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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SuperHERO (Super - High Energy Replicated Optics) Balloon Mission Concept

The SuperHERO hard-x-ray telescope is a proposed high-angular resolution successor to Marshall Space Flight Center’s (MSFC) successful High Energy Replicated Optic (HERO) and the HERO to Explore the Sun (HEROES) balloon missions. By utilizing recent advancements in MSFC’s replicated full shell, NiCo x-ray optic technology, SuperHERO will achieve its technical goal of demonstrating better than 10 arcseconds (arcsecs) angular resolution in half-power diameter (HPD) on a balloon platform. This represents a 6-fold improvement to the current state-of-the-art resolution for hard-x-ray, astrophysical telescopes and will allow for the observation of the high-energy-universe in unprecedented spatial detail. The SuperHERO balloon observatory will consist of seven coaligned, identical telescopes with a total focal length of 12-meters. Each telescope will pair a Mirror Module Assembly (MMA) comprised of NiCo shell optics matched to a CdTe Double-sided Strip Detector (CdTe-DSD) plane provided by iMAGINE-X Inc. and Kavli Institute for the Physics and Mathematics of the Universe (Kavli-IPMU). Iridium coating applied directly to each shell will boost reflectivity into the hard-x-ray regime. Employing additional high-heritage technology, SuperHERO will have an open-truss carbon-composite gondola similarly used on the XL-Calibur balloon mission and will use the Wallops Arc-Second Pointer (WASP) system for attitude control

NickThomas↗
DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Development and Integration of a Stochastic Clad Damage Propagation Model into PRONGHORN-SC Subchannel Analysis Code

The failure of fuel pins in nuclear reactors is intrinsically stochastic. Typically, a combination of variation in manufacturing that affects the material characteristics and the fuel assembly dimensions, variation in operating conditions, such as local power, coolant flow rate, and irradiation induced changes in material properties lead to a large uncertainty in failure margin of the fuel pins. Failure, therefore, may occur in exceptional pins with adverse combinations of these variations. Upon a metal fuel pin (U-Pu-Zr/HT9) failure, depressurization of the fuel pin takes place by release of fission gas, liquid sodium bond, and potentially solid fuel particles or molten/eutectic fuel droplets through the hole in cladding. The effect of a fission gas jet on neighbor fuel pins and possible propagation of a clad damage during normal operation was studied experimentally in 1970s and it was found that the post-failure fission gas jet insulates the jet impingement area of the target fuel pin surface and could increase the target pin’s surface temperature by as much as 100 – 200 K during the failed pin depressurization. It was concluded that the effect should not lead to fuel pin failure propagation during normal operation. In accident scenarios of sodium and lead fast reactors such as Unprotected Loss-Of-Flow (ULOF) or Unprotected Transient Over Power (UTOP), the fuel pins can be subjected to higher clad temperatures and fuel pin pressures or fuel clad mechanical/chemical interaction where thermal creep margin becomes significantly lower compared to the normal operation conditions. Therefore, possible stochastic failure and the post-failure fission gas/fuel jet impingement could be critical in order to predict fuel pin failure propagation. Pin depressurization due to fission gas release may degrade the heat transfer by formation of a gas blanket on a neighboring pin surface, which is a local phenomenon, and by causing coolant flow deceleration and starvation, which could affect a surrounding region as well. Furthermore, the potential presence of solid fuel particles or molten fuel at the time of clad failure could boost post-failure jet induced degradation even further. The present study models the U-Pu-Zr/HT9 metal fuel pin failure and stochastic clad damage propagation by biased sampling based on a Cumulative Damage Fraction (CDF) type clad failure criterion and the normal distribution of fuel failure probability density as a function of logarithm of Cumulative Damage Fraction. In addition, the effect of post-failure fission gas jet on heat transfer degradation is modeled for the target pins. This model is called stochastic Clad Damage Propagation (CDAP). The CDAP model is now fully integrated into developmental version of PRONGHORN-SC subchannel analysis code, allowing for modeling local failures and its propagation potential. Section 2 describes the components of the CDAP models. Section 3 describes the model implementation to PRONGHORN-SC and input specifications. Section 4 describes the CDAP model validation coupled to PRONGHORN-SC.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A reversible perovskite air electrode for active and durable oxygen reduction and evolution reactions via the A-site entropy engineering

