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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

The 2.5-12 micrometers spectrum of comet Halley from the IKS-VEGA experiment

The infrared instrument IKS flown on board the VEGA space probes was designed for the detection of emission bands of parent molecules, and for a measurement of the size and temperature of the thermal emitting nuclear region. The instrument had three channels with cooled detectors: an "imaging channel" designed to modulate the signal of the nucleus and two spectroscopic channels operating at 2.5-5 and 6-12 micrometers, respectively, equipped with circular variable filters of resolving power approximately 50. This paper presents and discusses the results from the spectral channels. On VEGA 1, usable spectra were obtained at distances D from the comet nucleus ranging from 250,000 to 40,000 km corresponding to fields of view 4000 and 700 km in diameter, respectively. The important internal background signal caused by the instrument itself, which could not be cooled, had to be eliminated. Since no sky chopping was performed, we obtain difference spectra between the current spectrum and a reference spectrum with little or no cometary signal taken at the beginning of the observing sequence (D approximately 200,000 km). Final discrimination between cometary signal and instrumental background is achieved using their different time evolution, since the instrumental background is proportional to the slow temperature drift of the instrument, and the cometary signal due to parent molecules or dust grains is expected to vary in first order as D-1. The 2.5-5 micrometers IKS spectra definitely show strong narrow signals at 2.7 and 4.25 micrometers, attributed to the nu 3 vibrational bands of H2O and CO2, respectively, and a broader signal in the region 3.2-3.5 micrometers, which may be attributed to CH-bearing molecules. All these signals present the expected D-1 intensity variation. Weaker emission features at 3.6 and 4.7 micrometers could correspond to the nu 1 and nu 5 bands of H2CO and the (1 - 0) band of CO, respectively. Molecular production rates are derived from the observed emissions, assuming that they are due to resonance fluorescence excited by the Sun's infrared radiation. For the strong bands of H2O and CO2, the rovibrational lines are optically thick, and radiative transfer is taken into account. We derive production rates, at the moment of the VEGA 1 flyby, of approximately 10(30) sec-1 for H2O, approximately 2.7 x 10(28) sec-1 for CO2, approximately 5 x 10(28) sec-1 for CO, and 4 x 10(28) sec-1 for H2CO, if attributions to CO and H2CO are correct. The production rate of carbon atoms in CH-bearing molecules is approximately 9 x 10(29) sec-1 assuming fluorescence of molecules in the gas phase, but could be much less if the 3.2-3.5 micrometers emission is attributed to C-H stretch in polycyclic aromatic hydrocarbons or small organic grains. In addition, marginal features are present at 4.85 and 4.45 micrometers, tentatively attributed to OCS and molecules with the CN group, respectively. Broad absorption at 2.8-3.0 micrometers, as well as a narrow emission at 3.15 micrometers, which follow well the D-1 intensity variation, might be due to water ice. Emission at 2.8 micrometers is also possibly present, and might be due to OH created in vibrationally excited states after water photodissociation. The 6-12 micrometers spectrum does not show any molecular emission, nor emission in the 7.5-micrometers region. The spectrum is dominated by silicate emission showing a double structure with maxima at 9.0 and 11.2 micrometers, which suggests the presence of olivine.

