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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Decarbonization in Climate Resilience Planning

Recent executive orders such as E.O. 14008 require federal agencies to address climate change by enhancing resilience and reducing emissions through decarbonization. Traditionally, federal agencies require their sites to develop preparedness plans, such as continuity of operations plans and/or more comprehensive resilience plans. When climate change is included in these plans, the focus tends to be on climate adaptation solutions such as hardening infrastructure and not on climate mitigation through decarbonization. However, with the ambitious emission reductions targets set out by the federal government, it is essential to provide resources and tools to support energy and water managers in achieving decarbonization goals. To achieve this, there is a need for decarbonization to be incorporated into existing resilience planning processes. We discuss a method of incorporating a decarbonization analysis into an existing resilience planning process to create a holistic framework that considers climate adaptation, climate mitigation, and risk reduction priorities when developing and prioritizing solutions for federal sites. Site managers and decision makers must be aware of potential opportunities and trade-offs in meeting energy and water performance goals, emission reductions goals, and climate adaptation goals when developing technological, operational, or institutional resilience solutions. By combining these processes, energy and water managers can minimize the additional level of effort to ensure that their sites are not only able to withstand climate-related energy and water disruptions, but also contribute to climate mitigation. A holistic approach can help federal agencies serve as a model for incorporating decarbonization strategies into site-level resilience planning for the rest of the nation.

Elliott, Douglas B.↗
DOE OSTI2021

Impact of Controlled Storage Conditions on the Hydrolysis and Surface Morphology of Amorphous-UO 3

The hydration and morphological effects of amorphous (A)-UO 3 following storage under varying temperature and relative humidity have been investigated. This study provides valuable insight into U-oxide speciation following aging, the U-oxide quantitative morphological data set, and, overall, the characterization of nuclear material provenance. A-UO 3 was synthesized via the washed uranyl peroxide synthetic route and aged based on a 3-factor circumscribed central composite design of experiment. Target aging times include 2.57, 7.00, 14.0, 21.0, and 25.4 days, temperatures of 5.51, 15.0, 30.0, 45.0, and 54.5 °C, and relative humidities of 14.2, 30.0, 55.0, 80.0, and 95.8% were examined. Following aging, crystallographic changes were quantified via powder X-ray diffraction and an internal standard Rietveld refinement method was used to confirm the hydration of A-UO 3 to crystalline schoepite phases. The particle morphology from scanning electron microscopy images was quantified using both the Morphological Analysis of MAterials software and machine learning. Results from the machine learning were processed via agglomerative hierarchical clustering analysis to distinguish trends in morphological attributes from the aging study. Significantly hydrated samples were found to have a much larger, plate-like morphology in comparison to the unaged controls. Predictive modeling via a response surface methodology determined that while aging time, temperature, and relative humidity all have a quantifiable effect on A-UO 3 crystallographic and morphological changes, relative humidity has the most significant impact.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Using a Large Language Model as a Building Block to Generate Usable Validation and Verification Suite for OpenMP

In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using – whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of providing input to a production quality validation and verification suite, we manually implement the corrections required to make the tests valid according to the current OpenMP specification. We quantify this effort as small, medium, or large, and record the lines of code changed to correct the invalid tests. With the corrected tests we validate recent implementations from HPE, AMD, and GNU on the Frontier supercomputer. Our experiment and subsequent analysis show that although LLMs are capable of producing HPC specific codes, they are limited by their understanding of the deeper semantics and restrictions of programming models such as OpenMP. Unsurprisingly more commonly used features have better support, while some OpenMP 3.0 directives such as sections and tasking are not universally supported on accelerators. We demonstrate that successful compilation and execution to completion are inadequate metrics for evaluating generated code and that, at this time, commodity LLMs require expert intervention for code verification. This points to gaps in the training data that is currently available for HPC. We demonstrate that with "small" effort 37% of generated invalid C tests and 63% of generated invalid Fortran tests could be corrected. This improves productivity of test generation as we circumvent writing from scratch and the common programming errors associated with it.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗
DOE OSTI2023

