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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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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Studies on Automated Manufacturing of High Performance Composites

The NASA Langley Research Center fiber placement facility has proven to be a valuable asset for obtaining data, experience, and insights into the automated fabrication of high performance composites. The facility consists of two automated devices: an Asea Brown Boveri (ABB) robotic arm with a modified heated head capable of hot gas and focused infrared heating and a 7' x 17' gantry containing a feeder head, rotating platform, focused infrared lamp and e-beam gun. While uncured thermoset tow and tape, e.g., epoxy and cyanate prepreg, can be placed with a robot, the placement facility s most powerful attribute is the ability to place thermoplastic and e-beam curable material to net shape. In recent years, ribbonizing techniques have been developed to make high quality thermoplastic and thermoset dry material forms to the standards required for robotic placement. A variety of composites have been fabricated from these ribbons by heated head tow and tape placement including both flat plates and cylinders. Composite mechanical property values of the former were between 85 and 100 percent of those obtained by hand lay-up/autoclave processing.

Cano, R. J.↗
NASA NTRS2014

Recent Advances in Water Analysis with Gas Chromatograph Mass Spectrometers

We report on progress made in developing a water sampling system for detection and analysis of volatile organic compounds in water with a gas chromatograph mass spectrometer (GCMS). Two approaches are described herein. The first approach uses a custom water pre-concentrator for performing trap and purge of VOCs from water. The second approach uses a custom micro-volume, split-splitless injector that is compatible with air and water. These water sampling systems will enable a single GC-based instrument to analyze air and water samples for VOC content. As reduced mass, volume, and power is crucial for long-duration, manned space-exploration, these water sampling systems will demonstrate the ability of a GCMS to monitor both air and water quality of the astronaut environment, thereby reducing the amount of required instrumentation for long duration habitation. Laboratory prototypes of these water sampling systems have been constructed and tested with a quadrupole ion trap mass spectrometer as well as a thermal conductivity detector. Presented herein are details of these water sampling system with preliminary test results.

quadrupole ion trap↗
NASA NTRS2019

Aerodynamic and Aerothermal Simulations of Mars Concept Vehicles Using Overset DPLR

NASA's Descent System Studies (DSS) Program is studying various concept vehicles to enable landing of heavy payloads on the surface of Mars. While it is desirable to run high-fidelity CFD simulations to accurately assess the aerodynamic and aerothermal effects of various design changes during EDL, it is usually difficult to quickly generate high-quality grids suitable for such analyses. One approach to address this bottleneck in mesh generation is through the use oversetting grids. Although the overset approach is efficient and powerful in solving partial differential equations on complex geometries, new users often find it challenging to apply overset concepts for their simulations. For example, generating hyperbolic grids with sufficient overlap; priority in hole-cutting on multiple overlapping grids; and fixes to assemble overlapping viscous grids at the body surface. The objective of this presentation is to introduce a simple process that combines the advantages of near-body, point-matched, structured grids with oversetting background grids suitable for grid alignment. This approach allows for grids that can be sequenced, reclustering of mesh spacing at the wall, and grid alignment with the bow shock. The current methodology is tested on a Mid-L/D configuration using the overset DPLR code.

Overset Grids↗
NASA NTRS2020

Miniature TOC Analyzer using Tunable Laser Spectroscopy and Combustion

Measurement of Total Organic Carbon (TOC) is an important tool on manned spaceflight missions for determining water quality. TOC quantification can be accomplished by oxidizing dissolved organic carbon to carbon dioxide (CO2) and analyzing the CO2 after volatilization. For human spaceflight applications, size, weight, and power need to be minimized while maintaining high sensitivity and accuracy. To this end, a tunable laser spectrometer tuned to measure CO2 was coupled to a miniature combustion reactor and the performance of the system for measuring the TOC was assessed. This combined system can be potentially reduced to around 1000 cm3 while offering the benefit afforded by combustion of converting all organics to CO2.