NASA Discipline Number 52-10↗
NASA NTRS2009

Influence Diagram Use With Respect to Technology Planning and Investment

Influence diagrams are relatively simple, but powerful, tools for assessing the impact of choices or resource allocations on goals or requirements. They are very general and can be used on a wide range of problems. They can be used for any problem that has defined goals, a set of factors that influence the goals or the other factors, and a set of inputs. Influence diagrams show the relationship among a set of results and the attributes that influence them and the inputs that influence the attributes. If the results are goals or requirements of a program, then the influence diagram can be used to examine how the requirements are affected by changes to technology investment. This paper uses an example to show how to construct and interpret influence diagrams, how to assign weights to the inputs and attributes, how to assign weights to the transfer functions (influences), and how to calculate the resulting influences of the inputs on the results. A study is also presented as an example of how using influence diagrams can help in technology planning and investment. The Space Propulsion Synergy Team (SPST) used this technique to examine the impact of R&D spending on the Life Cycle Cost (LCC) of a space transportation system. The question addressed was the effect on the recurring and the non-recurring portions of LCC of the proportion of R&D resources spent to impact technology objectives versus the proportion spent to impact operational dependability objectives. The goals, attributes, and the inputs were established. All of the linkages (influences) were determined. The weighting of each of the attributes and each of the linkages was determined. Finally the inputs were varied and the impacts on the LCC determined and are presented. The paper discusses how each of these was accomplished both for credibility and as an example for future studies using influence diagrams for technology planning and investment planning.

Levack, Daniel J. H.↗
DOE OSTI2016

Point pattern match-based change detection in a constellation of previously detected objects

A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2016

Catchment influence on nitrate and dissolved organic matter in Alaskan streams across a latitudinal gradient

Spatial patterns in carbon (C) and nitrogen (N) cycles of high-latitude catchments have been linked to climate and permafrost and used to infer potential changes in biogeochemical cycles under climate warming. However, inconsistent spatial patterns across regions indicate that factors in addition to permafrost and regional climate may shape responses of C and N cycles to climate change. In this paper, we hypothesized that physical attributes of catchments modify responses of C and N cycles to climate and permafrost. We measured dissolved organic C (DOC) and nitrate (NO 3 ¯ ) concentrations, and composition of dissolved organic matter (DOM) in 21 streams spanning boreal to arctic Alaska, and assessed permafrost, topography, and attributes of soils and vegetation as predictors of stream chemistry. Multiple regression analyses indicated that catchment slope is a primary driver, with lower DOC and higher NO 3 ¯ concentration in streams draining steeper catchments, respectively. Depth of the active layer explained additional variation in concentration of DOC and NO 3 ¯ . Vegetation type explained regional variation in concentration and composition of DOM, which was characterized by optical methods. Composition of DOM was further correlated with attributes of soils, including moisture, temperature, and thickness of the organic layer. Finally, regional patterns of DOC and NO 3 ¯ concentrations in boreal to arctic Alaska were driven primarily by catchment topography and modified by permafrost, whereas composition of DOM was driven by attributes of soils and vegetation, suggesting that predicting changes to C and N cycling from permafrost-influenced regions should consider catchment setting in addition to dynamics of climate and permafrost.

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

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

The role of remote sensing in process-scaling studies of managed forest ecosystems

Sustaining forest resources requires a better understanding of forest ecosystem processes, and how management decisions and climate change may affect these processes in the future. While plot and inventory data provide our most detailed information on forest carbon, energy, and water cycling, applying this understanding to broader spatial and temporal domains requires scaling approaches. Remote sensing provides a powerful resource for "upscaling" process understanding to regional and continental domains. The increased range of available remote sensing modalities, including interferometric radar, lidar, and hyperspectral imagery, allows the retrieval of a broad range of forest attributes. This paper reviews the application of remote sensing for upscaling forest attributes from the plot scale to regional domains, with particular emphasis on how remote sensing products can support parameterization and validation of ecosystem process models. We focus on four key ecological attributes of forests: composition, structure, productivity and evapotranspiration, and disturbance dynamics. For each attribute, we discuss relevant remote sensing technologies, provide examples of their application, and critically evaluate both strengths and challenges associated with their use. Published by Elsevier B.V.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Evaluation of design parameters for TRISO-coated fuel particles to establish manufacturing critical limits using PARFUME

