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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
DOE OSTI2021

REopt Lite User Manual

REopt Lite evaluates the economic viability of grid-connected solar photovoltaics, wind, combined heat and power, and electric and thermal storage at commercial and small industrial sites. It allows building owners to identify the system sizes and dispatch strategies that minimize the site’s life cycle cost of energy. REopt Lite also estimates the amount of time on-site generation and storage can sustain the site's critical load during a grid outage and allows the user the choice of optimizing for energy resilience. It is primarily used to inform project development decisions and to support research on the factors that drive project feasibility for market development and policy analysis. It is available through a web interface, application programing interface, and open-source code. This user manual provides an overview of the model, including its capabilities and typical applications; inputs and outputs; economic calculations; technology descriptions; and model parameters, variables, and equations. The model is highly flexible and is continually evolving to meet the needs of each analysis. Therefore, this report is not an exhaustive description of all capabilities, but rather a summary of the core components of the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Mechanical evaluation of WC-Co materials with varying microstructures

Tungsten carbide–cobalt materials are useful in a variety of extreme applications due to a desirable blend of properties, yet the technology has not significantly changed since their initial development in the 1920s. The mechanical properties of this class of materials is highly dependent on two variables, the size of the tungsten carbide grains, and the amount of binder phase present in the final body. In this study, the amount of binder phase is isolated across three commercial materials from the same manufacturer with three different grain sizes to investigate the effect on mechanical properties. The mechanical properties investigated are indentation hardness, flexure and tensile strength, as well as fracture toughness. In general, an increase in hardness and tensile strength with decreasing grain size was observed, while the fracture toughness showed the opposite trend with toughness increasing with increasing grain size. The flexure strength results did not show a correlation to grain size. Fractographic analysis identified the dominant strength-limiting flaw for each sample, which largely were in the form of porosity. Other flaws types, such as inclusions from the milling process, clusters of large grains, and machining cracks from the surface finishing process, were also identified. Finally, Weibull analysis was performed and deemed appropriate for analysis of these materials, but strength-size scaling was not conducted due to the variability in the strength-limiting feature between the different specimen geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a number of extreme metrics. While we exploit existing large ensembles, our perspective is that of a modeling center wanting to estimate a priori such sizes on the basis of an existing small ensemble (we assume the availability of only five members here). We therefore ask if such a small-size ensemble is sufficient to estimate accurately the population variance (i.e., the ensemble internal variability) and then apply a well-established formula that quantifies the expected error in the estimation of the population mean (i.e., the forced component) as a function of the sample size n, here taken to mean the ensemble size. We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extremes as a function of n by plugging into the formula an estimate of the population variance derived on the basis of five members. For a range of spatial and temporal scales, forcing levels (we use simulations under Representative Concentration Pathway 8.5) and two models considered here as our proof of concept, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, and this remains true when statistical inference through extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. Using the F test, we find that estimates on the basis of only 5 or 10 ensemble members accurately represent the full ensemble variance even when the analysis is conducted at the grid-point scale. The detection of changes in the variance when comparing different times along the simulation, especially for the precipitation-based metrics, requires larger sizes but not larger than 15 or 20 members. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, such as physics parameter settings, when designing ensemble simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Impact of Demographic Lifecycle States on Time to Vehicle Purchase: Insights from the Panel Study of Income Dynamics

This study examines the impact of demographic lifecycle stages on the timing of vehicle purchases, using data from the Panel Study of Income Dynamics from 1999 to 2021. Survival analysis was employed to model the duration until households purchase vehicles, incorporating key lifecycle variables such as age, employment status, marital status, childbirth, home ownership, and the presence of school-going children. The life table results indicate that early adulthood (ages 20–35) is the prime period for vehicle acquisition, with significant peaks around ages 25 to 30. Additionally, the instantaneous hazard of purchasing a vehicle is highest in the late 40s and early 50s. According to the Cox proportional hazards model, employment, marital status, and home ownership significantly increase the likelihood of purchasing a vehicle, while living in multi-unit dwellings decreases it. Interaction effects reveal that married individuals with employed spouses are substantially more likely to purchase vehicles. In conclusion, this study serves as a steppingstone toward integrating demographic lifecycle analysis into car ownership modeling that better reflects real-world scenarios and increases the accuracy of policy and strategic planning.