The performance of reversible solid oxide electrochemical cells (R-SOECs) is largely hindered by the insufficient electroactivity and poor durability of the bifunctional air electrodes, where the oxygen reduction and evolution reactions (ORR and OER) occur. Here, we report our findings in boosting the electrochemical activity and durability of an air electrode with Pr 0.2 Ba 0.2 Sr 0.2 La 0.2 Ca 0.2 CoO 3-δ (PBSLCC) via an A-site entropy engineering. The PBSLCC electrode shows enhanced oxygen reaction activity and excellent durability compared to binary and ternary double perovskites (PrBaCo 2 O 5+δ and Pr 0.8 Ba 0.8 Ca 0.4 Co 2 O 5+δ , respectively). A low and nearly unchanged area-specific resistance of 0.042Ωcm 2 is achieved at 750 °C during the 225-h stability test. La 0.9 Sr 0.1 Ga 0.8 Mg 0.2 O 3-δ electrolyte-supported cells with the PBSLCC air electrode show remarkable performance at 800 °C, demonstrating a peak power density of 1.2Wcm -2 in the fuel cell mode, and a current density of -1.1 Acm -2 at 1.3 V in the electrolysis mode while maintaining the excellent cycling durability of 228 h at ±0.5 Acm -2 under humidified H 2 (10% H 2 O). A bulk oxygen p-band center model is applied to verify that tailoring of the A-site entropy strongly influences the surface exchange coefficients (k* chem ), leading to higher oxygen reaction activity of PBSLCC than the binary and ternary perovskites. Finally, this study opens a new class of high-entropy perovskites for the rational design of air electrodes for R-SOECs with high activity and durability.

30 DIRECT ENERGY CONVERSION↗
NASA NTRSDate not supplied

Using Mesh Networking for A Dynamic Lunar Internet of Things (Liot)

The purpose of this project is to evaluate the feasibility of an IEEE 802.11 mesh protocol for lunar surface computing. This standard for wireless networking boosts speed, dependability and range of wireless transmissions. The concept is to integrate sensors (such as deployed science instruments) or Astronaut tools (such as a handheld spectrometer) that communicate with a node on a common cell. The nodes can extend the range of the cell and can dynamically reconfigure the data routing in case of another node failure. All of the data in a cell pass through a modem that communicates with a distant base station across a 4G link. The application of mesh networking to a potential lunar surface network increases robustness and fault tolerance over a traditional single-point modem system. By demonstrating the basic capability of a mesh network, the student team has learned about issues with power, distance, thermal, dust, radiation, data processing, and communication problems applicable to the lunar surface. This knowledge can feed into future NASA requirements to improve the capability of a LunaNET implementation for the Artemis program. This project follows 10 years of successful collaboration between NASA ARES, Texas Space, Technology, Applications and Research (T STAR) and Texas A&M University in a Public, Private, Academic (PPA) Partnership. NASA funds T STAR to mentor undergraduate Capstone teams in the College of Engineering Department to design, built, and test prototypes meeting NASA requirements. TAMU faculty lead the student teams in their academic class, and NASA Subject Matter Experts (SMEs) provide T STAR and students insight on requirements evolution, prior design projects, and future development goals.

Lunar Mesh Networking↗
DOE OSTI2024

In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination

Abstract Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on‐site, on‐the‐fly analysis needed for real‐time process monitoring. Here, a weighted lagged cross‐correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X‐ray diffraction metrology to monitor the calcination process of the archetypal nickel‐based cathode, LiNiO 2 . The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO 2 , providing information for agile on‐site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early‐stage phase nucleation and growth. Expanding this data‐powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Estimation of the Required Dipole Corrector Magnetic Field for PIP-II Injection Based on Beam Studies