SOLAr Critical Infrastructure Energization (SOLACE): Leveraging Distributed Energy Resources to Provide Local Power

This document is a technical report based on a large-scale DOE-funded project conducted between 2019 and 2022. The project, called SOLAr Critical infrastructure Energization (SOLACE), is aimed at leveraging distributed energy resources (DERs) to provide local power to communities, feeders, or other regions. The project included a particular focus on high-value critical loads such as municipal water supply, telecommunication hubs, and disaster shelters. The key developments of the project were: (1) A comprehensive pre-event power system analysis methodology that identifies and characterizes the viable local power options. (2) A DMS-based example control system for activating and operating the local power solution during a time of crisis. (3) Grid-forming inverter technology to enable isolated local power operation and black-starting. (4) Cyber-security considerations for isolated systems when wider-area communication may be offline. This report describes the overall process that identifies and assesses the viable DER-based local power solutions for a given facility, region, or community. The report documents the process, including the key analysis steps, tools required, data requirements, and recommended pass/fail criteria for each step. It also provides a sample implementation of this process through a test case. Finally, it provides details of how a viable pre-event plan can be selected and activated at go-time when an event has occurred.

14 SOLAR ENERGY↗
DOE OSTI2023

Estimation of Participation Factors Using the Synchrosqueezed Wavelet Transform

This paper proposes a data-driven approach for estimating participation factors for a power system using only simulation results on selected disturbances. The approach is purely response-based and does not need a linearized system model for eigen-analysis, which makes it applicable to systems whose detailed, complete mathematical models are not available. Considering the unavoidable nonlinearity as exhibited in the transient period of a system response, the Synchrosqueezed Wavelet Transform is applied to simulated responses for modal analysis to obtain participation factors. Based on simulations of Kundur's two-area system using both the electromagnetic transient model and phasor model, the participation factors estimated by the proposed approach are compared with two other signal processing tools, the Prony analysis and continuous wavelet transform, and are also benchmarked with conventional model-based participation factors.

participation factor↗
DOE OSTI2026

Evolution of the Antarctic Ice Sheet from 2000–2300 and beyond: model sensitivity and uncertainty analysis using MPAS-Albany Land Ice

We present a description of the Antarctic Ice Sheet model configuration submitted to the ISMIP6-Antarctica-2300 experiment using the MPAS-Albany Land Ice model, along with three new sets of simulations: (1) a set of extended simulations to 2500 for three forced experiments and to 2775 for the control experiment; (2) a sensitivity analysis of our model configuration to parameters controlling basal sliding and sub-shelf melt, and to model structural choices including the choice of the energy and stress balances; and (3) a 72-member ensemble run on graphics processing units (GPUs) and analysis of variance to determine the primary sources of uncertainty in our ice-sheet model projections. Our extended simulations predict rapid retreat beginning after 2300 for SSP1-2.6 forcing and after 2500 for present-day (control) forcing, primarily in the Amundsen Sea Embayment. We find that varying the sub-shelf melt parameter between the 5th to 95th percentile values for a mean-Antarctic calibration target results in an up to ∼ ± 40 % change in sea-level contribution relative to our baseline simulations that used the median value. Using a linear basal sliding law reduces sea-level contribution by 51 %–73 % relative to our baseline nonlinear sliding law with an exponent of 1/5. When using basal sliding law exponents of 1/3 and 1/10, the overall difference from our baseline simulations at 2300 is on the order of 10 %. The Amundsen Sea Embayment region displays a strongly non-linear dependence of mass loss on the sliding law exponent, with no discernible relationship between the sliding law exponent and the mass loss by 2300, while the sectors feeding the Ross and Filchner-Ronne ice shelves exhibit more mass loss with a more-plastic sliding law. Our model fidelity sensitivity experiments reveal a 9 %–31 % increase in sea-level contribution when using a depth-integrated stress balance approximation relative to our three-dimensional solver, while using a fixed-in-time temperature field increases sea-level contribution by 14 %–88 % relative to two thermomechanically coupled configurations. Our 72-member ensemble and analysis of variance show that the uncertainty in long-term projections is dominated by the choice of Earth system model forcing and the presence or absence of hydrofracture forcing, rather than uncertainty in sliding and sub-shelf melt parameters.