Car ownership↗
DOE OSTI2024

Siting Analysis of a Solar-Nuclear-Desalination Integrated Energy System

Nuclear power is typically deployed as a baseload generator. Increased penetration of variable renewables motivates combining nuclear and renewable technologies into Integrated Energy Systems (IES) to improve dispatchability, component synergies and, through cogeneration, address multiple markets. However, combining multiple energy resources heavily depends on the proper selection of each system’s location and design limitations. In this paper, co-siting options for IES that couple nuclear and concentrating solar power (CSP) with thermal desalination are investigated. A comprehensive siting analysis is performed that utilizes global information survey data to determine possible co-siting options for nuclear and solar thermal generation in the United States. Viable co-siting options are distributed across the Southwestern U.S., with the greatest concentration of siting options in the southern Great Plains, although siting with higher solar direct normal irradiance is possible in other states such as Arizona and New Mexico. Brackish water desalination is also attractive across the southwest U.S. due to high water stress, but for brackish water desalination reverse osmosis (an electricity driven process) is most cost- and energy-efficient, which does not require co-siting with the thermal generator. The most attractive state for nuclear and thermal desalination (which is more attractive when using seawater) is Texas, although other areas may become attractive as water stress increases over the coming decades. Co-siting of all CSP and thermal desalination is challenging as attractive CSP sites are not coastal.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Data and scripts associated with “Point-scale organic-matter decomposition in streambeds is weakly associated with reach-scale respiration”

This data package is associated with “Point-scale organic-matter decomposition in streambeds is weakly associated with reach-scale respiration” published in EGU Biogeosciences (Stegen et al., 2026; https://doi.org/10.5194/bg-23-3981-2026). It contains cotton strip decomposition rates (Kcd and Kdd) collected across the Yakima River Basin (YRB), Washington, USA. These data were collected to support a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. Associated data used in analysis, metadata, and field protocols can be accessed at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1987520. This data package is associated with the repository found at https://github.com/river-corridors-sfa/rcsfa-ST-2B-SSS-cotton-strip. A preliminary version of this data package was published in December 2025 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This data package consists of (1) readme; (2) data dictionary (dd); (3) file level metadata (flmd); and (4) four folders: (1) R-scripts; (2) figures; (3) outputs from the scripts; and (4) published data. The published data folder contains a readme directing the user to download data in order to run the R-scripts. All files are .csv, .pdf, .R, .Rmd, and .txt. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis

As the representatives of closely related Chinese medicinal materials (CMMs) originated from Rutaceae family, Aurantii fructus immaturus (AFI), Aurantii fructus (AF), Citri reticulatae pericarpium viride (CRPV), and Citri reticulatae pericarpium (CRP) have better functions in regulating qi and promoting gastrointestinal motility. However, differences in the quality of closely related Citrus CMMs have not yet been revealed until now. Herein, this study focused on the systematic differentiation and in-depth understanding of closely related Citrus CMMs by a strategy integrating chemometrics and network pharmacology. Determined by ultra performance liquid chromatography, the content of nine flavonoids showed obvious fluctuations in the decoction pieces from different species (Citrus aurantium Linnaeus and Citrus reticulate Blanco) with decreasing levels in the samples of ripe fruits. Decoction pieces from the different species and ripening stages were well distinguished by orthogonal projection to latent structure-discriminate analysis (OPLS-DA) and cluster analysis. As a result, four active components including narirutin, naringenin, hesperidin, and 3,5,6,7,8,3′,4′-heptemthoxyflavone were filtered out by variable importance for the projection (VIP) value (VIP > 1.0), which were regarded as chemotaxonomic markers. Furthermore, a components-targets-diseases network was constructed via ingenuity pathway analysis (IPA), and the correlations were predicted between four chemotaxonomic markers, 223 targets, and three diseases including colitis, breast cancer, and colorectal cancer. The obtained results will be of great significance for identifying closely related Citrus CMMs and conduce to improving the resource utilization of CMMs.

