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<p>The effect of process scale on the morphological properties of nuclear materials remains a pertinent question for developing signatures for material provenance. Uranyl oxalate precipitations&#xa0; were carried out at three process scales (1&#xa0;g, 20&#xa0;g, 400&#xa0;g). Plutonium (III) oxalate and plutonium (IV) oxalate were precipitated at scales from 10 to 200&#xa0;g and 30&#xa0;g to 155&#xa0;g, respectively. The parameters of the chemical synthesis were the same across precipitation scales, though the process equipment varied across scales. A multi-faceted approach to morphology analysis was utilized; dynamic image analysis (PSA-DIA) was used for particle size and shape analysis; scanning electron microscopy (SEM) images with unsupervised machine learning (ML) encoders were used to obtain embeddings for particles. A high-dimensional test statistic, d-dimensional Kolmogorov Smirnov (ddKS), was used to show that materials synthesized on scales from 1 to 400&#xa0;g have similar distribution distances to replicates within a process scale.</p>

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Effect of precipitation batch size on the morphological properties of various actinide oxalates

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Cody A. Nizinski,
  • Aaron M. Chalifoux,
  • Evan J. Warzecha,
  • Alex R. Hagen,
  • Alexa B. Hanson,
  • Kari Sentz,
  • Forrest D. Heller,
  • Richard A. Clark,
  • David E. Meier,
  • Gregg J. Lumetta,
  • Joel M. Tingey

摘要

The effect of process scale on the morphological properties of nuclear materials remains a pertinent question for developing signatures for material provenance. Uranyl oxalate precipitations  were carried out at three process scales (1 g, 20 g, 400 g). Plutonium (III) oxalate and plutonium (IV) oxalate were precipitated at scales from 10 to 200 g and 30 g to 155 g, respectively. The parameters of the chemical synthesis were the same across precipitation scales, though the process equipment varied across scales. A multi-faceted approach to morphology analysis was utilized; dynamic image analysis (PSA-DIA) was used for particle size and shape analysis; scanning electron microscopy (SEM) images with unsupervised machine learning (ML) encoders were used to obtain embeddings for particles. A high-dimensional test statistic, d-dimensional Kolmogorov Smirnov (ddKS), was used to show that materials synthesized on scales from 1 to 400 g have similar distribution distances to replicates within a process scale.