58 GEOSCIENCES↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗
DOE OSTI2025

Toward a sustainable circular economy of multilayer plastic films: Life cycle and techno-economic assessment with a focus on end-of-life treatment and multiple recovery cycles

This study presents a life cycle assessment (LCA) and techno-economic analysis (TEA) of end-of-life technologies for treating polyethylene–polyamide barrier film waste, focusing on quality degradation across recovery cycles. Novel treatment methods are experimentally validated, while others are drawn from literature and industry consultations. A displacement approach, assuming no quality loss, is first applied. Results show that solvent-based recycling via the solvent-targeted recovery and precipitation (STRAP) process outperforms alternatives across environmental indicators, reducing global warming potential (GWP) by 40% compared to landfilling. Incineration performs worst in most categories, particularly eutrophication (80% higher than landfilling), due to nitrogen emissions. Experimentally validated downcycling (pelletizing) proves more economically viable. The assumption of infinite recoverability is overly optimistic. To address this, we propose a mathematical framework accounting for a finite number of recovery cycles. This refined model shows reduced GWP and cost savings for solvent recovery, making its benefits less pronounced than initially estimated. Sensitivity and uncertainty analyses reveal strong dependence on recovered material quality and solvent recovery efficiency, underscoring the need for optimized process design. Finally, hotspot analysis identifies greenhouse gas emissions from the polyamide supply chain as the dominant GWP contributor. In conclusion, the results underscore potential trade-offs across pathways and show that solvent-based recovery’s sustainability depends heavily on process conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Technoeconomic and life cycle energy analysis of carbon fiber manufactured from coal via a novel solvent extraction process

Coal is a versatile energy resource and was a driver of the industrial revolution that transformed the economies of Europe and North America and the trajectory of civilization. In this work, a technoeconomic analysis was performed for a coal-to-carbon-fiber manufacture process developed at the University of Kentucky’s Center for Applied Energy Research. According to this process, coal, with decant oil as the solvent, was converted to mesophase pitch via solvent extraction, and the mesophase pitch was subsequently converted to carbon fiber. The total cost to produce carbon fibers from coal and decant oil via the solvent extraction process was estimated to be $\$$11.50/kg for 50,000-tow pitch carbon fiber with a production volume of 3750 MT/year. The estimated carbon fiber cost was significantly lower than the current commercially available PAN-based carbon fiber price ($\$$20–$\$$30/kg). With decant oil recycling rates of 50% and 70% in the solvent extraction process, the manufacturing cost of carbon fiber was estimated to be $\$$9.90/kg and $\$$9.50/kg of carbon fiber, respectively. A cradle-to-gate energy assessment revealed that carbon fiber derived from coal exhibited an embodied energy of 510 MJ/kg, significantly lower than that of conventionally produced carbon fiber from PAN. This notable difference is primarily attributed to the substantially higher conversion rate of coal-based mesophase pitch fibers into carbon fiber, surpassing PAN fibers by 1.6 times. These findings indicate that using coal for carbon fiber production through solvent extraction methods could offer a more energy-efficient and cost-competitive alternative to the traditional PAN based approach.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Digital Tools for the Preventive Conservation of Built Heritage: The Church of Santa Ana in Seville

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns.

Chaves, Estefanía↗
DOE OSTI2023

First measurement of the forward rapidity gap distribution in pPb collisions at $\sqrt{s_{NN}}$ = 8.16 TeV

For the first time at LHC energies, the forward rapidity gap spectra from proton-lead collisions for both proton and lead dissociation processes are presented. The analysis is performed over 10.4 units of pseudorapidity at a center-of-mass energy per nucleon pair of $\sqrt{s_{NN}}$ = 8.16 TeV , almost 300 times higher than in previous measurements of diffractive production in proton-nucleus collisions. For lead dissociation processes, which correspond to the pomeron-lead event topology, the EPOS-LHC generator predictions are a factor of 2 below the data, but the model gives a reasonable description of the rapidity gap spectrum shape. For the pomeron-proton topology, the EPOS-LHC , QGSJET II , and HIJING predictions are all at least a factor of 5 lower than the data. The latter effect might be explained by a significant contribution of ultraperipheral photoproduction events mimicking the signature of diffractive processes. These data may be of significant help in understanding the high energy limit of quantum chromodynamics and for modeling cosmic ray air showers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Simulations of Events for the LUX-ZEPLIN (LZ) Dark Matter Experiment

