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DOE OSTI2016

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.

42 ENGINEERING↗

DOE OSTI2020

Developing Scaling Relationships for Molecular Electrocatalysis through Studies of Fe-Porphyrin-Catalyzed O 2 Reduction

The oxygen reduction reaction (ORR) is a multiproton/multielectron transformation in which dioxygen (O 2 ) is reduced to water or hydrogen peroxide and serves as the cathode reaction in most fuel cells. The ORR (O 2 + 4e – + 4H + → 2H 2 O) involves up to nine substrates and thus requires navigating a complicated reaction landscape, typically with several high-energy intermediates. Many catalysts can perform this reaction, though few operate with fast rates and at low overpotentials (close to the thermodynamic potential). Attempts to optimize these parameters, both in homogeneous and heterogeneous electrocatalytic systems, have focused on modifying catalyst design and understanding kinetic/thermodynamic relationships between catalytic intermediates. One such method for analyzing and predicting catalyst reactivity and efficiency has been the development of “molecular scaling relationships”. In this work, we share our experience deriving and utilizing molecular scaling relationships for soluble, iron-porphyrin-catalyzed O 2 reduction in organic solvents. These relationships correlate turnover frequencies (TOF max ) and effective overpotentials (η eff ), properties uniquely defined for homogeneous catalysts. Following a general introduction of scaling relationships for both homogeneous and heterogeneous electrocatalysis, we describe the components of such scaling relationships: (i) the overall thermochemistry of the reaction and (ii) the rate and rate law of the catalyzed reaction. We then show how connecting these thermodynamic and kinetic parameters reveals multiple molecular scaling relationships for iron-porphyrin-catalyzed O 2 reduction. For example, the log(TOF max ) responds steeply to changes in ηeff that result from different catalyst reduction potentials (18.5 decades in TOF max /V in η eff ) but much less dramatically to changes in η eff that arise from varying the pKa of the acid buffer (5.1 decades in TOF max /V in η eff ). Thus, a single scaling relationship is not always sufficient for describing molecular electrocatalysis. This is particularly evident when the catalyst identity and reaction conditions are coupled. Using these multiple scaling relationships, we demonstrate that the metrics of turnover frequency and effective overpotential can be predictably tuned to achieve faster rates at lowered overpotentials. This Account uses a collection of related stories describing our research on soluble iron-porphyrin-catalyzed ORR to show how molecular scaling relationships can be derived and used for any electrocatalytic reaction. Such scaling relationships are powerful tools that connect the thermochemistry, mechanism, and rate law for a catalytic system. We hope that this collection shows the utility and simplicity of the molecular scaling approach for understanding catalysis, for enabling direct comparisons between catalyst systems, and for optimizing catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Modular Pd/Zeolite Composites Demonstrating the Key Role of Support Hydrophobic/Hydrophilic Character in Methane Catalytic Combustion

Complete catalytic oxidation of methane in the presence of steam at low temperatures (T < 400 °C) is a crucial reaction for emission control, yet it presents profound challenges. The activation of the strong C–H bond of methane at low temperature is difficult, and the water present in any realistic application poisons the active surface and promotes sintering of Pd particles during the reaction. Finding materials that can deliver high reaction rates while being more resistant to the presence of water is imperative for advancing several technological applications of natural gas-based systems. However, methods to fairly compare the activity of Pd catalysts (the most active metal for methane combustion) are needed in order to perform useful structure–property relationship studies. Here in this paper, we report a method to study how zeolite hydrophobicity affects the activity of Pd nanoparticles in the reaction, which led to a significant improvement in the water resistance. Mesoporous zeolites were synthesized starting from commercially available microporous zeolites. In this way, a variety of hierarchically porous zeolites, with different hydrophobic/hydrophilic character, were prepared. Preformed colloidal Pd nanoparticles could be deposited within mesostructured zeolites. This approach enabled the systematic study of key parameters such as zeolite framework, Al content, and the Pd loading while maintaining the same Pd particle size and structure for all the samples. Detailed catalytic studies revealed an optimum hydrophobic/hydrophilic character, and a promising steam-resistant catalyst, namely, 3.2 nm Pd particles supported on mesoporous zeolite beta or USY with a Si/Al ratio of 40, emerged from this multiparametric study with a T 50 of 355 °C and T 90 of 375 °C (where T 50 and T 90 are temperature values at which the samples reach 50% and 90% methane conversion, respectively) in steam-containing reaction conditions. Finally, we verified that the designed catalysts were stable by in-depth postcatalysis characterization and operando diffuse-reflectance infrared Fourier-transform spectroscopy (DRIFTS) analyses confirming that water adsorbs less strongly on the active PdO surface due to interaction with the zeolite acid sites. This method can be of general use to study how zeolite supports affect the reactivity of supported metals in several catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Perovskite ink with wide processing window for scalable high-efficiency solar cells