The success of modular high temperature gas-cooled reactors is highly dependent on the performance of the tristructural-isotopic (TRISO) coated fuel particle and the quality to which it can be manufactured. During irradiation, TRISO-coated fuel particles act as a pressure vessel to contain fission gas and mitigate the diffusion of fission products to the coolant boundary. The fuel specifications place limits on key attributes to minimize fuel particle failure under irradiation and postulated accident conditions. PARFUME (an integrated mechanistic coated particle fuel performance code developed at the Idaho National Laboratory) was used to calculate fuel particle failure probabilities. By systematically varying key TRISO-coated particle attributes, failure probability functions were developed to understand how each attribute contributes to fuel particle failure. Critical manufacturing limits were calculated for the key attributes of a low enriched TRISO-coated nuclear fuel particle with a kernel diameter of 425 μm. As a result, these critical manufacturing limits identify ranges beyond where an increase in fuel particle failure probability is expected to occur.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Elucidating Cycling Rate-Dependent Electrochemical Strains in Sodium Iron Phosphate Cathodes for Na-ion Batteries

Battery electrodes materials undergo significant mechanical instabilities which affects their longevity and exert rate-limitations during the cycling process. In this study, we investigate the rate-dependent mechanical response of sodium iron phosphate (NaFePO4, NFP) cathodes during Na intercalation via galvanostatic cycling at different rates by employing digital image correlation, electrochemical methods, and mathematical model. The mechanical behaviour of the electrode shows strong dependance on the applied scan rate. At slower rates, electrode shows asymmetrical strain generation between anodic and cathodic cycles, which is attributed to the formation of cathode-electrolyte interface layers. The electrode undergoes smaller strain generation when cycled at slower rates when the same amount of Na ions is removed or inserted into the electrode. A mathematical model was developed to predict strain evolution in the composite electrode as well as the concentration profile of the Na ions in the electrode particles. Rate-dependent and time dependent factors on the strain generation in the electrode are attributed to the capacity-dependent intercalation strains, rate-dependent mismatch strains, and time-dependent irreversible strains. The combination of in situ strain measurements with the analytical model provided new insight into the electrochemically induced mechanical deformations in Na-ion cathode electrodes.

Ozdogru, Bertan↗
DOE OSTI2016

The nature of nano-sized precipitates in ferritic/martensitic steel P92 produced by thermomechanical treatment

Thermomechanical treatment (TMT) can effectively improve the mechanical properties of high-Cr ferritic/martensitic (F/M) steels, which has been mainly attributed to a dense dispersion of nano-sized precipitates. Precipitate phases in high-Cr F/M steels produced by TMT require further investigations. Precipitates in commercial F/M steel P92 produced by a TMT process, warm-rolled at 650 °C plus tempered at 650 °C for 1 h, were investigated by transmission electron microscopy. Nano-sized precipitates with a high number density in the steel after the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The M{sub 2}(C,N) carbonitride has a hexagonal lattice with the lattice parameters about a/c = 0.299/0.463 nm. These M{sub 2}(C,N) carbonitrides with a typical composition of (Cr{sub 0.85}V{sub 0.06}Fe{sub 0.06}Mo{sub 0.03}){sub 2}(C,N) have an average diameter smaller than 30 nm, and mainly distribute on dislocations and at the boundaries of equiaxed ferrite grains in the TMT steel. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the P92 steel after the TMT, as reported previously, were considered to be mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT rather than to MX and M{sub 23}C{sub 6} precipitates. - Graphical abstract: TEM micrographs of precipitates on extraction carbon replicas prepared from ferritic/martensitic (F/M) steel P92. (a) After conventional heat treatment, normalized at 1050 °C for 30 min plus tempered at 765 °C for 1 h. (b) After a thermomechanical treatment (TMT), warm-rolled at 650 °C plus tempered at 650 °C for 1 h. Nano-sized precipitates with a high number density in the steel produced by the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the steel by the TMT are mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides, rather than to MX and M{sub 23}C{sub 6} precipitates. It is suggested that plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT are more helpful to improve the creep properties of the steel than M{sub 23}C{sub 6} plus MX precipitates obtained by the conventional heat treatment. - Highlights: •Carbon replicas were used for precisely identifying nano-sized phases in steel produced by TMT. •Densely nano-sized precipitates in P92 steel after TMT were found to be Cr-rich M{sub 2}(C,N) phase. •Fine precipitates produced by TMT can be M{sub 2}X phase instead of MX and/or M{sub 23}C{sub 6} phase. •Enhanced creep properties of P92 steel by TMT is attributed to fine M{sub 2}X, rather than to MX and M{sub 23}C{sub 6}. •TMT involving a tempering at 650 °C inhibits the formation of M{sub 23}C{sub 6}/M{sub 5}C{sub 2} phases in high-Cr steels.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Effects of size reduction on microstructural, optical, vibrational, magnetic and photocatalytic properties of ZnO nanocrystals

