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DOE OSTI2023

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗

DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

Aboveground biomass↗

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↗

DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Event-Based Energy Impact Tracking and Forecasting with Limited Measurements for Rooftop Units

Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.

packaged air conditioners, packaged heat pumps, ro↗

DOE OSTI2021

Stability enhancement module for grid-following converters: Hardware implementation and validation

Low-frequency oscillations limit power delivery of voltage-source converter interfaced resources, for example, wind and solar, operating in grid-following control mode. Our prior research identified the instability mechanism as the coupling between real power and voltage at point of common coupling. In turn, a stability enhancement control was proposed to weaken the coupling by introducing a simple voltage-based feedback loop. The stability enhancement control has been validated via electromagnetic transient simulation testbeds. The objectives of this paper are (a) to provide a more insightful explanation of the enhancement module by examining the impedance of the grid-following converter and (b) to verify the performance of the stability module using a hardware testbed. The theoretic examination demonstrates that stability enhancement module effectively reduces the negative resistance effect of $Z_{qq}$ and thus boosts stability. The experimental research leads to a hardware testbed of a grid-following converter with the ability of demonstrating low-frequency oscillations. Finally, the experimental results demonstrate the efficacy of the proposed stability enhancement control and the readiness of commercial realization of the stability enhancement unit.

42 ENGINEERING↗

DOE OSTI2021

Sink Engineering in Photosynthetic Microbes

Photosynthetic organisms must balance harvested light energy (source) with a corresponding capacity to utilize the energy via downstream metabolic processes (sink). Insufficient source energy leads to starvation conditions, while insufficient sink capacity results in an overreduction of the electron transport chain, reduced photosynthetic efficiency, and photodamage. Despite the central importance of source/sink balancing in photosynthesis, our understanding of the mechanisms used to control this process in remains poor across diverse phototrophs. In particular, these balancing processes are essentially unstudied in cyanobacteria and microalgae, which possess distinct needs relative to the multicellular plants that have served as the model organisms for elucidating source/sink pathways. Recent bioengineering efforts have uncovered phenomena that are consistent with a well-developed source/sink sensing machinery in unicellular phototrophs. Furthermore, a deeper understanding of the fundamental mechanisms underlying source/sink dynamics in cyanobacteria and algae may prove to be critical for our capacity to boost photosynthetic efficiencies in these organisms, and enable their use as alternative crop species.

Carbon Fixation↗

DOE OSTI2020

Autonomously Responsive Membranes for Chemical Warfare Protection

Stimuli-responsive materials offer new opportunities to resolve long-standing material challenges and are rapidly gaining pivotal roles in diverse applications. For example, smart protective garments that rapidly transport water vapor and autonomously block chemical threats are expected to enable an effective new paradigm of adaptive personal protection. However, the incorporation of these seemingly incompatible properties into a single responsive system remains elusive. Herein, a bistable membrane that can rapidly, selectively, and reversibly transition from a highly breathable state in a safe environment to a chemically protective state when exposed to organophosphate threats such as sarin is demonstrated. Dynamic response to chemical stimuli is achieved through the physical collapse of an ultrathin copolymer layer on the membrane surface, which efficiently gates transport through membrane pores composed of single-walled carbon nanotubes (SWNTs). The adoption of nanometer-wide SWNTs for ultrafast moisture conduction enables a simultaneous boost in size-sieving selectivity and water-vapor permeability by decreasing nanotube diameter, thereby overcoming the breathability/protection trade-off that limits conventional membrane materials. Adaptive multifunctional membranes based on this platform greatly extend the active use of a protective garment and present exciting opportunities in many other areas including separation processes, sensing, and smart delivery.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

Abstract To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ e ) as high as 2.22 cm 2 V −1 s −1 and a hole/electron mobility ratio (μ h /μ e ) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.

Liu, Qian↗

DOE OSTI2020

Secondary Ion Mass Spectrometry (SIMS) for Chemical Characterization of Metal Halide Perovskites

Metal halide perovskite (MHP) solar cells have attracted much attention due to the rapidly growing power conversion efficiency that has reached 25.2% in a decade, comparable to established commercial photovoltaic modules. Compositional engineering is one of the most effective methods to boost the performance of MHP solar cells. Further improving the efficiency and the stability of MHP solar cells necessitates good understanding of the chemical–efficiency correlation and the chemical evolution during the degradation of MHP solar cells. In this regard, time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool to investigate the chemical aspect of MHPs and has played an important role in advancing the development of MHP optoelectronics. However, up to date, a review that can guide future utilization of ToF-SIMS in the MHP development is missing. In this paper, the capabilities of ToF-SIMS in MHP investigations are summarized and analyzed from simple material synthesis and chemical distribution to more complicated device operation mechanism and stability. The strength of ToF-SIMS in resolving important issues in this field, such as interface composition, ion migration, and degradation in MHP is highlighted. Finally, an outlook with an emphasis on making the utmost of ToF-SIMS in developing MHP devices is provided.

36 MATERIALS SCIENCE↗