Kevin, Sanjubala and Colleagues Published in Clays and Clay Minerals

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DFT was used to determine counter-hydrogen ion retention strength in Montmorillonite clays, a common aluminosilicate mineral found in soil, as a measure of cation adsorption strength. Results show that impurity Mg-Fe separation distance is responsible for three possible adsorption strengths regimes and are consistent with experimental data.

Abstract

Although multiple types of adsorption sites have long been observed in montmorillonite, a consistent explanation about the chemical structure of these adsorption sites has not yet been established. Identifying the cation interlayer adsorption sites based on the octahedral cation distribution on montmorillonite was investigated in this study by using a Density Functional Theory (DFT) simulation. A clay structural model (H[Al6MgFe]Si16O40(OH)8) with a similar composition to Wyoming SWy-1 montmorillonite was built, where two octahedral Al were respectively substituted by Fe and Mg, and H+ was the charge compensating cation. This model had twenty-one different possible configurations as a function of the distribution of octahedral Al, Fe, and Mg cations. The DFT simulations of 15 of these different configurations showed no preference for the formation of any configuration with a specific octahedral Fe-Mg distance. However, the H+ adsorption energy was separated into three distinct groups based on the number of octahedral jumps from Fe to Mg atoms. The H+ adsorption energy significantly decreased with increasing number of octahedral jumps from Fe to Mg. Assuming an even probability of occurrence of 21 octahedral structures in montmorillonite, the percentages of these three groups are 43, 43, and 14%, respectively, which are very close to the three major sites on montmorillonite from published cation adsorption data. These DFT simulations offer an entirely new explanation for the location and chemical structure of the three major adsorption sites on montmorillonite, namely, all three sites are in the interlayer, and their adsorption strengths are a function of the number of octahedral jumps from Fe to Mg atoms.

Uche Was Inducted Into The John Lof Leadership Academy

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Uche was inducted into the John Lof Leadership Academy, an exclusive society for UConn Engineering graduate students.

UConn John Lof Leadership Academy

(Photo Credit: Christopher LaRosa/UConn)

 

UConn John Lof Leadership Academy

UConn John Lof Leadership Academy

(Photo Credit: Christopher LaRosa/UConn)

 

 

John Lof Leadership Academy Mission

Without culturally competent visionaries that are able to communicate, collaborate, and create, there would be no progress in society. The John Lof Leadership Academy strives to inform and inspire these next-generation leaders in academia, industry, and beyond.

Through a “for us, by us” philosophy, John Lof Scholars aim to develop themselves through focused training, specialized workshops, and active learning through outreach and enterprise on and off the UConn campus.

John Lof Leadership Academy

Tulsi & Kevin Publish in the Journal of Applied Physics

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Congratulations to Tulsi & Kevin on their publication, “Ferroelectric Films on Metal Substrates: The Role of Thermal Expansion Mismatch on Dielectric, Piezoelectric, and Pyroelectric properties”, in the Journal of Applied Physics for their findings on the role of metallic substrates on the functional properties of PZT solid solutions.

 

Ferroelectric Films on Metal Substrates: The Role of Thermal Expansion Mismatch on Dielectric, Piezoelectric, and Pyroelectric properties
T. A. Patel, K. Co, R. J. Hebert, and S. P. Alpay, J. Appl. Phys. 126, 134103 (2019)
DOI: DOI: https://doi.org/10.1063/1.5116134

ABSTRACT
We present here a comprehensive analysis of the effect of thermal stresses on the functional properties of ferroelectric oxides on metal substrates. We use a Landau-Devonshire formalism to quantitatively assess the role of in-plane thermal strains that arise from the coefficient of thermal expansion (CTE) mismatch between lead zirconate titanate [PbZrxTi1–xO3, PZT x/(1 – x)] films and Al, Cu, Fe, Ni, and Ti-based substrates. Specifically, we compute Curie transition temperatures, spontaneous polarizations, dielectric permittivities, piezoelectric coefficients, and pyroelectric responses of tetragonal PZT compositions as a function of the growth/processing temperature. To provide a rapid evaluation, we also develop Ashby diagrams of property coefficients as a function of PZT composition, processing temperature, and CTE of the substrate. Our results show that thermal strains in PZT may significantly alter the ferroelectric transition temperature, dielectric, piezoelectric, and pyroelectric properties. For example, for PZT 50/50 films on Ni-based superalloys processed/annealed at 700 °C, we predict monodomain intrinsic dielectric, piezoelectric, and pyroelectric responses to be 234, 152 pC/N, and 0.021 μC cm−2 °C−1, respectively, compared to bulk PZT 50/50 values of 381, 326 pC/N, and 0.045 μC cm−2 °C−1. These are substantial variations which show that thermal strains must be considered in the design and development of built-in functionality obtained through ferroelectric films in structural, aerospace components.

