Discovery Of Functional Materials Research Articles

A practical preparation of bicyclic boronates via metal-free heteroatom-directed alkenyl sp2-C‒H borylation

Bicyclic boronates play critical roles in the discovery of functional materials and antibacterial agents, especially against deadly bacterial pathogens. Their practical and convenient preparation is in high demand but with great challenge. Herein, we report an efficient strategy for the preparation of bicyclic boronates through metal-free heteroatom-directed alkenyl sp2-C‒H borylation. This synthetic approach exhibits good functional group compatibility, and the corresponding boronates bearing halides, aryls, acyclic and cyclic frameworks are obtained with high yields (43 examples, up to 95% yield). Furthermore, a gram-scale experiment is conducted, and downstream transformations of the bicyclic boronates are pursued to afford natural products, drug scaffolds, and chiral hemiboronic acid catalysts.

Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning

Rapid identification of lattice thermal conductivity of semiconductors from their crystal structure is required in the discovery of functional materials. A promising strategy is using a machine learning method based on a first-principles dataset, which, however, suffers from the dilemma of too little data available. In this work, the crystal graph convolutional neural networks (CGCNN) model was improved by enhancing the information of atomic descriptors (for short CGCNN-D), and the transfer learning (TL) method was combined to overcome the problem of small datasets. It is found that the CGCNN-D has improved predicting performance for both electronic bandgap with large data volume and thermal conductivity with small data volume, with the mean absolute error reducing 7% and 10%, respectively, indicating the importance of the improved atomic description. Applying TL with electronic bandgap as a proxy into the CGCNN-D further upgrades the prediction accuracy for thermal conductivity that has only 95 pieces of data, yielding 19% decrease in the mean absolute error as compared to the original CGCNN. The trained CGCNN-D-TL model was used to quickly estimate the thermal conductivities of thousands of semiconductors, and the materials identified with potentially high thermal conductivity were further screened by the optimized Slack model. Finally, the most promising BC2N was discovered and then confirmed by the first-principles calculations, which shows room-temperature thermal conductivities of 731, 594, and 500 W m−1 K–1 along the three principal axes of its lattice structure.

Temperature-Dependent Nitrous Oxide/Carbon Dioxide Preferential Adsorption in a Thiazolium-Functionalized NU-1000 Metal-Organic Framework.

Solvent-assisted ligand incorporation (SALI) of the ditopic linker 5-carboxy-3-(4-carboxybenzyl)thiazolium bromide [(H2PhTz)Br] into the zirconium metal–organic framework NU-1000 [Zr6O4(OH)8(H2O)4(TBAPy)2, where NU = Northwestern University and H4TBAPy = 1,3,6,8-tetrakis(p-benzoic-acid)pyrene], led to the SALIed NU-1000-PhTz material of minimal formula [Zr6O4(OH)6(H2O)2(TBAPy)2(PhTz)]Br. NU-1000-PhTz has been thoroughly characterized in the solid state. As confirmed by powder X-ray diffraction, this material keeps the same three-dimensional architecture of NU-1000 and the dicarboxylic extra linker bridges adjacent [Zr6] nodes ca. 8 Å far apart along the crystallographic c-axis. The functionalized MOF has a BET specific surface area of 1560 m2/g, and it is featured by a slightly higher thermal stability than its parent material (Tdec = 820 vs. 800 K, respectively). NU-1000-PhTz has been exploited for the capture and separation of two pollutant gases: carbon dioxide (CO2) and nitrous oxide (N2O). The high thermodynamic affinity for both gases [isosteric heat of adsorption (Qst) = 25 and 27 kJ mol–1 for CO2 and N2O, respectively] reasonably stems from the strong interactions between these (polar) “stick-like” molecules and the ionic framework. Intriguingly, NU-1000-PhTz shows an unprecedented temperature-dependent adsorption capacity, loading more N2O in the 298 K ≤ T ≤ 313 K range but more CO2 at temperatures falling out of this range. Grand canonical Monte Carlo simulations of the adsorption isotherms confirmed that the preferential adsorption sites of both gases are the triangular channels (micropores) in close proximity to the polar pillar. While CO2 interacts with the thiazolium ring in an “end-on” fashion through its O atoms, N2O adopts a “side-on” configuration through its three atoms simultaneously. These findings open new horizons in the discovery of functional materials that may discriminate between polluting gases through selective adsorption at different temperatures.

Plasma-molten Metal and/or Liquid Interactions for Materials/Chemical Processing

Several grand challenges in energy storage and conversion need the discovery of functional materials that many agree will be composed of complex compositions at nanoscale. In this regard, plasma based materials processing has been shown to be promising for combinatorial techniques and scalable processing. The use of plasma oxidation of liquid precursors allows for creation of metastable complex oxide particles with compositional control.1 A number of examples will be discussed in which the above two techniques are currently being used for accelerating the development of a variety of catalysts including electrocatalysts and materials for storage applications.This talk will highlight our efforts to understand the role of plasmas under two categories: (a) the synergistic effects hydrogen and nitrogen plasma interactions with molten metals;2 and (b) the oxygen plasma-liquid droplet interactions.3 To gain insights into these mechanisms we have studied the interaction of hydrogen and nitrogen plasmas with low melting point metals, primarily with gallium. Absorption/desorption experiments as well as theoretical-computational calculations were performed. Experiments have shown an increment of adsorbed gaseous species into the molten metal in the presence of plasmas. In the case of oxygen plasma-liquid droplet interactions for creating complex oxides, the role of solvated electrons, oxygen radicals and heating effects will be discussed. Finally, the use of plasmas for achieving liquid phase epitaxial growth of GaN and related materials will be discussed.4 Author acknowledge primary funding support from NSF Solar Project (DMS 1125909), and NSF EPSCoR (1355438).

Unsupervised discovery of solid-state lithium ion conductors

Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.