Perovskite solar cells have made tremendous progress using laboratory-scale spin-coating methods in the past few years owing to advances in controls of perovskite film deposition. However, devices made via scalable methods are still lagging behind state-of-the-art spin-coated devices because of the complicated nature of perovskite crystallization from a precursor state. Here we demonstrate a chlorine-containing methylammonium lead iodide precursor formulation along with solvent tuning to enable a wide precursor-processing window (up to ~8 min) and a rapid grain growth rate (as short as ~1 min). Coupled with antisolvent extraction, this precursor ink delivers high-quality perovskite films with large-scale uniformity. The ink can be used by both spin-coating and blade-coating methods with indistinguishable film morphology and device performance. Using a blade-coated absorber, devices with 0.12-cm 2 and 1.2-cm 2 areas yield average efficiencies of 18.55% and 17.33%, respectively. As a result, we further demonstrate a 12.6-cm 2 four-cell module (88% geometric fill factor) with 13.3% stabilized active-area efficiency output.

14 SOLAR ENERGY↗

DOE OSTI2025

High average-flux laser-driven neutron source

Laser-driven neutron generation is an attractive alternative to more established methods for compact, short-pulse-duration neutron sources with applications in medical science, material science and imaging. Despite extensive investigation of various techniques, achieving performance comparable to nuclear reactors or conventional accelerators remains challenging. In this work, we generate a stable, high-repetition-rate laser-driven neutron source reaching a record average flux of 7.8 × 10 7 n/sr/s, improving on other existing laser-based sources by more than one order of magnitude. Our approach is based on a two-step process where electrons are accelerated to relativistic energies via laser wakefield acceleration (LWFA), and subsequently generate neutrons through Bremsstrahlung emission followed by photonuclear reactions in a tungsten converter. Experimental results, supported by Monte Carlo simulations, show a neutron flux of 3.0 × 10 7 n/cm 2 /s near the target, on par with some compact accelerator-based neutron sources. Additionally, a direct comparison with the target-normal sheath acceleration (TNSA) pitcher-catcher scheme, performed on the same laser system, reveals a significantly higher total neutron yield of 3.9 × 10 8 neutrons per shot, outperforming the TNSA scheme by several orders of magnitude. These findings represent a significant advancement towards the development of practical laser-driven neutron sources and highlight the advantages of LWFA-based neutron generation for future applications.

Vallières, Simon [Institut National de la Recherch↗

DOE OSTI2020

The completed SDSS-IV extended baryon oscillation spectroscopic survey: geometry and growth from the anisotropic void–galaxy correlation function in the luminous red galaxy sample

ABSTRACT We present an analysis of the anisotropic redshift-space void–galaxy correlation in configuration space using the Sloan Digital Sky Survey extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 luminous red galaxy (LRG) sample. This sample consists of LRGs between redshifts 0.6 and 1.0, combined with the high redshift z > 0.6 tail of the Baryon Oscillation Spectroscopic Survey Data Release 12 CMASS sample. We use a reconstruction method to undo redshift-space distortion (RSD) effects from the galaxy field before applying a watershed void-finding algorithm to remove bias from the void selection. We then perform a joint fit to the multipole moments of the correlation function for the growth rate fσ8 and the geometrical distance ratio DM/DH, finding $f\sigma _8(z_\rm {eff})=0.356\pm 0.079$ and $D_M/D_H(z_\rm {eff})=0.868\pm 0.017$ at the effective redshift $z_\rm {eff}=0.69$ of the sample. The posterior parameter degeneracies are orthogonal to those from galaxy clustering analyses applied to the same data, and the constraint achieved on DM/DH is significantly tighter. In combination with the consensus galaxy BAO and full-shape analyses of the same sample, we obtain fσ8 = 0.447 ± 0.039, DM/rd = 17.48 ± 0.23, and DH/rd = 20.10 ± 0.34. These values are in good agreement with the ΛCDM model predictions and represent reductions in the uncertainties of $13{{\ \rm per\ cent}}$, $23{{\ \rm per\ cent}}$, and $28{{\ \rm per\ cent}}$, respectively, compared to the combined results from galaxy clustering, or an overall reduction of 55 per cent in the allowed volume of parameter space.