The Fermilab Booster will accept a 600 µs beam pulse from the new superconducting Linac for PIP-II operations. The Booster is a rapid-cycling synchrotron that uses a resonant magnet circuit ramping at 15 Hz. For PIP-II, the cycle rate will increase to 20 Hz, and the injection pulse length will expand from 40 µs to 600 µs due to the lower output current from the new Linac. Because the Booste main bending field follows a sinusoidal waveform, the magnetic field is not constant during the extended injection window. The longer pulse length and higher repetition rate modify the beam orbit and can lead to increased beam losses. The Booster contains 48 dipole-corrector packages distributed across its 24 periods. Each package includes horizontal and vertical dipoles, quadrupole, sextupole, skew-quadrupole, and skew-sextupole elements. By driving the dipole correctors with an appropriately shaped sinusoidal waveform during injection, we can compensate the changing main field and create an effectively flat bending field—referred to as flat injection. Over the past several years, machine studies have been performed to characterize the required correction fields and to determine the corresponding power-supply specifications needed for PIP-II operation. In this presentation, we will summarize the study results and discuss the estimated magnetic field requirements and power supply parameters for achieving flat injection in the Booster.

Seiya, K. [Fermilab] (ORCID:0000000250576943)↗
DOE OSTI2022

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Electronic tuning of confined sub-nanometer cobalt oxide clusters boosting oxygen catalysis and rechargeable Zn–air batteries.

Reasonable design of robust bifunctional oxygen catalysts from an electronic structure perspective is intriguing and challenging for the development of high active rechargeable zinc-air batteries (ZABs). In this study, the favorable regulation of the electronic structure of the cobalt oxide nanoclusters was firstly predicted by density functional theory (DFT) simulation, and then experimentally verified by confining sub-nanometer CoOx clusters (0.86 nm) into the small pore of ZIF-8 derived N-doped nanomaterials (PNC) using a microporous MOFs confinement strategy. The confined effect of the MOF micropores not only enhanced the stability of the subnanometer cobalt oxide clusters, but also make it coupled with Co-Nx to further regulate the electronic structure of the former, synergistic resulting in enhanced ORR/OER actives. As a result, the optimized 0.05CoOx@PNC catalyst demonstrates outstanding bifunctional oxygen performance with a smaller potential gap of 0.67 V. Moreover, the rechargeable Zn-air batteries integrated 0.05CoOx@PNC air cathode displays encouraging performance with a peak power density of 157.1 mW cm(-2), a specific capacity of 887 mAh g(Zn)(-1)at 10 mA cm(-2) and long-term cyclability for over 200 h, significantly outperforming the benchmark electrode couple consisted of Pt/C/RuO2. DFT calculation further revealed that reducing particle size and coupling with Co-N could effectively regulate the charge distribution of CoOx nanoclusters and downshift the D-band center of Co adsorption sites in CoOx nanoclusters, which reduced the reaction barrier of intermediate O-2* and OH* and ORR/ OER over potential, thus accelerating the overall ORR/OER kinetic process. This work offers a novel reference for the construction of a robust sub-nanometer cluster catalysts in the field of ZABs.

Bifunctional oxygen electrocatalysts↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Software With Strong Ties to Space

TieFlow is a simple but powerful business process improvement solution. It can automate and simplify any generic or industry-specific work process, helping organizations to transform work inefficiencies and internal operations involving people, paper, and procedures into a streamlined, well-organized, electronicbased process. TieFlow increases business productivity by improving process cycle times. The software can expedite generic processes in the areas of product design and development, purchase orders, expense reports, benefits enrollment, budgeting, hiring, and sales. It can also shore up vertical market processes such as claims processing, loan application and processing, health care administration, contract management, and advertising agency traffic. The processes can be easily and rapidly captured in a graphical manner and enforced together with rules pertaining to assignments that need to be performed. Aside from boosting productivity, TieFlow also reduces organizational costs and errors. TieFlow was developed with Small Business Innovation Research (SBIR) assistance from Johnson. The SBIR support entitles all Federal Government agencies to utilize the TieFlow software technology free of charge. Tietronix emphasizes that TieFlow is an outstanding workflow resource that could produce dramatic productivity and cost improvements for all agencies, just as it has done and continues to do for NASA. The Space Agency is currently using the software throughout several mission-critical offices, including the Mission Operations Directorate and the Flight Director s Office, for worldwide participation of authorized users in NASA processes. At the Flight Director s Office, TieFlow allows personnel to electronically submit and review changes to the flight rules carried out during missions.

