent to which the use of oxygenated fuels, when combined with DFI and charge-gas dilution, can simultaneously lower the soot and nitrogen-oxides (N0 x ) emissions from mixing-controlled compression-ignition engines, and what the corresponding impacts on other regulated emissions and efficiency are likely to be.
33 ADVANCED PROPULSION SYSTEMS↗
Multiscale and Multimodal Characterization of 2D Titanium Carbonitride MXene
Here, a comprehensive study on the prototype solid solution phase carbonitride MXene Ti 3 CN is conducted using nuclear magnetic resonance, electron spin resonance, total and quasi-elastic neutron scattering, combined with density functional theory-based electronic structure and molecular dynamic calculations. The combination of experiment and theory lead toward rational atomic structural models of Ti 3 CN. The remnant Al ions from the etching process significantly tune the interlayer spacing, distinct from the more typical MXene, Ti 3 C 2 , prepared similarly. Neutron scattering indicates the surface terminations of Ti 3 CN display high oxygen and fluorine concentrations and rather low hydroxyl and hydrogen concentrations. Calculations show that the structure including both the residual Al ions and mixed surface terminations give the best agreement with the measurements. The water molecules in Ti 3 CN are highly immobile, in strong contrast to those in Ti 3 C 2 . The analysis of the electronic structure suggests that the nitride MXene displays higher conductivity than the carbides. Finally, the absence of hydroxyl groups in terminations, the solid-solution in the anion sites, the remnants within layers, and immobile water altogether make the carbonitrides a unique series in the MXene family, implying a further exploration of their exotic properties and applications in energy storage.
36 MATERIALS SCIENCE↗
Mosaic Cu I –Cu II –In III 2D Perovskites: Pressure–Dependence of the Intervalence Charge Transfer and a Mechanochemical Alloying Method
The perovskite (BA) 4 [Cu II (Cu I In III ) 0.5 ]Cl 8 (1 BA ; BA + =butylammonium) allows us to study the high-pressure structural, optical, and transport properties of a mixed-valence 2D perovskite. Compressing 1 BA reduces the onset energy of Cu I/II intervalence charge transfer from 1.2 eV at ambient pressure to 0.2 eV at 21 GPa. The electronic conductivity of 1 BA increases by 4 orders of magnitude upon compression to 20 GPa, when the activation energy for conduction decreases to 0.16 eV. In contrast, Cu II perovskites achieve similar conductivity at ≈50 GPa. The solution-state synthesis of these perovskites is complicated, with more undesirable side products likely from the precursor mixtures containing three different metal ions. Finally, to circumvent this problem, we demonstrate an efficient mechanochemical synthesis to expand this family of halide perovskites with complex composition by simply pulverizing together powders of 2D Cu II single perovskites and Cu I In III double perovskites.
2D perovskite↗
Mosaic Cu I −Cu II −In III 2D Perovskites: Pressure‐Dependence of the Intervalence Charge Transfer and a Mechanochemical Alloying Method
Abstract The perovskite (BA) 4 [Cu II (Cu I In III ) 0.5 ]Cl 8 ( 1 BA ; BA + =butylammonium) allows us to study the high‐pressure structural, optical, and transport properties of a mixed‐valence 2D perovskite. Compressing 1 BA reduces the onset energy of Cu I/II intervalence charge transfer from 1.2 eV at ambient pressure to 0.2 eV at 21 GPa. The electronic conductivity of 1 BA increases by 4 orders of magnitude upon compression to 20 GPa, when the activation energy for conduction decreases to 0.16 eV. In contrast, Cu II perovskites achieve similar conductivity at ≈50 GPa. The solution‐state synthesis of these perovskites is complicated, with more undesirable side products likely from the precursor mixtures containing three different metal ions. To circumvent this problem, we demonstrate an efficient mechanochemical synthesis to expand this family of halide perovskites with complex composition by simply pulverizing together powders of 2D Cu II single perovskites and Cu I In III double perovskites.
Li, Jiayi↗
Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate
In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building’s operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid’s carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers’ performance. Simulation results indicate that the carbon responsive controllers can reduce the homes’ annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between –2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A High-Ceramic-Loading LATP–PVDF–Al 2 O 3 Composite Film for Lithium-Ion Batteries With Favorable Porous Microstructure and Enhanced Thermal Stability
A separator plays a crucial role in a Li-ion battery to carry liquid electrolytes while preventing short-circuiting between electrodes. Nevertheless, conventional commercial separators often exhibit poor wettability and are prone to shrink at elevated temperatures due to their limited thermal stability. Herein, we report a heat-resistant LATP–PVDF–Al 2 O 3 composite film with outstanding wetting performance. The thin film was prepared using ball mill mixing and tape-casting processes. Two solvents, NMP and glycerol, were applied to prepare the slurry, and a favorable microstructure in the film was created after drying. Here, the ionic conductivity of the film was tested at 1.39 mS cm –1 when paired with liquid electrolyte, almost double that of the commercial counterpart. The high ceramic loading of 70% improved both the thermal shrinkage resistance and dendrite inhibition of the membrane. When assembled in an NMC half-cell, the cycling capacity retentions of 92.8% and 92.1% are achieved after 50 cycles at 0.5 C and 1 C, demonstrating its capability to be used in Li-ion batteries.
