Chemical Compound Classification by Elemental Signatures in Castle Dust Using SEM Automated X-ray Particle Analysis

Ortiz-Montalvo, Diana L.; Vicenzi, Edward P.; Ritchie, Nicholas W. M.; Grissom, Carol A.; Livingston, Richard A.; Weldon-Yochim, Zoe; Conny, Joseph M.; Wight, Scott A.

doi:10.1017/s1431927618004087

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The hypothesis of an external source of Mn was dismissed both because no external source could be identified and because such an external source would have resulted in a more homogeneous or azimuthally oriented distribution of areas exhibiting patinas. Furthermore, the possible aerial deposition of Mn originating from automotive traffic would most likely be associated with a significant enrichment in other "traffic-related" elements such as Ba, Zn, or Pb (Macholdt et al, 2017;Ortiz-Montalvo et al, 2018;Sharps et al, 2020), and that was not detected. In addition, the contribution of such an external "traffic-related" source of Mn is expected to be minor in the center of a small town (∼ 18 000 inhabitants as of 2018) with cobbled streets.…”

Section: Discussionmentioning

confidence: 99%

Characterization and origin of the Mn-rich patinas formed on Lunéville château sandstones

et al. 2021

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Abstract. The formation of iron- and/or manganese-rich dark patinas on sandstones is a common natural phenomenon that occurs also on building stones. Lunéville château, in eastern France, presents such patinas that developed either under natural conditions (rain and time) or after an accidental fire and exposure to significant amounts of water as part of attempts to extinguish the fire. The present study aimed at characterizing both types of patinas in an effort to determine their formation mechanisms and Mn sources. In both cases, Mn required for patina formation likely derives from the reductive dissolution of Mn-rich minerals present in pristine sandstones, as suggested by the contrasting mineralogy and chemistry of Mn-rich phases present in the bulk and in the patina of a given building block. Reduced Mn species then migrate to the exposed surface of building blocks where they are re-oxidized via undetermined processes. Patinas developing “naturally” over time result from the alternation of wetting-reducing and drying-oxidizing cycles and appear to be composed of birnessite. Patinas formed after the 2003 fire result from this single accidental event and form a much thinner, heterogeneous, and discontinuous layer of poorly crystalline lithiophorite at the sandstone surface (∼ 0–150 µm compared to ∼ 300–600 µm for “natural” patinas). The lack of Mn-rich patinas on areas of Lunéville château is likely related to the lower Mn content of pristine sandstone blocks.

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Section: Discussionmentioning

confidence: 99%

Characterization and origin of the Mn-rich patinas formed on Lunéville château sandstones

et al. 2021

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Nanoscale structure and compositional analysis of manganese oxide coatings on the Smithsonian Castle, Washington, DC

2020

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Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Eshun,

Lamar,

Aksoy

et al. 2024

Microscopy and Microanalysis

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Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. As a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

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Chemical Compound Classification by Elemental Signatures in Castle Dust Using SEM Automated X-ray Particle Analysis

Cited by 3 publications

References 3 publications

Characterization and origin of the Mn-rich patinas formed on Lunéville château sandstones

Characterization and origin of the Mn-rich patinas formed on Lunéville château sandstones

Nanoscale structure and compositional analysis of manganese oxide coatings on the Smithsonian Castle, Washington, DC

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

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