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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 OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2021

Synthesis, structure, electronic characterization, and halogenation of gold(III) phlorin complexes

The metalation chemistry of the phlorin, which is a non-aromatic tetrapyrrole macrocycle containing a single sp 3 -hybridized meso-carbon has remained underdeveloped, as compared to that of more traditional tetrapyrroles such as porphyrins, corroles and phthalocyanines. There have been few prior efforts to prepare metallophlorins, and those that have been reported have relied on either reduction or nucleophilic attack of parent metalloporphyrins, rather than direct metalation of freebase phlorin constructs. In this work, an alternate synthetic approach for preparation of gold(III) phlorin complexes that involves the first direct metalation of two different freebase phlorin derivatives (3H(Phl tBu ) and 3H(Phl F )) with AuBr 3 to produce the stable and fully isolable gold(III) phlorin complexes Au(Phl tBu ) and Au(Phl F ) is reported. The first structural characterization of a metallophlorin bearing geminal dimethyl substituents at the sp 3 -hybridized meso-carbon via X-ray crystallography is also reported. In addition to the preparation of Au(Phl tBu ) and Au(Phl F ), the UV-vis absorption and redox properties of these two gold(III) phlorins in comparison to those of their freebase homologues is also detailed. Notably, the metallophlorins are characterized by panchromatic absorbance profiles and intense and broad bands that span the long-visible and into the near-IR regions, as well as two fully reversible oxidation and reduction waves as probed by cyclic voltammetry. Lastly, the chlorination of Au(Phl tBu ) using PhI(Cl 2 ) was probed and it was found that this regioslective reaction generates monochlorinated (Au(Phl tBu Cl)) and dichlorinated (Au(Phl tBu Cl 2 )) products, which were both structurally characterized by X-ray crystallography.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

AstraAI v1

AstraAI is an open-source, structure-aware AI coding agent designed for large scientific and DOE-HPC codebases such as AMReX-based applications. Unlike general-purpose coding assistants, AstraAI combines retrieval-augmented generation (RAG) with compiler-level Abstract Syntax Tree (AST) analysis to perform precise, scope-constrained code modifications. It identifies exact function spans, enforces locality of edits, and maintains cross-file invariants, enabling deterministic and build-safe transformations in complex C++/GPU environments. AstraAI is intended for developers working on large, evolving HPC frameworks where correctness, reproducibility, and structural integrity are critical. Typical use cases include modifying physics kernels, updating GPU device lambdas, and performing multi-file refactors without breaking compilation or runtime semantics. Compared to conventional LLM-based coding agents - even those with repository access - AstraAI provides structural guarantees rather than free-form text patches. It minimizes unintended diffs, prevents scope drift, preserves formatting and build stability, and reduces structural hallucinations. By integrating compiler tooling directly into the generation loop, AstraAI transforms AI-assisted coding from probabilistic text editing into deterministic, structure-preserving program transformation suitable for mission-critical scientific software.

Natarajan, Mahesh [Lawrence Berkeley National Labo↗
NASA NTRS1990

Thermal stress analysis of the NASA Dryden hypersonic wing test structure

Present interest in hypersonic vehicles has resulted in a renewed interest in thermal stress analysis of airframe structures. While there are numerous texts and papers on thermal stress analysis, practical examples and experience on light gage aircraft structures are fairly limited. A research program has been undertaken at General Dynamics to demonstrate the present state of the art, verify methods of analysis, gain experience in their use, and develop engineering judgement in thermal stress analysis. The approach for this project has been to conduct a series of analyses of this sample problem and compare analysis results with test data. This comparison will give an idea of how to use our present methods of thermal stress analysis, and how accurate we can expect them to be.

