Research Experience and Training Coordination Core (RETCC)

2022
Srishti Gupta, Ngan Anh Nguyen, and Christopher L. Muhich. 7/2022. “Surface water H-bonding network is key controller of selenate adsorption on [012] α-alumina: An Ab-initio study.” Journal of Colloid and Interface Science, 617, Pp. 136-146. Publisher's VersionAbstract
Selenate adsorption onto metal oxide surfaces is a cost-effective method to remove the toxin from drinking water systems. However, the low selectivity of metal oxides requires frequent sorbent replacement. The design of selective adsorbents is stymied because the surface factors controlling selenate adsorption remain unknown. We calculate adsorption energies of selenate on the (0 1 2) α-Al2O3 surface using density functional theory to unravel the physics that controls adsorption. Our model is validated against experiment by correctly predicting selenate removal efficiency as a function pH. We find that the selenate adsorption energy on the anhydrous α-Al2O3 surface is surprisingly anti-correlated with the fully solvated adsorption energy; therefore, the direct interaction between adsorbate and sorbent is eliminated as the controlling mechanism. Rather, the change in number of surface hydrogen bonds after adsorption is the factor most correlated with the adsorption energy (R2 > 0.8); and is thus determined to be the factor controlling selenate adsorption. We find that pH affects adsorption by controlling the number of surface protons available for H-bonding to selenate. This work demonstrates that adsorption prediction should not be made based on gas phase sorption energies and suggests that surface engineering which increases surface protonation may be an effective strategy for increasing selenate sorption.
2021
Nilanjana Laha, Nathan Huey, Brent Coull, and Rajarshi Mukherjee. 9/24/2021. “On Statistical Inference with High Dimensional Sparse CCA.” arXiv.org. Publisher's VersionAbstract
We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this regard, our main contribution is the development of a loss function, based on which, one can operationalize a one-step bias-correction on reasonable initial estimators. Our analytic results in this regard are adaptive over suitable structural restrictions of the high dimensional nuisance parameters, which, in this set-up, correspond to the covariance matrices of the variables of interest. We further supplement the theoretical guarantees behind our procedures with extensive numerical studies.
2020
Holly E Rudel, Mary Kate M Lane, Christopher L Muhich, and Julie B Zimmerman. 2020. “Toward Informed Design of Nanomaterials: A Mechanistic Analysis of Structure-Property-Function Relationships for Faceted Nanoscale Metal Oxides.” ACS Nano.Abstract
Nanoscale metal oxides (NMOs) have found wide-scale applicability in a variety of environmental fields, particularly catalysis, gas sensing, and sorption. Facet engineering, or controlled exposure of a particular crystal plane, has been established as an advantageous approach to enabling enhanced functionality of NMOs. However, the underlying mechanisms that give rise to this improved performance are often not systematically examined, leading to an insufficient understanding of NMO facet reactivity. This critical review details the unique electronic and structural characteristics of commonly studied NMO facets and further correlates these characteristics to the principal mechanisms that govern performance in various catalytic, gas sensing, and contaminant removal applications. General trends of facet-dependent behavior are established for each of the NMO compositions, and selected case studies for extensions of facet-dependent behavior, such as mixed metals, mixed-metal oxides, and mixed facets, are discussed. Key conclusions about facet reactivity, confounding variables that tend to obfuscate them, and opportunities to deepen structure-property-function understanding are detailed to encourage rational, informed design of NMOs for the intended application.