Engineering topics

Smith, Jacob G.

Publications and source records attributed to Smith, Jacob G..

3 records

DOE OSTI2024

Local Coordination Environment of Lanthanides Adsorbed onto Cr- and Zr-based Metal–Organic Frameworks

Separating individual lanthanide (Ln) elements in aqueous mixtures is challenging. Ion-selective capture by porous materials, such as metal–organic frameworks (MOFs), is a promising approach. To design ion-selective MOFs, molecular details of the Ln adsorption complexes within the MOFs must be understood. We determine the local coordination environment of lanthanides Nd(III), Gd(III), and Lu(III) adsorbed onto Cr(III)-based terephthalate MOF (Cr-MIL-101) and Zr(IV)-based Universitet in Oslo MOFs (UiO-66 and UiO-68) and their derivatives. In the Cr(III)- and Zr(IV)-based MOFs, Ln adsorb as inner-sphere complexes at the metal oxo clusters, regardless of whether the organic linkers are decorated with amino groups. Missing linkers result in favorable Ln binding sites at oxo clusters; however, Ln can coordinate to metal sites even with linkers in place. Further, MOF functionalization with phosphonate groups led to Ln chemisorption onto these groups, which out-compete metal cluster sites. Ln form monodentate and bidentate and mononuclear and binuclear surface complexes. We conclude that MOFs for ion-selective Ln capture can be designed by a combination of (1) maximizing metal-lanthanide interactions via shared O atoms at the metal oxo cluster sites, where mixed oxo clusters can lead to ion-selective Ln adsorption, and (2) functionalizing MOFs with Ln-selective groups capable of out-completing the metal oxo cluster sites.

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DOE OSTI2023

Ion solvation as a predictor of lanthanide adsorption structures and energetics in alumina nanopores

Adsorption reactions at solid-water interfaces define elemental fate and transport and enable contaminant clean-up, water purification, and chemical separations. For nanoparticles and nanopores, nanoconfinement may lead to unexpected and hard-to-predict products and energetics of adsorption, compared to analogous unconfined surfaces. Here we use X-ray absorption fine structure spectroscopy and operando flow microcalorimetry to determine nanoconfinement effects on the energetics and local coordination environment of trivalent lanthanides adsorbed on Al 2 O 3 surfaces. We show that the nanoconfinement effects on adsorption become more pronounced as the hydration free energy, ΔG hydr , of a lanthanide decreases. Neodymium (Nd 3+ ) has the least exothermic ΔG hydr (-3336 kJ·mol -1 ) and forms mostly outer-sphere complexes on unconfined Al 2 O 3 surfaces but shifts to inner-sphere complexes within the 4 nm Al 2 O 3 pores. Lutetium (Lu 3+ ) has the most exothermic ΔG hydr (-3589 kJ·mol -1 ) and forms inner-sphere adsorption complexes regardless of whether Al 2 O 3 surfaces are nanoconfined. Importantly, the energetics of adsorption is exothermic in nanopores only, and becomes endothermic with increasing surface coverage. Changes to the energetics and products of adsorption in nanopores are ion-specific, even within chemically similar trivalent lanthanide series, and can be predicted by considering the hydration energies of adsorbing ions.

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DOE OSTI2023

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom–atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. Here, this work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.

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