Morris, Glenn↗
NASA NTRS2003

XML-Based Generator of C++ Code for Integration With GUIs

An open source computer program has been developed to satisfy a need for simplified organization of structured input data for scientific simulation programs. Typically, such input data are parsed in from a flat American Standard Code for Information Interchange (ASCII) text file into computational data structures. Also typically, when a graphical user interface (GUI) is used, there is a need to completely duplicate the input information while providing it to a user in a more structured form. Heretofore, the duplication of the input information has entailed duplication of software efforts and increases in susceptibility to software errors because of the concomitant need to maintain two independent input-handling mechanisms. The present program implements a method in which the input data for a simulation program are completely specified in an Extensible Markup Language (XML)-based text file. The key benefit for XML is storing input data in a structured manner. More importantly, XML allows not just storing of data but also describing what each of the data items are. That XML file contains information useful for rendering the data by other applications. It also then generates data structures in the C++ language that are to be used in the simulation program. In this method, all input data are specified in one place only, and it is easy to integrate the data structures into both the simulation program and the GUI. XML-to-C is useful in two ways: 1. As an executable, it generates the corresponding C++ classes and 2. As a library, it automatically fills the objects with the input data values.

Hua, Hook↗
NASA NTRS2013

Adaptive Distributed Environment for Procedure Training (ADEPT)

ADEPT (Adaptive Distributed Environment for Procedure Training) is designed to provide more effective, flexible, and portable training for NASA systems controllers. When creating a training scenario, an exercise author can specify a representative rationale structure using the graphical user interface, annotating the results with instructional texts where needed. The author's structure may distinguish between essential and optional parts of the rationale, and may also include "red herrings" - hypotheses that are essential to consider, until evidence and reasoning allow them to be ruled out. The system is built from pre-existing components, including Stottler Henke's SimVentive instructional simulation authoring tool and runtime. To that, a capability was added to author and exploit explicit control decision rationale representations. ADEPT uses SimVentive's Scalable Vector Graphics (SVG)- based interactive graphic display capability as the basis of the tool for quickly noting aspects of decision rationale in graph form. The ADEPT prototype is built in Java, and will run on any computer using Windows, MacOS, or Linux. No special peripheral equipment is required. The software enables a style of student/ tutor interaction focused on the reasoning behind systems control behavior that better mimics proven Socratic human tutoring behaviors for highly cognitive skills. It supports fast, easy, and convenient authoring of such tutoring behaviors, allowing specification of detailed scenario-specific, but content-sensitive, high-quality tutor hints and feedback. The system places relatively light data-entry demands on the student to enable its rationale-centered discussions, and provides a support mechanism for fostering coherence in the student/ tutor dialog by including focusing, sequencing, and utterance tuning mechanisms intended to better fit tutor hints and feedback into the ongoing context.

Domeshek, Eric↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Nanoindentation of HMX and Idoxuridine to Determine Mechanical Similarity

Assessing the mechanical behavior (elastic properties, plastic properties, and fracture phenomena) of molecular crystals is often complicated by the difficulty in preparing samples. Pharmaceuticals and energetic materials in particular are often used in composite structures or tablets, where the individual grains can strongly impact the solid behavior. Nanoindentation is a convenient method to experimentally assess these properties, and it is used here to demonstrate the similarity in the mechanical properties of two distinct systems: individual crystals of the explosive cyclotetramethylene tetranitramine (HMX) and the pharmaceutical idoxuridine were tested in their as-precipitated state, and the effective average modulus and hardness (which can be orientation dependent) were determined. Both exhibit a hardness of 1.0 GPa, with an effective reduced modulus of 25 and 23 GPa for the HMX and idoxuridine, respectively. They also exhibit similar yield point behavior. This indicates idoxuridine may be a suitable mechanical surrogate (or “mock”) for HMX. While the methodology to assess elastic and plastic properties was relatively insensitive to specific crystal orientation (i.e., a uniform distribution in properties was observed for all random crystals tested), the indentation-induced fracture properties appear to be much more sensitive to tip-crystal orientation, and an unloading slope analysis is used to demonstrate the need for further refinement in relating toughness to orientation in these materials with relatively complex slip systems and crystal structures. View Full-Text