Winiberg, Frank↗
DOE OSTI2021

Computational Fermi Level Engineering and Doping-Type Conversion of Ga2O3 Via Three-Step Processing

Ga2O3 is being actively explored for power electronics, deep-ultraviolet optoelectronics, and other applications due to its ultra-wide bandgap and low projected fabrication cost of large-size and high-quality crystals. N-type doping of Ga2O3 can be achieved and tuned, but p-type doping faces fundamental obstacles due to deep character of acceptor levels and polaron transport of resulting holes. However, successful engineering of Ga2O3 based devices requires critical control of doping density, Fermi level position, and free carrier concentration, providing opportunities for predictive process simulation. We use first-principles defect theory and defect equilibrium calculations to simulate a 3-step growth-annealing-quench protocol for hydrogen assisted Mg doping in Ga2O3, taking into account the hydrogen-oxygen-water gas phase equilibrium. We predict type conversion to a net p-type regime following O-rich annealing after growth under reducing conditions in the presence of H2. This process is similar to the Mg acceptor activation by H removal in GaN. We show that there is an optimal temperature that maximizes the net acceptor density during the equilibrium annealing step for a given Mg doping level. Quenching of non-equilibrium annealed samples then results in a Fermi level EF below mid-gap down to about EV +1.5 eV, creating a significant number of uncompensated neutral MgGa0 acceptors. The resulting free hole concentration in quenched samples is very low (109 – 1012 cm-3) due to deep energy level of these Mg acceptors, but this type converted Ga2O3 material can create a significant built-in field in a p-n junction with an adjoining n-type material. Additionally, the electron concentration is greatly suppressed in such acceptor-doped Ga2O3, which could enable the use as a current blocking layer to fabricate normally-off (enhancement-mode) vertical Ga2O3 based metal-oxide-semiconductor field effect transistors (MOSFETs), and as a guard ring for edge termination in MOSFETs and Schottky barrier diodes (SBDs) with increased breakdown voltage.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,M↗
DOE OSTI2023

Status of HEU-Pb in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook

The Department of Energy invests tens of millions of dollars each year to develop the next generation of nuclear engineering modeling & simulation (M&S) tools. These tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers become more powerful, we are able to enhance resolution in our calculations. This improved resolution is taking us to a point where the limitations of simulation capability are in the quality of data, including our ability to quantify the uncertainty and sensitivity of the data. In order to model systems of interest with increasing accuracy, the industry must improve key nuclear data measurements. Thus, M&S tools need evaluated and quality-assured experimental data for validation purposes. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles and evaluates experiment data in a handbook that can be used by criticality safety engineers and others to validate computer codes and cross-section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. Figure 1 organizes all the benchmark evaluations that have been performed by the isotope of interest, in this case Pb, and the average neutron energy the system. Compared to other isotopes of interest for nuclear applications, there are few benchmark evaluations for Pb systems. The lack of integral measurements to determine errors in Pb cross-section data has caused the latest nuclear cross-section libraries to over/underestimate k eff compared to experimental results. Therefore, this evaluation fills an important knowledge gap in benchmark evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Value of Information and Data Management Platform Promoting Modern Data Analysis Utilization. STTR Phase I, Final Technical Report

The immense growth in weather and river sensor data availability along with increases in computational power and algorithmic sophistication have enabled the widespread application of physical, machine learning, and artificial intelligence algorithms to river forecasting. The forecasts produced by widely varying model approaches are inherently limited in different ways, such as by the number and quality of training observations, producing biased predictions that achieve optimal skill only over specific ranges of weather and river conditions. Thus, despite the growing availability and new sources of forecasts, hydropower owners cannot fully leverage the information because they lack an effective mechanism to determine the “best” forecast for given conditions or objectives. Tools are needed to consolidate forecast information, demonstrate skill, and extract optimal forecast guidance from the range of possibilities. Such tools will increase uptake and beneficial use of the growing wealth of forecast information, and lead to improved operating decisions, optimal power generation, and enhanced system value. This research proposes to improve the value of forecasts by developing a broadly accessible verification framework and applying machine learning techniques to identify the ‘best’ forecast from an ensemble of sources given the current weather, river, and operating conditions.