Source record↗
DOE OSTI2024

A Workflow to Optimize Fast Neutron Irradiation in A Thermal Neutron Spectrum Test Reactor Leveraging Open-Source Tools

The Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) is one of the key nuclear engineering research and testing facilities within the US Department of Energy (DOE). The ATR is one of few high-power research reactors in the world with different application including accelerated testing of nuclear fuel, materials irradiation in a very high neutron flux environment, and medical radioisotope production [1]. Also, the ATR offers opportunities for testing fast spectrum fission and fusion reactor materials. The key challenges in this area are in further detailing and optimizing a fast spectrum environment within a thermal test reactor. This challenge involves researching, developing, and testing novel concepts for the multiplying of neutron populations into ever higher energy spectra in high flux test reactors like ATR. The main objective of this work is to investigate candidate materials for establishing a fast neutron experiment irradiation in thermal neutron spectrum test reactors which can be accomplished by filtering thermal and epithermal neutrons and boosting fast neutrons at designated irradiation positions. However, adding these filters will render the neutron spectrum and the criticality of the system. The selection of the thickness and material layers should be accomplished by developing an optimization design algorithm that is applicable for ATR to enhance the fast neutron spectrum irradiation utilizing high-fidelity Monte Carlo methods along with advanced machine learning capabilities. This paper presents workflow for design optimization to enhance fast neutron irradiation in the ATR. The workflow leverages open-source tools to develop an algorithm that is viable to ATR and can be leveraged in other reactors. The following sections discuss the development of the experiment design optimization workflow and its application to ATR irradiation positions.

42 - ENGINEERING↗
DOE OSTI2020

Structure, Morphology, and Photovoltaic Implications of Halide Alloying in Lead-Free Cs 3 Sb 2 Cl x I 9-x 2D-Layered Perovskites

Compositional tuning has been a major driving force behind the excellent optoelectronic properties observed in typical Pb-based perovskites. For lead-free perovskite derivatives, a challenge to understanding the connection between compositional tuning and intrinsic optoelectronic properties, hence a barrier towards boosting their performance, comes from the fact that multiple crystalline substructures can form based on composition, film processing, or both. Especially with lower dimensional (0D, 1D, 2D) substructures, the particular polymorph present in the film can be a greater determinant of optoelectronic properties than the composition itself. Herein, we report a simple method to alloy the halide site in all-inorganic lead-free Cs 3 Sb 2 I 9 films while maintaining a consistent 2D-layered substructure, as a means to independently study the photovoltaic implications of halide substitution. We use a broad suite of spectroscopy and device measurements to identify an optimal stoichiometric substitution of chloride for iodide (~ 8 mol%, measured) that balances both intrinsic and bulk optoelectronic properties to achieve a top power conversion efficiency of 2.2%. This work underscores the importance of controlling substructure while investigating the impacts of compositional tuning for the development of lead-free perovskites, and more broadly validates the approach towards realizing lead-free alternative perovskite solar technologies.

14 SOLAR ENERGY↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2013

Summary of the 2012 Inductive Pulsed Plasma Thruster Development and Testing Program

Inductive pulsed plasma thrusters are spacecraft propulsion devices in which energy is capacitively stored and then discharged through an inductive coil. While these devices have shown promise for operation at high efficiency on a range of propellants, many technical issues remain before they can be used in flight applications. A conical theta-pinch thruster geometry was fabricated and tested to investigate potential improvements in propellant utilization relative to more common, flat-plate planar coil designs. A capacitor charging system is used to permit repetitive discharging of thrusters at multiple cycles per second, with successful testing accomplished at a repetition-rate of 5 Hz at power levels of 0.9, 1.6, and 2.5 kW. The conical theta-pinch thruster geometry was tested at cone angles of 20deg, 38deg, and 60deg, with single-pulse operation at 500 J/pulse and repetitionrate operation with the 38deg model quantified through direct thrust measurement using a hanging pendulum thrust stand. A long-lifetime valve was designed and fabricated, and initial testing was performed to measure the valve response and quantify the leak rate at beginning-of-life. Subscale design and testing of a capacitor charging system required for operation on a spacecraft is reported, providing insights into the types of components needed in the circuit topology employed. On a spacecraft, this system would accept as input a lower voltage from the spacecraft DC bus and boost the output to the high voltage required to charge the capacitors of the thruster.