36 MATERIALS SCIENCE↗
NASA NTRS1994

The Buffer Diagnostic Prototype: A fault isolation application using CLIPS

This paper describes problem domain characteristics and development experiences from using CLIPS 6.0 in a proof-of-concept troubleshooting application called the Buffer Diagnostic Prototype. The problem domain is a large digital communications subsystems called the real-time network (RTN), which was designed to upgrade the launch processing system used for shuttle support at KSC. The RTN enables up to 255 computers to share 50,000 data points with millisecond response times. The RTN's extensive built-in test capability but lack of any automatic fault isolation capability presents a unique opportunity for a diagnostic expert system application. The Buffer Diagnostic Prototype addresses RTN diagnosis with a multiple strategy approach. A novel technique called 'faulty causality' employs inexact qualitative models to process test results. Experimental knowledge provides a capability to recognize symptom-fault associations. The implementation utilizes rule-based and procedural programming techniques, including a goal-directed control structure and simple text-based generic user interface that may be reusable for other rapid prototyping applications. Although limited in scope, this project demonstrates a diagnostic approach that may be adapted to troubleshoot a broad range of equipment.

Porter, Ken↗
NASA NTRS2010

DigitalHuman (DH): An Integrative Mathematical Model ofHuman Physiology

Mathematical models and simulation are important tools in discovering the key causal relationships governing physiological processes and improving medical intervention when physiological complexity is a central issue. We have developed a model of integrative human physiology called DigitalHuman (DH) consisting of -5000 variables modeling human physiology describing cardiovascular, renal, respiratory, endocrine, neural and metabolic physiology. Users can view time-dependent solutions and interactively introduce perturbations by altering numerical parameters to investigate new hypotheses. The variables, parameters and quantitative relationships as well as all other model details are described in XML text files. All aspects of the model, including the mathematical equations describing the physiological processes are written in XML open source, text-readable files. Model structure is based upon empirical data of physiological responses documented within the peer-reviewed literature. The model can be used to understand proposed physiological mechanisms and physiological interactions that may not be otherwise intUitively evident. Some of the current uses of this model include the analyses of renal control of blood pressure, the central role of the liver in creating and maintaining insulin resistance, and the mechanisms causing orthostatic hypotension in astronauts. Additionally the open source aspect of the modeling environment allows any investigator to add detailed descriptions of human physiology to test new concepts. The model accurately predicts both qualitative and more importantly quantitative changes in clinically and experimentally observed responses. DigitalHuman provides scientists a modeling environment to understand the complex interactions of integrative physiology. This research was supported by.NIH HL 51971, NSF EPSCoR, and NASA

Hester, Robert L.↗
DOE OSTI2026

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2012

Vibration mitigation in J-TEXT far-infrared diagnostic systems

Optical structure stability is an important issue for far-infrared (FIR) phase measurements. To ensure good signal quality, influence of vibration should be minimized. Mechanical amelioration and optical optimization can be taken in turn to decrease vibration's influence and ensure acceptable measurement. J-TEXT (Joint Texal Experiment Tokamak, formerly TEXT-U) has two FIR diagnostic systems: a HCN interferometer system for electron density measurement and a three-wave polarimeter-interferometer system (POLARIS) for electron density and Faraday effect measurements. All use phase detection techniques. HCN interferometer system has almost eliminated the influence of vibration after mechanical amelioration and optical optimization. POLARIS also obtained first experimental results after mechanical stability improvements and is expected to further reduce vibration's influence on Faraday angle to 0.1 Degree-Sign after optical optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2017

Creative Analytics of Mission Ops Event Messages

Historically, tremendous effort has been put into processing and displaying mission health and safety telemetry data; and relatively little attention has been paid to extracting information from missions time-tagged event log messages. Todays missions may log tens of thousands of messages per day and the numbers are expected to dramatically increase as satellite fleets and constellations are launched, as security monitoring continues to evolve, and as the overall complexity of ground system operations increases. The logs may contain information about orbital events, scheduled and actual observations, device status and anomalies, when operators were logged on, when commands were resent, when there were data drop outs or system failures, and much much more. When dealing with distributed space missions or operational fleets, it becomes even more important to systematically analyze this data. Several advanced information systems technologies make it appropriate to now develop analytic capabilities which can increase mission situational awareness, reduce mission risk, enable better event-driven automation and cross-mission collaborations, and lead to improved operations strategies: Industry Standard for Log Messages. The Object Management Group (OMG) Space Domain Task Force (SDTF) standards organization is in the process of creating a formal standard for industry for event log messages. The format is based on work at NASA GSFC. Open System Architectures. The DoD, NASA, and others are moving towards common open system architectures for mission ground data systems based on work at NASA GSFC with the full support of the commercial product industry and major integration contractors. Text Analytics. A specific area of data analytics which applies statistical, linguistic, and structural techniques to extract and classify information from textual sources. This presentation describes work now underway at NASA to increase situational awareness through the collection of non-telemetry mission operations information into a common log format and then providing display and analytics tools to provide in-depth assessment of the log contents. The work includes: Common interface formats for acquiring time-tagged text messages Conversion of common files for schedules, orbital events, and stored commands to the common log format Innovative displays to depict thousands of messages on a single display Structured English text queries against the log message data store, extensible to a more mature natural language query capability Goal of speech-to-text and text-to-speech additions to create a personal mission operations assistant to aid on-console operations. A wide variety of planned uses identified by the mission operations teams will be discussed.

