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All sources NASA NTRS DOE OSTI

DOE OSTI2019

A high-speed, high-performance, microfabricated comprehensive two-dimensional gas chromatograph

A small, consumable-free, low-power, ultra-high-speed comprehensive GC×GC system consisting of microfabricated columns, nanoelectromechanical system (NEMS) cantilever resonators for detection, and a valve-based stop-flow modulator is demonstrated. The separation of a highly polar 29-component mixture covering a boiling point range of 46 to 253 °C on a pair of microfabricated columns using a Staiger valve manifold in less than 7 seconds, and just over 4 seconds after the ensemble holdup time is demonstrated with a downstream FID. The analysis time of the second dimension was 160 ms, and peak widths in the second dimension range from 10–60 ms. A peak capacity of just over 300 was calculated for a separation of just over 6 s. Data from a continuous operation testing over 40 days and 20 000 runs of the GC×GC columns with the NEMS resonators using a 4-component test set is presented. As a result, the GC×GC-NEMS resonator system generated second-dimension peak widths as narrow as 8 ms with no discernable peak distortion due to under-sampling from the detector.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

AHF: Array-Based Half-Facet Data Structure for Mixed-Dimensional and Non-Manifold Meshes

We present an Array-based Half-Facet mesh data structure, or AHF, for efficient mesh query and modification operations. The AHF extends the compact array-based half-edge and half-face data structures (T.J. Alumbaugh and X. Jiao, Compact array-based mesh data structures, IMR, 2005) to support mixed-dimensional and non-manifold meshes. The design goals of our data structure include generality to support such meshes, efficiency of neighborhood queries and mesh modification, compactness of memory footprint, and facilitation of interoperability of mesh-based application codes. To accomplish these goals, our data structure uses sibling half-facets as a core abstraction, coupled with other explicit and implicit representations of entities. A unique feature of our data structure is a comprehensive implementation in MATLAB, which allows rapid prototyping, debugging, testing, and deployment of meshing algorithms and other mesh-based numerical methods. We have also developed C++ implementation built on top of MOAB (T.J. Tautges, R. Meyers, and K. Merkley, MOAB: A Mesh-Oriented Database, Sandia National Laboratories, 2004). We present some comparisons of the memory requirements and computational costs, and also demonstrate its effectiveness with a few sample applications.

Dyedov, Volodymyr↗

DOE OSTI2015

Extensive sensitivity study of PWR fuel assay data - 14301

Accurate calculations of the isotopic composition of spent nuclear fuel play a fundamental role in several parts of nuclear energy. An important example is the use of burnup credit in criticality safety analysis, where the results gained from depletion calculations are used to determine the fuel composition in subsequent criticality calculations. The work presented here is a sensitivity study for depletion calculations of high-burnup fuel samples from the Vandellos II PWR that entails an unusually detailed evaluation of assay data. In addition to investigating the impacts of experimental uncertainties on the calculated isotopic composition, the consequences of various modelling approaches are studied to give quantitative estimations of the impact on accuracy. Furthermore, the reactivity of the calculated fuel composition is tracked, because this is the integral characteristic of the irradiated fuel that one is actually interested in for burnup credit applications. While the sensitivity study is performed only for one of the nine experimental samples of the Vandellos II assay data, the lessons learned lead to evaluation models with different levels of model detail for all fuel samples. The results show that the discrepancies between the measured and calculated isotopic compositions are considerably larger than the uncertainties inherent in the measurement and the calculation method would indicate. This is not unexpected, since large discrepancies are a common result of assay data recalculations. However, the unusually extensive sensitivity study performed here allows to exclude some possible sources of error: since the influence of experimental uncertainties like fuel assembly geometry and reactor parameters on the isotopic composition is shown to be small, one valuable conclusion of this study is to focus more on the measurement uncertainties of the chemical assay itself, and the nuclear data that the calculation is based upon. The study also demonstrates that using the reactivity of the depleted fuel as an additional comparison point between measurement and calculation can provide some orientation regarding the importance of the manifold influences on calculation results. Tracking this integral characteristic of the depleted fuel is very helpful in focusing on the actual impact of the depletion calculation uncertainties on criticality safety. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Data-Driven Compositional Optimization in Misspecified Regimes