13 HYDRO ENERGY↗
DOE OSTI2021

Impact of Non-Steady State Operation on Cooling Water Consumption at Coal- and Natural Gas-Fired Power Plants

Increased renewable energy penetration in the electricity grid in coming decades will result in more frequent cycling at thermal power plants. Simultaneously, thermal power plants may face water scarcity with declining availability of cooling water. Therefore, to enhance thermal power plants’ resiliency to water shortages it is important to understand how non-steady conditions due to cycling will impact cooling water consumption and withdrawal intensity. Non-steady operation at power plants has been previously shown to decrease power plant thermal efficiency. Energy balance models have also demonstrated that a decrease in thermal efficiency is expected to increase cooling water consumption intensity. Furthermore, past work has used operating hours data to show idling and cycling gaps where cooling systems operate more than corresponding generators. As a result, an increase in cycling behavior may impact cooling water consumption and withdrawal intensity. This study uses data from the Energy Information Administration (EIA) and Environmental Protection Agency (EPA) to quantify the impact of cycling cooling water consumption intensity for recirculating cooling systems and withdrawal intensity for once-through cooling systems using energy balance and statistical approaches. In a novel application of a fixed effects model to study the effect of temperature on cooling water consumption and withdrawal intensity, this study finds temperature was consistently expected to increase consumption intensity and withdrawal intensity. Non-steady state conditions do not increase cooling water consumption intensity with statistical significance across unit types. However, additional validation of cooling water data is required to confirm these observed trends due to the sensitivity of these findings to model form and concerns with data quality of the dependent variables, cooling water consumption and withdrawal.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2017

Mind the Gap: Exploring the Underground of the NASA Space Cancer Risk Model

The REID quantifies the lifetime risk of death from radiation-induced cancer in an exposed astronaut. The NASA Space Cancer Risk (NSCR) 2012 mode incorporates elements from physics, biology, epidemiology, and statistics to generate the REID distribution. The current model quantifies the space radiation environment, radiation quality, and dose-rate effects to estimate a NASA-weighted dose. This weighted dose is mapped to the excess risk of radiation-induced cancer mortality from acute exposures to gamma rays and then transferred to an astronaut population. Finally, the REID is determined by integrating this risk over the individual's lifetime. The calculated upper 95% confidence limit of the REID is used to restrict an astronaut's permissible mission duration (PMD) for a proposed mission. As a statistical quantity characterized by broad, subjective uncertainties, REID estimates for space missions result in wide distributions. Currently, the upper 95% confidence level is over 350% larger than the mean REID value, which can severely limit an astronaut's PMD. The model incorporates inputs from multiple scientific disciplines in the risk estimation process. Physics and particle transport models calculate how radiation moves through space, penetrates spacecraft, and makes its way to the human beings onboard. Epidemiological studies of exposures from atomic bombings, medical treatments, and power plants are used to quantify health risks from acute and chronic low linear energy transfer (LET) ionizing radiation. Biological studies in cellular and animal models using radiation at various LETs and energies inform quality metrics for ions present in space radiation. Statistical methodologies unite these elements, controlling for mathematical and scientific uncertainty and variability. Despite current progress, these research platforms contain knowledge gaps contributing to the large uncertainties still present in the model. The NASA Space Radiation Program Element (SRPE) defines the knowledge gaps that impact our understanding of the cancer risks. These gaps are outlined in NASA's Human Research Roadmap [4], which identifies the research questions and actions recommended for reducing the uncertainty in the current NSCR model and for formulation of future models. The greatest contributors to uncertainty in the current model include radiation quality, dose rate effects, and the transfer of exposure-based risk from other populations to an astronaut population. Future formulations of the risk model may benefit from including other potential sources of uncertainty such as space dosimetry, errors in human epidemiology data, and the impact of microgravity and other spaceflight stressors. Here, we discuss the current capabilities of the NSCR-2012 model and several immediate research needs, highlighting areas expected to have an operational impact on the current model schema. The following subway-style route map outlines the NSCR-2012 model (Green Line), emphasizing the research gaps in the Human Research Roadmap for risk of radiation-induced carcinogenesis (Stops on Dashed Lines). The map diagrams how these research gaps feed specific portions of the model.