The LUX-ZEPLIN dark matter search aims to achieve a sensitivity to the WIMP-nucleon spin-independent cross-section down to (1–2) ×10−12 pb at a WIMP mass of 40 GeV/ c 2 . This paper describes the simulations framework that, along with radioactivity measurements, was used to support this projection, and also to provide mock data for validating reconstruction and analysis software. Of particular note are the event generators, which allow us to model the background radiation, and the detector response physics used in the production of raw signals, which can be converted into digitized waveforms similar to data from the operational detector. Inclusion of the detector response allows us to process simulated data using the same analysis routines as developed to process the experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Status Report on Thermal Extraction Modeling within HYBRID

Idaho National Laboratory continues to be at the forefront of advanced reactor systems and integrated energy systems (IES) research. The HYBRID modeling repository is the computational modeling repository used to evaluate potential IES configurations. To support IES analysis, it is necessary to develop models that can evaluate a range of thermal integration points within given energy conversion systems. This work includes as many technologies as possible anticipating that many technologies will be deployed to broadly reduce climate impacts of energy intensive processes. To provide this analysis, a new balance of plant model has been generated. This report will discuss progress on this new main model and discuss nominal conditions for the primary reactor types contained within HYBRID.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Hydrothermal Liquefaction and Upgrading of Wastewater-Grown Microalgae: 2021 State of Technology

The fiscal year (FY) 2021 State of Technology (SOT) Assessment for the hydrothermal liquefaction (HTL) of wastewater (WW)-grown microalgae and biocrude upgrading system was completed and reported here. An industrial partner, Gross-Wen Technologies (GWT), provided algae feedstock cultivated on a revolving algal biofilm (RAB) system by using the primary effluent from a water resource recovery facility (WRRF). This provided algae was tested at PNNL for HTL processing. The experimental results provided the major design basis of the HTL process of the SOT baseline case. The primary effluent of the Metropolitan Water Reclamation District (MWRD) of Greater Chicago was assumed to be the nutrients source for algae growth and the algae yield data per gallon wastewater provided by GWT were used to estimate the total algae production rate and thus the HTL conversion plant scale. Considering different cultivation technologies and wastewater streams with different flow rates and nutrients contents can be used to produce algae, this SOT assessment just provided an example case study for WW-grown algae based HTL conversion to fuels systems. A preliminary economic analysis was developed based on process simulation results. Sensitivity analysis was implemented to evaluate cost impacts of plant scales, potential cost improvements and other key factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
DOE OSTI2024

Distinguishing the XUV-induced Coulomb explosion dynamics of iodobenzene using covariance analysis

The primary and secondary fragmentation dynamics of iodobenzene following its ionization at 120 eV were determined using three-dimensional velocity map imaging and covariance analysis. Site-selective iodine 4d ionization was used to populate a range of excited polycationic parent states, which primarily broke apart at the carbon-iodine bond to produce I+ with phenyl or phenyl-like cations (C n H x + or C n H, with n = 1 – 6 and x = 1 – 5). The molecular products were produced with varying degrees of internal excitation and dehydrogenation, leading to stable and unstable outcomes. This further allowed the secondary dynamics of C 6 H x 2+ intermediates to be distinguished using native-frame covariance analysis, which isolated these processes in their own centre-of-mass reference frames. Furthermore, the mass resolution of the imaging mass spectrometer used for these measurements enabled the primary and secondary reaction channels to be specified at the level of individual hydrogen atoms, demonstrating the ability of covariance analysis to comprehensively measure the competing fragmentation channels of aryl cations, including those involving intermediate steps.

74 ATOMIC AND MOLECULAR PHYSICS↗