25 ENERGY STORAGE↗
Development of 3D Printing Techniques for Solid-State Lithium Batteries: Energy Storage Internship (Final Report)
The goal of this internship was to develop a solid polymer electrolyte with a suitable ionic conductivity and mechanical properties and integrate it with 3D printed cathode for solid state lithium batteries. While liquid electrolytes will have much higher conductivities and therefore higher power densities than solid electrolytes, batteries with such electrolytes suffer from extreme dendritic growth, short-circuiting, and general failure. Typically, batteries prevent this by including a solid yet porous separator somewhere between the anode and cathode to suppress dendritic growth. Solid polymer electrolyte acts as the conductive medium between electrodes as well as the separator that prevents failure from short-circuiting. The specific system explored is a vinyl ethylene carbonate (VEC) and polyethylene glycol diacrylate (PEGDA) mixed polymer solution using both 2,2’-azobis(2-methylpropionitrile) (AIBN) and Irgacure 819, respectively as thermal and photo- initiators. Electrochemical and rheological techniques were used to determine the optimal polymer solution and curing conditions. Through this research, multiple new pathways of exploration have been determined to create a usable solid polymer electrolyte through a combination of varying cure intensities and initiator concentrations.
25 ENERGY STORAGE↗
Bench-Scale Development of Promoted High-Capacity Structured Sorbents (Final Technical Report)
The project objective was to develop high-capacity structured sorbent capable of achieving low CO 2 removal from air. The sorbent framework consists of an amine-functionalized onto hydrophobic polymer backbone with an added promoter. The functionalized amine provides high CO 2 capacity and adsorption rates and the polymer backbone to reduce water uptake. For the sorbent development, multiple functionalized amines and promoters were assessed to select a candidate that achieved high CO 2 capacity, high adsorption rate, and high stability. A sorbent-coated filter design was selected as the structured sorbent, which provides high sorbent loading capacity and contains an electrically conductive nonwoven filter substrate that can be Joule-heated to provide efficient utilization of available electricity for sorbent regeneration. A commercial partner operated a pilot filter manufacturing line to produce the filter panels coated with the developed sorbent. A 1 kg CO 2 /day bench unit was designed and fabricated to test the structured filter sorbent. The key achievements from the structured sorbent development activity were demonstrating that existing filter industrial-scale processes can be used for manufacturing Susteon’s structured sorbent and completing a proof-of-concept demonstration of the commercially manufactured structured sorbent filters for CO 2 capture from air with direct, Joule-heated regeneration. A techno-economic assessment with sensitivity analysis was conducted on a 100,000 TPY facility with 85% operating capacity. Through sorbent optimization and process design improvements, it is estimated that the cost of capture was reduced from $\$$349/tCO 2 to $\$$241/tCO 2 . The TEA projects further reductions to $165/tCO 2 through enhancements in CO 2 adsorption rate and sorbent capacity and reducing manufacturing and scale up risks. A life cycle analysis was conducted on the same 100,000 TPY facility and confirmed that the facility’s electricity demand drives its greenhouse gas impact. It was determined that electricity supplied through the current grid mix would result in net-positive CO 2 emissions and that achieving net-negative emissions is only possible by powering the system with renewable electricity or fossil fuel sources equipped with carbon capture and sequestration.
36 MATERIALS SCIENCE↗
Exogenous Enzymes Influenced Eimeria-Induced Changes in Cecal Fermentation Profile and Gene Expression of Nutrient Transporters in Broiler Chickens
Two 21-day experiments were conducted to investigate the effects of exogenous enzymes on growth performance, tight junctions, and nutrient transporters, jejunal oligosaccharides and cecal short-chain fatty acids (SCFA) of broiler chickens challenged with mixed Eimeria. Two different basal diets: high fiber-adequate protein (HFAP; Expt. 1) or low fiber-low protein (LFLP; Expt. 2) were used in the two experiments. In each experiment, birds were allocated to four treatments in a 2 × 2 factorial arrangement (with or without protease and xylanase combination; with or without Eimeria challenge). In Expt. 1, with HFAP diets, Eimeria upregulated (p < 0.05) the expression of claudin-1, but downregulated (p < 0.05) glucose transporters GLUT2/GLUT5. On the contrary, enzymes downregulated (p < 0.05) claudin-1 and alleviated the Eimeria-depressed GLUT2/GLUT5 expression. In both experiments, Eimeria decreased (p < 0.05) cecal saccharolytic SCFA and increased (p < 0.05) cecal branched-chain fatty acids. The challenge × enzyme interaction (p < 0.05) showed that enzymes reversed the Eimeria effects on fermentation pattern shift. In conclusion, Eimeria altered tight junctions and nutrient transporters expression promoted cecal proteolytic fermentation and inhibited saccharolytic fermentation. Exogenous enzymes showed the potential of alleviating the Eimeria-induced intestinal gene expression changes and reversing the unfavorable cecal fermentation pattern.
59 BASIC BIOLOGICAL SCIENCES↗
Optimal PV Inverter Control in Distribution Systems via Data-Driven Distributionally Robust Optimization
Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range.
Xue, Yaosuo↗