With a manifold growth in the scale and intricacy of systems, the challenges of parametric misspecification become pronounced. These concerns are further exacerbated in compositional settings, which emerge in problems complicated by modeling risk and robustness. In “Data-Driven Compositional Optimization in Misspecified Regimes,” the authors consider the resolution of compositional stochastic optimization problems, plagued by parametric misspecification. In considering settings where such misspecification may be resolved via a parallel learning process, the authors develop schemes that can contend with diverse forms of risk, dynamics, and nonconvexity. They provide asymptotic and rate guarantees for unaccelerated and accelerated schemes for convex, strongly convex, and nonconvex problems in a two-level regime with extensions to the multilevel setting. Surprisingly, the nonasymptotic rate guarantees show no degradation from the rate statements obtained in a correctly specified regime and the schemes achieve optimal (or near-optimal) sample complexities for general T-level strongly convex and nonconvex compositional problems.

Business & Economics↗

DOE OSTI2019

Resolving Local Configurational Contributions to X-ray and Neutron Radial Distribution Functions Within Solutions of Concentrated Electrolytes – A Case Study of Concentrated NaOH

Extreme conditions of complex materials often lead to a manifold of local environments that challenge characterization and require new advances at the intersection of modern experimental and theoretical techniques. In this contribution, highly caustic and viscous aqueous NaOD solutions were characterized with a combination of X-ray and neutron radial distribution function (RDF) analyses, molecular dynamics simulations and sub-ensemble analysis. The interpretation of the experimental RDFs extends beyond the traditional understanding of atom-pair interactions and to the individual contributions of specific molecular-scale configurations (below 3.5 Å). This integrated approach yields unique insight into the complex and significant breath of ion solvation and pairing structural motifs present, the experimental sensitivity of RDFs to changes in local geometries, and the challenge of experimentally differentiating the ensemble of all superimposed local environments — a feature of increasing importance within the extreme condition of high ionic strength. This study was facilitated by the development of a chemically resistant sample cell for in-situ neutron total scattering measurements of highly caustic materials at elevated temperature and pressure conditions. Advancing these integrated capabilities is critical for ongoing model validation efforts and for extending the predictive capabilities needed for the extremes of chemical composition, aiding a variety of industrial processes that include advanced nuclear waste remediation and metal refining strategies.

Semrouni, David↗

DOE OSTI2014

New Co–Pd–Zn γ-Brasses with Dilute Ferrimagnetism and Co 2 Zn 11 Revisited: Establishing the Synergism between Theory and Experiment

A synergism between electronic structure theory and the targeted synthesis of new ternary γ-brass compounds is demonstrated in the Co–Zn system. Co 2 Zn 11 , which adopts a cubic γ-brass structure, is shown to be at the Zn-rich end of a homogeneity range that varies from 15.4 to 22.1 atom % Co. Four samples were examined by single-crystal diffraction, all of which crystallize in space group I43¯m with the lattice parameter ranging from 8.9851(1) to 8.8809(1) Å as the Co content increases. In the 26-atom γ-brass clusters, Co atoms preferentially occupy the outer tetrahedron (OT) sites and then replace Zn atoms at the octahedron (OH) sites at higher Co concentrations. In addition, a small fraction of vacancies occurs on the inner tetrahedron (IT) sites. The electronic structure of Co 2 Zn 11 shows two distinct pseudogaps near the Fermi level: one at 292 valence electrons per primitive unit cell and the other at 302–304 valence electrons per primitive unit cell. Using molecular orbital arguments applied to the body-centered cubic packing of the 26-atom Co 4 Zn 22 γ-brass cluster, these pseudogaps arise from (i) splitting among the valence s and p orbitals, which gives rise to the Hume–Rothery electron counting rule, and (ii) splitting within the manifold of Co 3d orbitals via Co–Zn orbital interactions. Co 2 Zn 11 is Pauli paramagnetic, although the density of states at the Fermi level is large, whereas Curie–Weiss behavior emerges for higher Co concentrations. Because Pd has a size and an electronegativity similar to those of Zn, and inspired by the pseudogaps in the electronic density of states curve of Co 2 Zn 11 , Pd-doped γ-brass compounds were designed and two new γ-brass compounds were obtained: Co 0.92(2) Pd 1.08 Zn 11 and Co 2.50(1) Pd 2.50 Zn 8 . In these, the site preferences for Co and Pd can be rationalized by electronic structure calculations. The densities of states indicate that Co 3d states are the major contributors near their Fermi levels, with the Pd 4d band lying ~2–3 eV below this. The magnetic properties of the Co–Pd–Zn γ-brasses are quite different from those of Co 2 Zn 11 : a giant magnetic moment on the Co atom is induced by the Pd atom, and Co 2.50(1) Pd 2.50 Zn 8 shows magnetization consistent with a dilute ferrimagnet. The results of first-principles calculations on two different models of the 26-atom γ-brass clusters indicate that intracluster Co–Co exchange is ferromagnetic, whereas intercluster Co–Co exchange is antiferromagnetic. These different magnetic exchange interactions provide rationalization for the high-temperature magnetization behavior of Co 2.50(1) Pd 2.50 Zn 8 .