Highlights: • ZnO nanocrystals of different sizes 4–19 nm, thus different S/V ratios synthesized. • Lattice strain and lattice volume increase with size reduction. • Raman spectra show enhanced surface effects with phonon mode disappearance. • ESR shows exchange interaction; VSM shows surface originated ferromagnetism. • Photocatalysis is less affected with size reduction and surface enhancement. - Abstract: ZnO nanocrystals were synthesized in the size range 4 nm–19 nm by chemical co-precipitation method and extensively characterized by various techniques. Size control was achieved by changing the concentration of ME that acted as the capping agent. Lattice strain and lattice volume were found to increase with the decrease in crystallite size that could be attributed to an enhanced influence of surface atoms. Raman spectroscopy provided evidence of enhancement of surface influence and reduction of crystallinity with the observation of decrease in E{sub 2}{sup high} phonon mode intensity with a decrease in crystallite size. Electron spin resonance spectra revealed the presence of exchange interaction as well as presence of lattice disorder. Vibrating sample magnetometry results showed that M-H curves exhibit hysteresis loops which confirmed the ferromagnetic nature of the nanocrystals and it could be attributed to the interaction between localized electron spins resulting from oxygen vacancies at the surface. Blue shift of the UV–Visible absorption spectra was observed with decrease in crystallite size, which could be attributed to quantum confinement effect. Room temperature photoluminescence spectra were recorded under UV light excitation, which showed emission of different colours (light violet, violet, blue and green) in the visible region, attributable to the presence of various types of defects such as zinc interstitial, zinc vacancy and oxygen vacancy in the ZnO nanocrystals. Photodegradation of aqueous methylene blue by the ZnO nanocrystals was performed under simulated sunlight irradiation. There was a very small increase in efficiency of photodegradation with size reduction, which showed that there was no significant change in surface defects with size reduction in the nanocrystals despite the surface enhancement. A mechanism for photocatalysis involving defect states has been proposed.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

A framework for assessment of predictive capability maturity and its application in nuclear thermal hydraulics

Here this work presents a formalized and computerized framework for the assessment of decision regarding the adequacy of a simulation tool for a nuclear reactor application. The decision regarding a code’s adequacy for an application is dependent on the assessment of different attributes that govern verification, validation, and uncertainty quantification of the code. In this work, the focus is on code validation. Therefore, the framework is developed and illustrated from the perspective of decision regarding the validation assessment of a code. Code validation assessment is performed based on the validation test results, data applicability, and process quality assurance factors. The process quality assurance factors warrant the trustworthiness of the evidence and help in checking people and process compliance with respect to the standard requirements. The proposed framework is developed using an argument modeling technique called Goal Structuring Notation (GSN). Goal structuring notation facilitates structural knowledge representation, information abstraction, evidence incorporation, and provides a skeletal structure for quantitative maturity assessment. The decision schema for the development of the decision model is based on the Predictive Capability Maturity Model (PCMM) and Analytic Hierarchy Process (AHP) and formalized using Goal structuring notation. Each decision attribute is formulated as a claim, where the degree of validity of the claim (attribute’s assessment) is expressed using different maturity levels. The GSN representation of the decision model is transformed into a confidence network to provide evidence-based quantitative maturity assessment using the Bayesian network. A metric based on the expected utility of maturity levels, called expected distance metric, is proposed to measure the distance between target maturity and achieved maturity on a scale of zero to one. Expected distance metric helps in comparing the assessment of different attributes and identification of major areas of concern in terms of modeling capability, data needs, and quality of assessment process. The practical application of the framework is demonstrated by a case study on validation assessment of a thermal-hydraulic code for a challenge problem called Departure from Nucleate Boiling (DNB).