A New Appointment For Our PI !

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Congratulations to Dr. Alpay on his new appointment as Associate Dean for Research and Industrial Partnerships.

In addition to his role leading the UConn Tech Park, we are thrilled to share that our PI and Executive Director Dr. Alpay was recently named Associate Dean for Research and Industrial Partnerships at the UConn School of Engineering. This newly created position seeks to enhance and grow partnerships with industries of all sizes in the state and beyond and will complement ongoing efforts at the Tech Park.  The dual role will allow him to effectively lead engineering centers and initiatives in IPB/UConn Tech Park.

What an Exciting Conference at Georgia Tech!!

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Dennis & Uche presented their research at the Machine Learning in Science and Engineering Conference at Georgia Tech.

Dennis Trujillo gave a talk:

Applications of Deep Learning to Coherent Diffractive Imaging

Abstract. Convolutional Neural Networks have experienced a surge in interest over recent years as a viable technique in computer vision and other problems involving large datasets. Coherent  diffractive imaging experiments typically generate extremely large datasets which require extensive data storage and extensive computation and manpower to screen through utilizing standard screening (expert analysis by eye) and phase retrieval algorithms. We apply these techniques in the domain of coherent diffractive imaging and phase retrieval for classification of objects of interest (topological phases) and in a regression scheme as an alternative to standard phase reconstruction algorithms.

Uche Anene presented a poster:

Ligand Functionalization for Enhanced Selective Gas Adsorption of Hydrostable STAM-17-OEt MOF

Abstract. Metal-organic framework (MOF), a class of porous crystalline materials, has received extensive attention due to their wide range of applications in areas such as gas storage and separation, catalysis and luminescence. However, its hydrolytic instability has been a major drawback and limit further implementation into new areas. Recently, STAM-17-OEt MOF was reported to overcome these shortcomings.1 Here we use density functional theory to investigate STAM-17-OEt for its selective sorption for technologically important gases such as CH4, CO, CO2, H2, N2 andH2O by using both molecular cluster and 3D periodic structural models. Further, MOF properties such as their sorption, selectivity, and their stability can be tuned through the functionalization of their organic linkers. In this work, we investigate howlinker functionalization modifies the electronic structure of STAM-17-OEt. Our findings will provide important insight on the unique binding characteristics of the different gas molecules and electronic structural factors associated with the functionalized STAM-17-OEt.

Reference:
1. Mchugh, L. N.; Mcpherson, M. J.; Mccormick, L. J.; Morris, S. A.; Wheatley, P. S.; Teat, S. J.; Mckay, D.; Dawson, D. M.; Sansome, C. E. F.; Ashbrook, S. E.; Stone, C. A.; Smith, M. W.; Morris, R. E. Hydrolytic Stability in Hemilabile Metal–Organic Frameworks. Nature Chemistry. 2018, 10(11), 1096–1102. doi:10.1038/s41557-018-0104-x

 

Congratulations Dennis & Uche for Receiving MLSE Travel Awards

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Dennis and Uche were both awarded travel grants to attend the Machine Learning in Science and Engineering Conference to be held at Georgia Tech on June 10-12.

The second-annual MLSE conference highlights advances in research that utilize methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and the ways these methods lead to innovations across various fields. Researchers from academia, government, and industry will gather to explore the future of research in science and engineering.

On May 10, 2017, an internal symposium known as Machine Learning in Science and Engineering was held at Carnegie Mellon University to identify ways in which these computational tools are advancing diversity in several fields. Based on the strong response at CMU, an open conference was held on June 6–8, 2018 at the CMU campus in Pittsburgh in partnership with Georgia Tech.

This conference surveyed advances in basic research that utilizes methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and ways that these methods are leading to innovations across these fields. Researchers from academia, government, and industry participated in a unique and fascinating forum on the future of research and innovation in science and engineering.

http://dsf.ideas.gatech.edu/events/mlse

 

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