36 MATERIALS SCIENCE↗

DOE OSTI2018

Addressing Research and Development Gaps for Plasma-Material Interactions with Linear Plasma Devices

Plasma-material interactions in future fusion reactors have been identified as a knowledge gap to be dealt with before any next step device past ITER can be built. The challenges are manifold. They are related to power dissipation so that the heat fluxes to the plasma-facing components can be kept at technologically feasible levels; maximization of the lifetime of divertor plasma-facing components that allow for steadystate operation in a reactor to reach the neutron fluence required; the tritium inventory (storage) in the plasma-facing components, which can lead to potential safety concerns and reduction in the fuel efficiency; and it is related to the technology of the plasma-facing components itself, which should demonstrate structural integrity under the high temperatures and high neutron fluence. While the dissipation of power exhaust can and should be addressed in high power toroidal devices, the interaction of the plasma with the materials can be best addressed in dedicated linear devices due to their cost effectiveness and ability to address urgent research and development gaps more timely. However, new linear plasma devices are needed to investigate the PMI under fusion reactor conditions and test novel plasma-facing components. Existing linear devices are limited either in their flux, their reactor-relevant plasma transport regimes in front of the target, their fluence, or their ability to test material samples a priori exposed to high neutron fluence. The proposed Material Plasma Exposure eXperiment (MPEX) is meant to address those deficiencies and will be designed to fulfill the fusion reactor-relevant plasma parameters as well as the ability to expose a priori neutron activated materials to plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

MORFOMETRYKA—A NEW WAY OF ESTABLISHING MORPHOLOGICAL CLASSIFICATION OF GALAXIES

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C{sub 1}, Asymmetry A{sub 3}, and Smoothness S{sub 3}), and the new parameters entropy, H, and spirality σ{sub ψ}. The new parameters A{sub 3}, S{sub 3}, and H are better to discriminate galaxy classes than A{sub 1}, S{sub 1}, and G, respectively. The new parameter σ{sub ψ} captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from the Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair and Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index (M{sub i}) and we show that it follows the human based T-type index very closely. We calculate morphometric index M{sub i} for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how M{sub i} correlates with stellar population parameters obtained using the spectra available from SDSS–DR7.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Ru nucleation and thin film smoothness improvement with ammonia during chemical vapor deposition

This study reports the use of ammonia to inhibit the growth of previously nucleated ruthenium islands and force the nucleation of additional islands such that thinner films form as the islands coalesce with continued growth. Ruthenium films are grown at 448 K in a chemical vapor deposition process on SiO{sub 2}/Si(001) using triruthenium dodecacarbonyl, Ru{sub 3}(CO){sub 12}, with and without a constant partial pressure of ammonia. Film growth was performed at a Ru{sub 3}(CO){sub 12}/Ar pressure of 47.2 mTorr. The ammonia partial pressure varied from 0 to 27.8 mTorr. X-ray photoelectron spectroscopy was used to analyze the samples in situ. Ex situ characterization included scanning electron microscopy, atomic force microscopy, and x-ray diffraction and x-ray reflectivity. Nucleation studies limited to the first 10 min of growth revealed the maximum nanoparticle (island) density of 8.1 × 10{sup 11 }cm{sup −2} occurred at an intermediate ammonia pressure (5.25 mTorr) compared to a density of 3.1 × 10{sup 11 }cm{sup −2} for no ammonia addition. Extending film growth to 120 min and varying the ammonia partial pressure during the first 10 min followed by 5.25 mTorr ammonia pressure for the final 110 min reveals the importance of nucleation on film smoothness. A model describing the inhibition effects of ammonia during nucleation and growth is presented.

