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Title: Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition

Abstract

Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances ofmore » the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.« less

Authors:
 [1];  [2];  [2];  [1]
  1. Univ. of Nevada, Las Vegas, NV (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1511857
Report Number(s):
[LLNL-JRNL-704344]
[Journal ID: ISSN 0009-2541; 805395]
Grant/Contract Number:  
[AC52-07NA27344]
Resource Type:
Accepted Manuscript
Journal Name:
Chemical Geology
Additional Journal Information:
[ Journal Volume: 507; Journal Issue: C]; Journal ID: ISSN 0009-2541
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY; Environmental sciences; Geosciences; Chemistry - Radiation chemistry; radiochemistry; nuclear chemistry

Citation Formats

Fitzgerald, M. A., Knight, K. B., Matzel, J. E., and Czerwinski, K. R. Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition. United States: N. p., 2019. Web. doi:10.1016/j.chemgeo.2018.12.018.
Fitzgerald, M. A., Knight, K. B., Matzel, J. E., & Czerwinski, K. R. Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition. United States. doi:10.1016/j.chemgeo.2018.12.018.
Fitzgerald, M. A., Knight, K. B., Matzel, J. E., and Czerwinski, K. R. Thu . "Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition". United States. doi:10.1016/j.chemgeo.2018.12.018. https://www.osti.gov/servlets/purl/1511857.
@article{osti_1511857,
title = {Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition},
author = {Fitzgerald, M. A. and Knight, K. B. and Matzel, J. E. and Czerwinski, K. R.},
abstractNote = {Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.},
doi = {10.1016/j.chemgeo.2018.12.018},
journal = {Chemical Geology},
number = [C],
volume = [507],
place = {United States},
year = {2019},
month = {1}
}

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