Precision Atomistic Structures of Actinium-/Radium-/Barium-Doped Lanthanide Nanoconstructs for Radiotherapeutic Applications
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Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.
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High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal−organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanidebased MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals.
Doping is widely used to enhance the photoelectrochemical performance of BiVO 4 , yet solubility limits and polymorphic stability constrain compositional tuning. Here, in this study, the role of trivalent cation substitution (Ln = La, Nd, Dy, Ho, Y) on pressure-induced phase transformations in Bi 1–x Ln x VO 4 (x ≤ 0.5) is described. Powder X-ray and neutron diffraction reveal that increasing Ln content stabilizes the tetragonal zircon-type polymorph under ambient conditions, while applied pressures of up to ∼5 GPa promote conversion to the monoclinic fergusonite-type polymorph. In-situ neutron diffraction on Bi 0.8 La 0.2 VO 4 shows a reversible monoclinic to tetragonal transition near 2–3 GPa with a bulk modulus of 147 GPa. The extent of conversion depends strongly on dopant identity, concentration, and synthetic route, with mixed-phase solid-state samples converting more efficiently than phase-pure coprecipitated materials. These results demonstrate pressure as a viable pathway to access metastable, doped BiVO 4 compositions beyond conventional solubility limits.
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The development of nuclear waste forms for both existing and future nuclear wastes is critical to ensuring global environmental safety. This study focuses on waste management from molten salt reactors, where fuel exists in a salt form and could be processed in real time for the removal of neutron poisons such as xenon isotopes (e.g., 135 Xe) and rare earth elements (REEs, e.g., 149 Sm). To ensure safe, stable, and long-term disposal in geological repositories, REEs must be incorporated into a durable waste form. Iron-phosphate glasses are a promising candidate due to their low melting points, high chemical durability, and their ability to incorporate high concentrations of REEs. In this study, we successfully prepared iron-phosphate glass waste forms with high Nd loadings (up to 37 mass %) in batch sizes ranging from small (23 g) to large (1600 g). The resulting materials contained up to 75 mass % NdPO 4 , contributing to their mechanical resilience and exceptional chemical durability. These findings highlight the potential of iron-phosphate glasses as high-efficiency, chemically durable waste forms and demonstrate the successful transition from theoretical design to scaled-up production.
A microextraction (ME) sampling system, paired with inductively coupled plasma-mass spectrometry (ICP-MS), was employed to spatially analyze proteins tagged with lanthanum (La), gadolinium (Gd), or terbium (Tb) on the surface of western blot paper. The proteins were covalently tagged, separated via gel electrophoresis, and transferred to western blot paper for analysis by ME-ICP-MS. The ME-ICP-MS method enables the direct sampling of the tagged species on the western blot paper, without any sample preparation. Traditionally, the tagged analyte would need to be stained, excised, and digested to be analyzed by ICP-MS for its elemental and isotopic characterization. Preliminary detection limits for the ME-ICP-MS method applied to western blot paper were established to be 564, 54, and 2.5 fg for La, Gd, and Tb, respectively. The developed ME-ICP-MS method was compared to laser ablation (LA) ICP-MS, another direct solid sampling technique; it was readily determined that ME-ICP-MS can effectively map the elemental constituents on the western blot paper with comparable analysis time and measurement sensitivity. The analysis time per 2 × 4 mm extraction is ∼1 minute; if protein spots are directly targeted (rather than systematically mapping the entire blot paper), the analysis time per protein spot is ∼1 min. This developed method proved to be fast, effective, and accessible for correlating protein molecular weight with the detection of the inorganic tagant. The ME-ICP-MS approach could be widely applicable in research areas that involve metal-tagged protein bioconjugates, such as in the development of diagnostic and therapeutic agents and other biochemical probes.
Peptide surfactants (PEPS) are studied to capture and retain rare earth elements (REEs) at air–water interfaces to enable REE separations.