36 MATERIALS SCIENCE↗

NASA NTRS2016

Biomolecular Analysis Capability for Cellular and Omics Research on the International Space Station

International Space Station (ISS) assembly complete ushered a new era focused on utilization of this state-of-the-art orbiting laboratory to advance science and technology research in a wide array of disciplines, with benefits to Earth and space exploration. ISS enabling capability for research in cellular and molecular biology includes equipment for in situ, on-orbit analysis of biomolecules. Applications of this growing capability range from biomedicine and biotechnology to the emerging field of Omics. For example, Biomolecule Sequencer is a space-based miniature DNA sequencer that provides nucleotide sequence data for entire samples, which may be used for purposes such as microorganism identification and astrobiology. It complements the use of WetLab-2 SmartCycler"TradeMark", which extracts RNA and provides real-time quantitative gene expression data analysis from biospecimens sampled or cultured onboard the ISS, for downlink to ground investigators, with applications ranging from clinical tissue evaluation to multigenerational assessment of organismal alterations. And the Genes in Space-1 investigation, aimed at examining epigenetic changes, employs polymerase chain reaction to detect immune system alterations. In addition, an increasing assortment of tools to visualize the subcellular distribution of tagged macromolecules is becoming available onboard the ISS. For instance, the NASA LMM (Light Microscopy Module) is a flexible light microscopy imaging facility that enables imaging of physical and biological microscopic phenomena in microgravity. Another light microscopy system modified for use in space to image life sciences payloads is initially used by the Heart Cells investigation ("Effects of Microgravity on Stem Cell-Derived Cardiomyocytes for Human Cardiovascular Disease Modeling and Drug Discovery"). Also, the JAXA Microscope system can perform remotely controllable light, phase-contrast, and fluorescent observations. And upcoming confocal microscopy capability will allow for optical sectioning of biological tissues to determine microanatomical localization of biomarkers. Furthermore, NASA's geneLAB effort addresses integration of genomic, epigenomic, transcriptomic, proteomic and metabolomic datasets, by applying an innovative open source science platform for multi-investigator high throughput utilization of the ISS. In sum, the expanding ISS capability for analysis of biomolecules is enabling innovative research in a broad spectrum of areas such as cellular and molecular biology, biotechnology, tissue engineering, biomedicine, and Omics, providing manifold benefits for humanity.

Guinart-Ramirez, Y.↗

DOE OSTI2015

A catalog of visual-like morphologies in the 5 CANDELS fields using deep learning

We present a catalog of visual-like H-band morphologies of ∼50.000 galaxies (H{sub f160w} < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS, and COSMOS). Morphologies are estimated using Convolutional Neural Networks (ConvNets). The median redshift of the sample is 〈z〉∼1.25. The algorithm is trained on GOODS-S, for which visual classifications are publicly available, and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves for each galaxy the probabilities of having a spheroid or a disk, presenting an irregularity, being compact or a point source, and being unclassifiable. ConvNets are able to predict the fractions of votes given to a galaxy image with zero bias and ∼10% scatter. The fraction of mis-classifications is less than 1%. Our classification scheme represents a major improvement with respect to Concentration-Asymmetry-Smoothness-based methods, which hit a 20%–30% contamination limit at high z. The catalog is released with the present paper via the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow-navigator-public/).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