Nadathur, Seshadri↗

DOE OSTI2022

Experimental Measurement of Out-of-Time-Ordered Correlators at Finite Temperature

Out-of-time-ordered correlators (OTOCs) are a key observable in a wide range of interconnected fields including many-body physics, quantum information science, and quantum gravity. Measuring OTOCs using near-term quantum simulators will extend our ability to explore fundamental aspects of these fields and the subtle connections between them. Here, we demonstrate an experimental method to measure OTOCs at finite temperatures and use the method to study their temperature dependence. These measurements are performed on a digital quantum computer running a simulation of the transverse field Ising model. Our flexible method, based on the creation of a thermofield double state, can be extended to other models and enables us to probe the OTOC’s temperature-dependent decay rate. Measuring this decay rate opens up the possibility of testing the fundamental temperature-dependent bounds on quantum information scrambling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Analysis of Refrigeration Units in Off Grid PV/Wind Hybrid Systems on Tribal Land

This paper looks at the performance of residential refrigeration units in off-grid photovoltaic and small wind hybrid (PV/wind) systems with battery storage. Off grid hybrid power systems, dispatched by Navajo Tribal Utility Authority (NTUA), allow Navajo Nation residents in rural areas to have the benefits of electricity. On the Navajo Nation, the rural and rugged nature of the land results in a high capital cost to connecting a house to the grid. In this case, a hybrid PV/wind power generation system may be used at a much lower cost to the customer. In providing affordable power systems to customers, the systems are designed to provide adequate power for use. This requires a constant conservative use of electricity. For systems with refrigerators which are large alternating current (AC) loads, there are performance issues that arise. NTUA reports that new energy star rated refrigerators have performance issues in working with these off grid systems. This paper looks at ways to improve the performance of these systems with the use of a refrigeration unit. This involves looking at conventional refrigerators with its operation. This paper also looks at alternative methods of refrigeration. Also, the power system is looked at to increase the performance in maintaining a proper balance of the system. Lastly, solutions will be recommended to solve the refrigeration issues faced by NTUA.

14 SOLAR ENERGY↗

DOE OSTI2019

Thermodynamics and Kinetics of Phase Transformations in Energy Materials (Final Report)

The primary goal of this program is to develop computational tools and methodology for enabling a shift from empirical trial-and-error driven materials discovery towards data and information technology driven design of materials with targeted functional properties. Both thermodynamic and kinetic factors need to be taken into account when designing new materials. Thermodynamics sets the necessary-but-not-sufficient conditions for the suitability of a particular material in a given application, while the kinetics controls the rates of microscopic processes and hence the performance in energy applications (e.g., power in energy storage, conversion efficiencies in solar cells and thermoelectrics, chemical activity in catalysis). The complexity of these phenomena makes computational design difficult, not least due to the lack of suitable theoretical methods and computational tools. To address these needs, we develop systematic, quantitatively accurate first-principles methods to address two important problems: (i) predict and optimize the thermodynamics of materials over a large composition and structure space, and (ii) model kinetic processes such as mass and heat transport in crystalline solids under non-equilibrium conditions. These tasks are addressed by combining high-throughput computation with automatic construction of accurate lattice models using mathematically rigorous methods of information theory, such as compressive sensing and Bayesian inference.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Modeling of Thermal Decomposition of TATB-Based Explosive for Safety Analysis

We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.

Chemistry - Chemical explosives↗

DOE OSTI2022

Fault Characterization and Diagnostics Supporting Condition-Based Operation and Maintenance of Gas Turbine Engines

Condition-Based Operation and Maintenance (CBOM) is the state-of-the-art in maintenance approaches for gas turbine engines. CBOM applies engine sensor information to the optimization of future operation and maintenance procedures; this technique reduces costs for engine operators by minimizing unplanned outages and catastrophic engine degradation. However, due to the complexities of gas turbine operation and the lack of available engine sensors, further development is required to fully realize the benefits of CBOM. In the turbine section, rotating components interact with high temperature flows, which creates intense thermal and mechanical stresses. As a result, there are numerous mechanisms of component degradation in the turbine section. Furthermore, turbine components – like stator vanes and rotor blades – are among the most expensive in the engine because they are complex to design and manufacture. For these reasons, this dissertation addresses two main questions: (i) which parameters or faults within the turbine section are most important to monitor, and (ii) how can these parameters or faults be monitored in an engine-relevant environment? Although many turbine parameters and faults have been investigated in the open literature, there are some faults that are still not well understood. Rotor-casing eccentricity, which causes a non-constant blade tip clearance around the annulus, has not been investigated in terms of its effects on turbine efficiency. Therefore, the first study in this dissertation quantifies overall and local turbine efficiency for varying levels of rotor-casing eccentricity. Results showed negligible variations to overall turbine efficiency, meaning rotor-casing eccentricity only becomes relevant to CBOM when its severity causes rotordynamic issues. Purge flow is critical to turbine hardware longevity because it prevents ingestion of hot main gas path (MGP) flow into the under-platform regions. Despite its importance, there are currently no methods for monitoring purge flow performance in an engine environment. Therefore, the second study in this dissertation develops a predictive model for sealing effectiveness using inputs from two fast-response pressure sensors. Results exhibited low prediction errors across a full range of purge flow rates, which supports the viability of the modelling approach for CBOM. The final two studies in this dissertation address blade coolant flow monitoring. This cooling flow is responsible for protecting the turbine blades from the MGP flow, which exits the combustor at temperatures greater than the blade melting point. These studies showed that temperature measurements on the blade surface can be used to accurately predict blade coolant flow rate, and that defining the candidate features relative to the coolant trajectory is important for maintaining accuracy as coolant flow rate degradation occurs. This work enables blade coolant flow monitoring, which is currently not possible through existing condition monitoring techniques.