Chappell, L. J.↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2020

Structural and electronic switching of a single crystal 2D metal-organic framework prepared by chemical vapor deposition

The incorporation of metal-organic frameworks into advanced devices remains a desirable goal, but progress is hindered by difficulties in preparing large crystalline metal-organic framework films with suitable electronic performance. We demonstrate the direct growth of large-area, high quality, and phase pure single metal-organic framework crystals through chemical vapor deposition of a dimolybdenum paddlewheel precursor, Mo 2 (INA) 4 . These exceptionally uniform, high quality crystals cover areas up to 8600 µm 2 and can be grown down to thicknesses of 30 nm. Moreover, scanning tunneling microscopy indicates that the Mo 2 (INA) 4 clusters assemble into a two-dimensional, single-layer framework. Devices are readily fabricated from single vapor-phase grown crystals and exhibit reversible 8-fold changes in conductivity upon illumination at modest powers. Moreover, we identify vapor-induced single crystal transitions that are reversible and responsible for 30-fold changes in conductivity of the metal-organic framework as monitored by in situ device measurements. Gas-phase methods, including chemical vapor deposition, show broader promise for the preparation of high-quality molecular frameworks, and may enable their integration into devices, including detectors and actuators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗
NASA NTRS2023

Characterization of Errors in Satellite-Based HCHO/NO 2 Tropospheric Column Ratios With Respect to Chemistry, Column-to-PBL Translation, Spatial Representation, and Retrieval Uncertainties

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2 ) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO 2 + RO 2 (LRO x ) to the chemical loss of NO x (LNO x ). Subsequently, we challenge the predictive power of HCHO/NO 2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LRO x /LNO x . FNRs show a strongly linear (R 2 =0.94) relationship with LRO x /LNO x , but only on the logarithmic scale. Following the baseline (i.e., ln(LRO x /LNO x ) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NO x -sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the RO x –HO x cycle. We calculate high ozone production rates (PO 3 ) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NO x -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km 2 ). A pixel with a grid size of 216 km 2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 molec. cm −2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Amir H. Souri↗
DOE OSTI2022

Intermediate-phase engineering via dimethylammonium cation additive for stable perovskite solar cells

Achieving the long-term stability of perovskite solar cells is arguably the most important challenge required to enable widespread commercialization. Understanding the perovskite crystallization process and its direct impact on device stability is critical to achieving this goal. The commonly employed dimethyl-formamide/dimethyl-sulfoxide solvent preparation method results in a poor crystal quality and microstructure of the polycrystalline perovskite films. In this work, we introduce a high-temperature dimethyl-sulfoxide-free processing method that utilizes dimethylammonium chloride as an additive to control the perovskite intermediate precursor phases. By controlling the crystallization sequence, we tune the grain size, texturing, orientation (corner-up versus face-up) and crystallinity of the formamidinium (FA)/caesium (FA) y Cs 1-y Pb(I x Br 1-x )3 perovskite system. A population of encapsulated devices showed improved operational stability, with a median T80 lifetime (the time over which the device power conversion efficiency decreases to 80% of its initial value) for the steady-state power conversion efficiency of 1,190 hours, and a champion device showed a T80 of 1,410 hours, under simulated sunlight at 65 degrees C in air, under open-circuit conditions. In conclusion, this work highlights the importance of material quality in achieving the long-term operational stability of perovskite optoelectronic devices.