The galactic globular cluster system

We explore correlations between various properties of Galactic globular clusters, using a database on 143 objects. Our goal is identify correlations and trends which can be used to test and constrain theoretical models of cluster formation and evolution. We use a set of 13 cluster parameters, 9 of which are independently measured. Several arguments suggest that the number of clusters still missing in the obscured regions of the Galaxy is of the order of 10, and thus the selection effects are probably not severe for our sample. Known clusters follow a power-law density distribution with a slope approximately -3.5 to -4, and an apparent core with a core radius approximately 1 kpc. Clusters show a large dynamical range in many of their properties, more so for the core parameters (which are presumably more affected by dynamical evolution) than for the half-light parameters. There are no good correlations with luminosity, although more luminous clusters tend to be more concentrated. When data are binned in luminosity, several trends emerge: more luminous clusters tend to have smaller and denser cores. We interpret this as a differential survival effect, with more massive clusters surviving longer and reaching more evolved dynamical states. Cluster core parameters and concentrations also correlate with the position in the Galaxy, with clusters closer to the Galactic center or plane being more concentrated and having smaller and denser cores. These trends are more pronounced for the fainter (less massive) clusters. This is in agreement with a picture where tidal shocks form disk or bulge passages accelerate dynamical evolution of clusters. Cluster metallicities do not correlate with any other parameter, including luminosity and velocity dispersion; the only detectable trend is with the position in the Galaxy, probably reflecting Zinn's disk-halo dichotomy. This suggests that globular clusters were not self-enriched systems. Velocity dispersions show excellent correlations with luminosity and surface brightness. Their origin is not well understood, but they may well reflect initial conditions of cluster formation, and perhaps even be used to probe the initial density perturbation spectrum on a approximately 10(exp 6) solar mass scale. Core radii and concentrations play a role of a 'second parameter' in these correlations. While a global manifold of cluster properties has a high statistical dimensionality (D greater than 4), a subset of structural, photometric, and dynamical parameters forms a statistically three-dimensional family, as expected from objects following King models; we propose to call this set of quantities the King Manifold. Some of the observed correlations may be usable as distance indicator relations for globular clusters.

Djorgovski, S.↗

DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗

DOE OSTI2019

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Colby, Sean M↗

DOE OSTI2016

Combined theoretical and experimental study of the valence, Rydberg, and ionic states of chlorobenzene

New photoelectron (PE) and ultra violet (UV) and vacuum UV (VUV) spectra have been obtained for chlorobenzene by synchrotron study with higher sensitivity and resolution than previous work and are subjected to detailed analysis. In addition, we report on the mass-resolved (2 + 1) resonance enhanced multiphoton ionization (REMPI) spectra of a jet-cooled sample. Both the VUV and REMPI spectra have enabled identification of a considerable number of Rydberg states for the first time. The use of ab initio calculations, which include both multi-reference multi-root doubles and singles configuration interaction (MRD-CI) and time dependent density functional theoretical (TDDFT) methods, has led to major advances in interpretation of the vibrational structure of the ionic and electronically excited states. Franck-Condon (FC) analyses of the PE spectra, including both hot and cold bands, indicate much more complex envelopes than previously thought. The sequence of ionic states can be best interpreted by our multi-configuration self-consistent field computations and also by comparison of the calculated vibrational structure of the B and C ionic states with experiment; these conclusions suggest that the leading sequence is the same as that of iodobenzene and bromobenzene, namely: X{sup 2}B{sub 1}(3b{sub 1}{sup −1}) < A{sup 2}A{sub 2}(1a{sub 2}{sup −1}) < B{sup 2}B{sub 2}(6b{sub 2}{sup −1}) < C{sup 2}B{sub 1}(2b{sub 1}{sup −1}). The absorption onset near 4.6 eV has been investigated using MRD-CI and TDDFT calculations; the principal component of this band is {sup 1}B{sub 2} and an interpretation based on the superposition of FC and Herzberg-Teller contributions has been performed. The other low-lying absorption band near 5.8 eV is dominated by a {sup 1}A{sub 1} state, but an underlying weak {sup 1}B{sub 1} state (πσ{sup ∗}) is also found. The strongest band in the VUV spectrum near 6.7 eV is poorly resolved and is analyzed in terms of two ππ{sup ∗} states of {sup 1}A{sub 1} (higher oscillator strength) and {sup 1}B{sub 2} (lower oscillator strength) symmetries, respectively. The calculated vertical excitation energies of these two states are critically dependent upon the presence of Rydberg functions in the basis set, since both manifolds are strongly perturbed by the Rydberg states in this energy range. A number of equilibrium structures of the ionic and singlet excited states show that the molecular structure is less subject to variation than corresponding studies for iodobenzene and bromobenzene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