condition-based, gas turbines, diagnostics,↗

DOE OSTI2021

CTRAMER: An open-source software package for correlating interfacial charge transfer rate constants with donor/acceptor geometries in organic photovoltaic materials

In this paper, we present CTRAMER (Charge-Transfer RAtes from Molecular dynamics, Electronic structure, and Rate theory)—an opensource software package for calculating interfacial charge-transfer (CT) rate constants in organic photovoltaic (OPV) materials based on ab initio calculations and molecular dynamics simulations. The software is based on identifying representative donor/acceptor geometries within interfacial structures obtained from molecular dynamics simulation of donor/acceptor blends and calculating the corresponding Fermi’s golden rule CT rate constants within the framework of the linearized-semiclassical approximation. While the methods used are well established, the integration of these state-of-the-art tools originating from different disciplines to study photoinduced CT processes with explicit treatment of the environment, in our opinion, makes this package unique and innovative. The software also provides tools for investigating other observables of interest. After outlining the features and implementation details, the usage and performance of the software are demonstrated with results from an example OPV system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

In this paper, we report on disruption prediction using a shallow machinelearning method known as Random Forests, trained on large databases containingonly plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~10 6 times throughout ~10 4 discharges (disruptive and non-disruptive) over the last 4years of operation. It is found that a number of parameters (e.g.P rad /P input ,l i ,n/n G ,B n=1 /B 0 ) exhibit changes in aggregate as a disruption is approached on one ormore of these tokamaks. However, for each machine, the most useful parameters,as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices "close to disruption" and "far from disruption," it is found that the prediction algorithms differ substantially in performance among the three machines ona time slice-by-time slice basis, but have similar disruption detection rates (~80-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which developmentof a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing inputsignals, which may have implications for avoiding disruptive scenarios. Lastly, to further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training

Abstract Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and $$20\%$$ of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costly physical models. However, these model-free DRL approaches often suffer from long training time to reach a good performance, which is a major obstacle for their practical deployment. In this work, we present a systematic approach to accelerate online reinforcement learning for HVAC control by taking full advantage of the knowledge from domain experts in various forms . Specifically, the algorithm stages include learning expert functions from existing abstract physical models and from historical data via offline reinforcement learning, integrating the expert functions with rule-based guidelines, conducting training guided by the integrated expert function and performing policy initialization from distilled expert function. Moreover, to ensure that the learned DRL-based HVAC controller can effectively keep room temperature within the comfortable range for occupants, we design a runtime shielding framework to reduce the temperature violation rate and incorporate the learned controller into it. Experimental results demonstrate up to 8.8 X speedup in DRL training from our approach over previous methods, with low temperature violation rate.