Polzin, K. A.↗
DOE OSTI2022

Siloxane-functional small molecule acceptor for high-performance organic solar cells with 16.6% efficiency

As one of the simple but most effective molecular design strategies, side-chain engineering has been widely employed to modify the photoelectric properties of active layer materials for boosting the photovoltaic performance of organic solar cells (OSCs). In this work, a functionalized small molecule acceptor (SMA) named BTSi-4F with a bulky siloxane-terminated solubilizing group as side-chains, derived from a classical SMA of Y6, was designed and synthesized. The results demonstrate that the introduction of siloxane-functional terminated groups into SMA not only affects the optical absorption and molecular energy levels, but also regulates the miscibility between the polymer donor and SMA. Compared to the original Y6, BTSi-4F exhibits a better solubility, upshifted lowest unoccupied molecular orbital (LUMO) energy level, more ordered molecular packing, and higher electron-mobility. Matched with a wide bandgap polymer donor PM6, the chlorobenzene-processed OSCs based on PM6:BTSi-4F achieved a superior power conversion efficiency (PCE) of 16.6% with both high open-circuit voltage (V oc ) of 0.90 V and high fill factor (FF) of 0.77, while the devices based on PM6:Y6 obtained a much lower PCE of 13.0% with a V oc of 0.81 V and FF of 0.69 under the same conditions. This work offers a promising molecular design strategy of siloxane-terminated side chain engineering to develop high-performance SMAs for efficient OSCs.

14 SOLAR ENERGY↗
DOE OSTI2021

Constraining the baryon abundance with the kinematic Sunyaev-Zel’dovich effect: Projected-field detection using Planck , WMAP , and unWISE

The kinematic Sunyaev-Zel'dovich (kSZ) effect -- the Doppler boosting of cosmic microwave background (CMB) photons scattering off free electrons with non-zero line-of-sight velocity -- is an excellent probe of the distribution of baryons in the Universe. In this paper, we measure the kSZ effect due to ionized gas traced by infrared-selected galaxies from the \emph{unWISE} catalog. We employ the "projected-field" kSZ estimator, which does not require spectroscopic galaxy redshifts. To suppress non-kSZ foreground signals associated with the galaxies (e.g., dust emission and thermal SZ), this estimator requires cleaned CMB maps, which we obtain from \emph{Planck} and \emph{WMAP} data. Using a new "asymmetric" estimator that combines different foreground-cleaned CMB maps to maximize the signal-to-noise, we measure the kSZ$^2$-galaxy cross-power spectrum for three subsamples of the \emph{unWISE} galaxy catalog, which peak at mean redshifts $z \approx$ 0.6, 1.1, and 1.5, have average halo mass $\sim 1$-$5\times 10^{13}$ $h^{-1} M_{\odot}$, and in total contain over 500 million galaxies. After marginalizing over CMB lensing contributions, we measure the amplitude of the kSZ signal $A_{\rm kSZ^2} = 0.42 \pm 0.31$, $5.02 \pm 1.01$, and $8.23 \pm 3.23$, for the three subsamples, where $A_{\rm kSZ^2} = 1$ corresponds to our fiducial model. The combined kSZ detection S/N $>$ 5. We discuss possible explanations for the excess kSZ signal associated with the $z \approx 1.1$ sample, and show that foreground contamination in the CMB maps is very unlikely to be the cause. Our measurements illustrate clearly that no baryons are missing on large scales at low redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

pyCRTM: A Python Interface for the Community Radiative Transfer Model

The Community Radiative Transfer Model (CRTM) is a powerful and versatile scalar radiative transfer model for satellite data assimilation and remote sensing applications. It is implemented as an object-oriented Fortran library, enabling flexible code development and optimal runtime performance on clusters. The downsides of the Fortran interface are a steep learning curve for students and the reduced productivity of users that is typical for static compiled languages, in contrast to dynamic interpreted languages like Python. pyCRTM is a new software framework that directly interfaces the CRTM Fortran data structures and procedures in Python, leveraging both the simplicity and ease of use of Python syntax as well as the flexibility arising from the vast contemporary Python ecosystem. The goal of pyCRTM is to lower the barrier of entry for university students to learn and use the CRTM and to boost the productivity of researchers seeking to create new methods in radiative transfer and data assimilation, or seeking to apply the CRTM to study atmospheric phenomena without having to go through the pre-existing complexity of the CRTM Fortran interface.

Python↗