Mu, Qixuan↗
DOE OSTI2026

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Unraveling the Wrinkle in Time-Variable Sources with Lunes and Synthetic Seismic Data

In this report, we describe how to estimate the time-variable components of the seismic moment tensor and compare these estimates to the more conventional analysis that incorporates an assumption of the source time function (STF) across all components of the seismic moment tensor. The advantage of our method is that we are able to independently estimate the time-evolution of each component of the seismic moment tensor, which may help to resolve the complex source phenomena associated with buried explosions. By performing an eigen decomposition of the time-evolving seismic moment tensor components, we are able to plot the seismic mechanism as a trajectory on a lune diagram. This technique enables interpretation of the seismic mechanism as a function of time, as opposed to the more conventional analysis which assumes that the seismic mechanism is time invariant. Finally, we describe the differences between the seismic moment and the seismic moment rate STFs, how to implement each one in inversion schemes, and the relative strengths/weaknesses of each. Our key take-away is that we are able to distinguish nearly-overlapping sources with highly different mechanisms, such as an explosion immediately following an earthquake, by estimating moment rate from seismic data through a STF-invariant inversion for the full time-variable moment tensor.

58 GEOSCIENCES↗
DOE OSTI2022

Analysis of implant loss risk factors after simultaneous guided bone regeneration: A retrospective study of 5404 dental implants

Abstract Purpose The purpose was to analyze the risk factors for implant loss after simultaneous guided bone regeneration (GBR). Materials and Methods Patients who underwent implant placement with simultaneous GBR between January 2011 and December 2018 were screened for this study. The cumulative survival rate (CSR) was calculated using the life table method. Log‐rank test and Kaplan–Meier survival estimates were used to identify potential risk factors for implant loss. The association between the investigated variables and implant loss was determined using hazard ratios (HRs) obtained from a multivariate Cox regression analysis. Results A total of 3973 patients with 5404 implants were included in this study. The CSRs of the implants at 1, 5, and 10 years were 99.6%, 98.9%, and 98.7%, respectively. Male patient (HR = 2.94, 95% CI: 1.41–6.14), periodontitis (HR = 4.26, 95% CI: 2.05–9.86), tissue‐level implants (HR = 3.02, 95% CI: 1.30–6.98), narrow implants (HR = 2.71, 95% CI: 1.12–6.57), and implant length ≤10 mm (HR = 2.91, 95% CI: 1.41–6.02) significantly increased the risk of implant loss ( p < 0.05). The risk of implant loss was significantly higher in the maxillary posterior region (HR = 2.26, 95% CI: 1.04–4.90) than in the maxillary anterior region ( p < 0.05). Compared to Straumann, Nobel (HR = 4.07, 95% CI: 1.75–9.44) and other implant systems (HR = 14.23, 95% CI: 4.32–46.85) showed a significantly higher risk of implant loss ( p < 0.05). Conclusion Male patient, periodontitis, maxillary posterior region, Nobel implant system, other implant systems, tissue‐level implants, narrow implants, and implant length ≤10 mm were considered risk factors for implant loss after simultaneous GBR.

Shen, Xiaoting↗
DOE OSTI2024

TDCOSMO - XVI. Measurement of the Hubble constant from the lensed quasar WGD 2038–4008

Time-delay cosmography is a powerful technique to constrain cosmological parameters, particularly the Hubble constant (H0). The TDCOSMO Collaboration is performing an ongoing analysis of lensed quasars to constrain cosmology using this method. In this work, we obtain constraints from the lensed quasar WGD 2038−4008 using new time-delay measurements and previous mass models by TDCOSMO. This is the first TDCOSMO lens to incorporate multiple lens modeling codes and the full time-delay covariance matrix into the cosmological inference. The models are fixed before the time delay is measured, and the analysis is performed blinded with respect to the cosmological parameters to prevent unconscious experimenter bias. We obtain DΔ t = 1.68−0.38+0.40 Gpc using two families of mass models, a power-law describing the total mass distribution, and a composite model of baryons and dark matter, although the composite model is disfavored due to kinematics constraints. In a flat ΛCDM cosmology, we constrain the Hubble constant to be H0 = 65−14+23 km s−1 Mpc−1. The dominant source of uncertainty comes from the time delays, due to the low variability of the quasar. Future long-term monitoring, especially in the era of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, could catch stronger quasar variability and further reduce the uncertainties. This system will be incorporated into an upcoming hierarchical analysis of the entire TDCOSMO sample, and improved time delays and spatially-resolved stellar kinematics could strengthen the constraints from this system in the future.Key words: gravitational lensing: strong / cosmological parameters / distance scale⋆ Corresponding author; kcwong19@gmail.com.⋆⋆ NHFP Einstein fellow.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Theoretical kinetics predictions for NH 2 + HO 2