Xu, Shichao↗

DOE OSTI2017

FY2016 ILAW Glass Corrosion Testing with the Single-Pass Flow-Through Method

The inventory of immobilized low-activity waste (ILAW) produced at the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will be disposed of at the near-surface, on-site Integrated Disposal Facility (IDF). When groundwater comes into contact with the waste form, the glass will corrode and radionuclides will be released into the near-field environment. Because the release of the radionuclides is dependent on the dissolution rate of the glass, it is important that the performance assessment (PA) model accounts for the dissolution rate of the glass as a function of various chemical conditions. To accomplish this, an IDF PA model based on Transition State Theory (TST) can be employed. The model is able to account for changes in temperature, exposed surface area, and pH of the contacting solution as well as the effect of silicon concentrations in solution, specifically the activity of orthosilicic acid (H4SiO4), whose concentration is directly linked to the glass dissolution rate. In addition, the IDF PA model accounts for the alkali-ion exchange process as sodium is leached from the glass and into solution. The effect of temperature, pH, H4SiO4 activity, and the rate of ion-exchange can be parameterized and implemented directly into the PA rate law model. The rate law parameters are derived from laboratory tests with the single-pass flow-through (SPFT) method. To date, rate law parameters have been determined for seven ILAW glass compositions, thus additional rate law parameters on a wider range of compositions will supplement the existing body of data for PA maintenance activities. The data provided in this report can be used by ILAW glass scientists to further the understanding of ILAW glass behavior, by IDF PA modelers to use the rate law parameters in PA modeling efforts, and by Department of Energy (DOE) contractors and decision makers as they assess the IDF PA program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An In Situ , multi-electrode electrochemical method to assess the open circuit potential corrosion of Cr in unpurified molten FLiNaK

An in situ electrochemical method to investigate the time-dependent spontaneous corrosion of pure Cr in an unpurified LiF-NaF-KF eutectic salt at 600 °C was developed. A multi-electrode electrochemical cell and a dual-electrode method were utilized to detect Cr(II) and Cr(III) ions on a platinum working electrode using cyclic voltammetry as a function of exposure time and compensating for the distance from the Cr electrode. XRD was performed to characterize the crystalline composition of the exposed FLiNaK and the salt films formed on the working electrodes. The fate of all electro-oxidized Cr must be accounted for to quantify corrosion rates. The concentrations of Cr(II) and Cr(III) species obtained were compared with the gravimetric mass change and ICP-OES analysis of the residual salt. Finally, outcomes, difficulties, and limitations are discussed.

ICP-OES↗

DOE OSTI2017

Carbon nanotube supported PdAg nanoparticles for electrocatalytic oxidation of glycerol in anion exchange membrane fuel cells

Electro-oxidation of alcohol is the key reaction occurring at the anode of a direct alcohol fuel cell (DAFC), in which both reaction kinetics (rate) and selectivity (to deep oxidation products) need improvement to obtain higher power density and fuel utilization for a more efficient DAFC. We recently found that a PdAg bimetallic nanoparticle catalyst is more efficient than Pd for alcohol oxidation: Pd can facilitate deprotonation of alcohol in a base electrolyte, while Ag can promote intermediate aldehyde oxidation and cleavage of C-single bondC bond of C 3 species to C 2 species. Furthermore, a combination of the two active sites (Pd and Ag) with two different functions, can simultaneously improve the reaction rates and deeper oxidation products of alcohols. In this continuing work, Pd, Ag mono, and bimetallic nanoparticles supported on carbon nanotubes (Ag/CNT, Pd/CNT, Pd 1 Ag 1 /CNT, and Pd 1 Ag 3 /CNT) were prepared using an aqueous-phase reduction method; they served as working catalysts for studying electrocatalytic oxidation of glycerol in an anion-exchange membrane-based direct glycerol fuel cell. Combined XRD, TEM, and HAADF-STEM analyses performed to fully characterize as-prepared catalysts suggested that they have small particle sizes: 2.0 nm for Pd/CNT, 2.3 nm for PdAg/CNT, 2.4 nm for PdAg 3 /CNT, and 13.9 nm for Ag/CNT. XPS further shows that alloying with Ag results in more metal state Pd presented on the surface, and this may be related to their higher direct glycerol fuel cell (DGFC) performances. Single DGFC performance and product analysis results show that PdAg bimetallic nanoparticles can not only improve the glycerol reaction rate so that higher power output can be achieved, but also facilitate deep oxidation of glycerol so that a higher faradaic efficiency and fuel utilization can be achieved along with optimal reaction conditions (increased base-to-fuel ratio). Half-cell electrocatalytic activity measurement and single fuel cell product analysis of different glycerol oxidation intermediates, including C 3 : glycerate, tartronate, mesoxalate, and lactate; C 2 : glycolate and oxalate, over PdAg/CNT catalyst was further conducted and produced deeper insight into the synergistic effects and reaction pathways of bimetallic PdAg catalysts in glycerol electrocatalytic oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data

This report presents the results from the 2016 ABRF Proteome Informatics Research Group (iPRG) study on proteoform inference and FDR estimation from bottom-up proteomics data. For this study, three replicate Q Exactive Orbitrap LC-MS/MS datasets were generated from each of four E. coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file, and asked to identify the proteins and provide estimates on the false discovery rate at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, though those who participated did well, with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique “ground-truth” dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.

Proteomics, proteoforms, inference, false discover↗