events↗
DOE OSTI2015

Designing a new structure for storing nuclear data: Progress of the Working Party for Evaluation Cooperation subgroup #38

An international effort is underway to design a new structure for storing and using nuclear reaction data, with the goal of eventually replacing the current standard, ENDF-6. This effort, organized by the Working Party for Evaluation Cooperation, was initiated in 2012 and has resulted in a list of requirements and specifications for how the proposed new structure shall perform. The new structure will take advantage of new developments in computational tools, using a nested hierarchy to store data. Here, the structure can be stored in text form (such as an XML file) for human readability and data sharing, or it can be stored in binary to optimize data access. In this paper, we present the progress towards completing the requirements, specifications and implementation of the new structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2019

Structure Deformation Calculation Program Based on Displacement Theory for Shape Predictions

Separated programs were written in C/C++ to validate the Displacement Transfer Functions. The Structure Deformation Calculation Program was written to combine all of the programs to calculate deformed shapes of a structure using surface strain data and structural geometrical parameters. Users do not need to know the material properties, nor the complex internal structures geometry because the Displacement Theory is purely geometrical in nature. Users only need to know the structure types as defined in this report and information such as the structure length, depth factors, number of strain sensors, and the surface strains measured at the strain-sensing stations installed on the structures. Depending on the structure type, an applicable Displacement Transfer Function will be used. This program requires two input files created by users; the recorded strain data file in comma-separated values format and the structure geometry data file in text format. The program will output the out-of-plane deflections, slopes, cross-sectional twist angles, and depth factors if applicable. All output files are created in comma-separated values format. A section in this report describes step-by-step procedures on how to use the Structure Deformation Calculation Program for structure deformed shape calculations.

Displacement theory↗
DOE OSTI2022

Photoelectron spectroscopy of cryogenically cooled NiO 2 – via slow photoelectron velocity-map imaging

In this work, high-resolution anion photoelectron spectra of cryogenically cooled NiO 2 – anions, obtained using slow photoelectron velocity-map imaging (cryo-SEVI), are presented in tandem with coupled cluster electronic structure calculations including relativistic effects. The experimental spectra encompass the $\tilde{\text{X}}^1Σ_{\text{g}}^+ ← \tilde{\text{X}}^2Π_{\text{g}}, ã^3Π{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$, and $\tilde{\text{A}}^1Π_{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$ photodetachment transitions of linear ONiO 0/– , revealing previously unobserved vibrational structure in all three electronic bands. The high-resolution afforded by cryo-SEVI allows for the extraction of vibrational frequencies for each state, consistent with those previously measured in the ground state and in good agreement with scalar-relativistic coupled-cluster calculations. Previously unobserved vibrational structure is observed in the $ã^3Π_{\text{g}}$ and $Ã^1Π_{\text{g}}$ states and is tentatively assigned. Further, a refined electron affinity of 3.0464(7) eV for NiO 2 is obtained as well as precise term energies for the ã and à states of NiO 2 of 0.3982(7) and 0.7422(10) eV, respectively. Numerous Franck–Condon forbidden transitions involving the doubly degenerate $ν_2$ bending mode are observed and ascribed to Herzberg-Teller coupling to an excited electronic state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