Recent modeling studies of NH 3 oxidation, which are motivated by the prospective role of ammonia as a zero-carbon fuel, have indicated significant discrepancies between existing literature mechanisms. In this study high level theoretical kinetics predictions have been obtained for the reaction of NH 2 with HO 2 , which has previously been highlighted as an important reaction with high sensitivity and high uncertainty. The potential energy surface is explored with coupled cluster calculations including large basis sets and high-level corrections to yield high accuracy (~0.2 kcal/mol) estimates of the stationary point energies. Variational transition state theory is used to predict the microcanonical rate constants, which are then incorporated in master equation treatments of the temperature and pressure dependent kinetics. For the radical-radical channels, the microcanonical rates are obtained from variable reaction coordinate transition state theory implementing directly evaluated multireference electronic energies. The analysis yields predictions for the total rate constant as well as the branching to the NH 3 + O 2 , H 2 NO + OH, and HNO + H 2 O channels. Rate constants are also reported for the H 2 NO + OH reaction as they arise naturally from the analysis. The rate constant and branching fraction determined in this work for the NH 2 + HO 2 reaction deviate significantly from values used in most previous modeling studies. The fact that the main product channel is chain terminating, rather than propagating, has strong implications for modeling NH 3 ignition and oxidation, in particular at intermediate temperatures and elevated pressure.

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

Theoretical Kinetics Predictions for Reactions on the NH 2 O Potential Energy Surface

Recent modeling studies of ammonia oxidation, which are motivated by the prospective role of ammonia as a zero-carbon fuel, have indicated significant discrepancies among the existing literature mechanisms. In this study, high-level theoretical kinetics predictions have been obtained for reactions on the NH 2 O potential energy surface, including the NH 2 + O, HNO + H, and NH + OH reactions. These reactions have previously been highlighted as important reactions in NH 3 oxidation with high sensitivity and high uncertainty. The potential energy surface is explored with coupled cluster calculations, including large basis sets and high-level corrections to yield high-accuracy (~0.2 kcal/mol 2σ uncertainty) estimates of the stationary point energies. Variational transition state theory is used to predict the microcanonical rate constants, which are then incorporated in master equation treatments of the temperature- and pressure-dependent kinetics. For radical–radical channels, the microcanonical rates are obtained from variable reaction coordinate transition state theory implementing directly evaluated multireference electronic energies. The analysis yields predictions for the total rate constants as well as the branching ratios. We find that the NO + H 2 channel contributes 10% of the total NH 2 + O flux at combustion temperatures, although this channel is not included in modern NH 3 oxidation mechanisms. Modeling is used to illustrate the ramifications of these rate predictions on the kinetics of NH 3 oxidation and NO x formation. Finally, the present results for NH 2 + O are important for predicting the chain branching and formation of NO in the oxidation of NH 3 and thermal DeNO x .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exploring Responses of Contact Kelvin Probe Force Microscopy in Triple-Cation Double-Halide Perovskites

Hybrid organic–inorganic perovskite (HOIP) solar cells have undergone unprecedentedly rapid improvement in power conversion efficiencies during the last decade, but controversy remains regarding the role of local phenomena and heterogeneity in grain by grain. These challenges in turn necessitate understanding the fundamental mechanisms operating at individual grains and exploring how these mechanisms give rise to the collective behaviors of the films. Although numerous efforts have targeted local phenomena in these materials using classical scanning probe microscopy modalities, the measurements remain difficult to interpret due to intrinsic coupling between possible ferroelectric, ion migration, bulk and surface electrochemical phenomena, and the effects of light on these behaviors. Here, we explore the electromechanical responses in (Cs 0.05 FA 0.85 MA 0.15 )Pb(I 0.85 Br 0.15 ) 3 perovskite thin film in the dark and under illumination using band excitation-piezoresponse force microscopy (BE-PFM). To identify and separate ferroic, electrostatic, and electrochemical responses, we utilize contact-Kelvin probe force microscopy (cKPFM). The cKPFM responses show weak variability with morphological features in the dark, while such variability significantly increases under illumination. Deconvolution of the cKPFM responses via principal component analysis (PCA) shows heterogeneity in grain to grain possibly due to variation in migration and segregation of halide ions in mixed perovskites at specific grains. Further, we propose an electrostatic mechanism based on the physics of semiconductors, which can directly link the observed cKPFM behaviors with the ionic and electronic density of states (DOS) and their time dynamics. Here, these findings suggest that cKPFM is a powerful probe of local ionic behaviors on the nanoscale, both in HOIPs and other materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