14 SOLAR ENERGY↗
NASA NTRS2001

Automated Theorem Proving in High-Quality Software Design

The amount and complexity of software developed during the last few years has increased tremendously. In particular, programs are being used more and more in embedded systems (from car-brakes to plant-control). Many of these applications are safety-relevant, i.e. a malfunction of hardware or software can cause severe damage or loss. Tremendous risks are typically present in the area of aviation, (nuclear) power plants or (chemical) plant control. Here, even small problems can lead to thousands of casualties and huge financial losses. Large financial risks also exist when computer systems are used in the area of telecommunication (telephone, electronic commerce) or space exploration. Computer applications in this area are not only subject to safety considerations, but also security issues are important. All these systems must be designed and developed to guarantee high quality with respect to safety and security. Even in an industrial setting which is (or at least should be) aware of the high requirements in Software Engineering, many incidents occur. For example, the Warshaw Airbus crash, was caused by an incomplete requirements specification. Uncontrolled reuse of an Ariane 4 software module was the reason for the Ariane 5 disaster. Some recent incidents in the telecommunication area, like illegal "cloning" of smart-cards of D2GSM handies, or the extraction of (secret) passwords from German T-online users show that also in this area serious flaws can happen. Due to the inherent complexity of computer systems, most authors claim that only a rigorous application of formal methods in all stages of the software life cycle can ensure high quality of the software and lead to real safe and secure systems. In this paper, we will have a look, in how far automated theorem proving can contribute to a more widespread application of formal methods and their tools, and what automated theorem provers (ATPs) must provide in order to be useful.

Schumann, Johann↗
DOE OSTI2022

Metal–ceramic composite structures for fabrication of high power density plasmonic devices

The recent decade brought many advances to plasmonics, but high power density plasmonic antennas designed to behave as heaters or operate in high temperature environments are still facing material stability challenges preventing their ultimate use. Gold has been the optimal choice among plasmonic materials but experiences morphology changes at temperature that result in device efficiency reduction and failure. Bulk titanium nitride has been explored as a solution but has deal-breaking tradeoffs in device quality factor. In this paper, we explore via proof-of-concept the use of a metal–ceramic composite structure to determine whether a bulk Au nanorod can provide strong plasmonic resonances while coated with an ultrathin conformal layer of titanium nitride or silica to provide morphological stability and sufficient plasmonic activity without excessive resonance quality degradation. We show SEM-level morphological stability for temperatures up to 500 °C with coatings below 4 nm. Computer modeling suggests the ultrathin titanium nitride has detrimental effects on the strong plasmonic resonances of a Au nanorod. Here, we then looked into other possible coatings for solutions to stabilize high power density plasmonic antennas including plasmonic oxides, metal adhesion layers, and silica, the latter appearing to be the best option while lowering the overall peak electric field intensity, the silica increases the electric field intensity at its boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tailoring the Intermediate Phase to Control Formation of γ‑CsPbI3 Films

Controlling the crystallization pathway of inorganic CsPbI3 perovskite is essential for achieving high efficiency and stability in optoelectronic devices. Here, we report a solvent-engineering strategy that combines an antisolvent process with vacuum treatment (AVT) to modulate evaporation dynamics of the precursor, guiding the formation of highly oriented (CH3)2NH2PbI3 (DMAPbI3) and Cs4PbI6 intermediate phases. Synchrotron and in situ analyses revealed correlations between intermediate orientation and γ-CsPbI3 crystallinity. This directional crystallization pathway promotes vertical alignment and grain enlargement in γ-CsPbI3 films, resulting in fewer voids, lower defect densities, and reduced tensile strain. Photovoltaic devices based on AVT-processed films achieved a power conversion efficiency of 18.47% with a fill factor of 83.14% and retained 101.9% of their initial efficiency after 526 h without encapsulation. This study first reports that the quality of DMAPbI3 and Cs4PbI6 intermediates, controlled by combination of antisolvent and vacuum treatment, plays a crucial role in achieving high-quality γ-CsPbI3 films.

Yoon, Geon Woo↗
DOE OSTI2022

Femtosecond-Terawatt Hard X-Ray Pulse Generation with Chirped Pulse Amplification on a Free Electron Laser

Advances of high intensity lasers have opened up the field of strong field physics and led to a broad range of technological applications. Recent x ray laser sources and optics development makes it possible to obtain extremely high intensity and brightness at x ray wavelengths. In this paper, we present a system design that implements chirped pulse amplification for hard x ray free electron lasers. Numerical modeling with realistic experimental parameters show that near-transform-limit single-femtosecond hard x ray laser pulses with peak power exceeding 1 TW and brightness exceeding 4×10 35 s -1 mm -2 mrad -2 0.1\%bandwdith -1 can be consistently generated. In conclusion, realization of such beam qualities is essential for establishing systematic and quantitative understanding of strong field x-ray physics and nonlinear x ray optics phenomena.

47 OTHER INSTRUMENTATION↗