42 ENGINEERING↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Preface: Chemical forensics

Introduction: This virtual special issue stems from 36 presentations given at the Fourth Chemical Forensics Symposium held 3 - 4 April 2017 as part of the 253rd American Chemical Society (ACS) National Meeting & Exposition in San Francisco, CA, USA. Since 2011, the ACS Division of Analytical Chemistry has hosted symposia that review developments and topics arising in chemical forensics. Chemical forensics is a developing scientific discipline that aims to attribute a chemical (or mixture) of interest to its source by the analysis of the chemical itself or associated material constituents to address investigative, legal or intelligence questions. “Source” refers to how, where and when a chemical was handled or produced. Given past and recent attacks with chemical warfare agents (CWAs) and other toxic chemicals there is a strong need for research and development in chemical forensics for the purpose of finding those responsible for chemical attacks. Hence, many of the papers in this issue deal with the source attribution of toxic chemicals that can be used as weapons. However, given the needs, principles and methods that are shared across different application areas of material provenance or authentication, papers that deal with the source attribution of other illicitly used chemicals and materials are included.

Chemical Forensic↗
DOE OSTI2024

Ultrafast Exciton Dynamics of CH 3 NH 3 PbBr 3 Perovskite Nanoclusters

Exciton dynamics of perovskite nanoclusters has been investigated for the first time using femtosecond transient absorption (TA) and time-resolved photoluminescence (TRPL) spectroscopy. The TA results show two photoinduced absorption signals at 420 and 461 nm and a photoinduced bleach (PB) signal at 448 nm. The analysis of the PB recovery kinetic decay and kinetic model uncovered multiple processes contributing to electron-hole recombination. The fast component (~8 ps) is attributed to vibrational relaxation within the initial excited state, and the medium component (~60 ps) is attributed to shallow carrier trapping. The slow component is attributed to deep carrier trapping from the initial conduction band edge (~666 ps) and the shallow trap state (~40 ps). The TRPL reveals longer time dynamics, with modeled lifetimes of 6.6 and 93 ns attributed to recombination through the deep trap state and direct band edge recombination, respectively. In conclusion, the significant role of exciton trapping processes in the dynamics indicates that these highly confined nanoclusters have defect-rich surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Atmospheric and Long-term Aging Effects on the Electrical Properties of Variable Thickness WSe 2 Transistors

Atmospheric and long-term aging effects on electrical properties of WSe 2 transistors with various thicknesses are examined. While countless published studies report electrical properties of transition-metal dichalcogenide materials, many are not attentive to testing environment or to age of samples, which we have found significantly impacts results. Our as-fabricated exfoliated WSe 2 pristine devices are predominantly n-type, which is attributed to selenium vacancies. Transfer characteristics of as-fabricated devices measured in air then vacuum reveal physisorbed atmospheric molecules significantly reduced n-type conduction in air. First-principles calculations suggest this short-term reversible atmospheric effect can be attributed primarily to physisorbed H 2 O on pristine WSe 2 , which is easily removed from the pristine surface in vacuum due to the low adsorption energy. Devices aged in air for over 300 h demonstrate irreversibly increased p-type conduction and decreased n-type conduction. Moreover, they develop an extended time constant for recovery of the atmospheric adsorbents effect. Short-term atmospheric aging (up to approximately 900 h) is attributed to O 2 and H 2 O molecules physisorbed to selenium vacancies where electron transfer from the bulk and adsorbed binding energies are higher than the H 2 O-pristine WSe2. The residual/permanent aging component is attributed to electron trapping molecular O 2 and isoelectronic O chemisorption at selenium vacancies, which also passivates the near-conduction band gap state, p-doping the material, with very high binding energy. All effects reflected have the expected thickness dependence, namely, thinner devices are more sensitive to atmospheric and long-term aging effects.

36 MATERIALS SCIENCE↗