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Highlights

Highlights

Schematic of the molecular structure and vibrational modes of the perylene diimide molecules used in this study.
Schematic of the molecular structure and vibrational modes of the perylene diimide molecules used in this study.
Jun 5, 2018
Northwestern University

Probing Intermolecular Interactions with Intramolecular Resolution

At the nanometer-scale, the surface area to volume ratio increases substantially compared to bulk materials. Consequently, methods for functionalizing and passivating surfaces can play a dominant role in determining the properties of nanomaterials. Of particular interest are self-assembled monolayers of organic molecules that have been widely used to control the electronic, optical, chemical, and frictional properties of nanomaterials in a range of applications.
(a) Thin-film transistor structures. (b) Mobility as a function of PEI concentration. (c) Schematic energy band diagram illustrating the shift of the In2O3 work function with PEI.
(a) Thin-film transistor structures. (b) Mobility as a function of PEI concentration. (c) Schematic energy band diagram illustrating the shift of the In2O3 work function with PEI.
Jun 5, 2018
Northwestern University

High Performance Heterojunction Oxide Thin Film Transistors

Due to their outstanding electronic properties and high optical transparency, metal oxide thin-film transistors have significant potential in state-of-the-art flat panel display technologies. Here, high performance solution-processed metal oxide thin-film transistors were realized by fabricating heterojunctions of indium oxide (In2O3) and polyethylenimine (PEI) as the semiconducting channel layer. Due to the tunable work function of the In2O3-PEI blends, electron mobilities as high as ~10 cm2V-1s-1 were obtained.
Synchrotron X-ray diffraction allows the evolution of the atomic structure of oxide films to be measured during crystallization.
Synchrotron X-ray diffraction allows the evolution of the atomic structure of oxide films to be measured during crystallization.
Jun 5, 2018
Northwestern University

Amorphous to Crystalline Transition in Indium Oxide Semiconductors

Amorphous oxide semiconductors commonly are indium oxides doped with other metal ions. Although it is known that the introduction of secondary metal ions decreases the degree of crystallinity and elevates the crystallization temperature, there is a lack of systematic study to compare and quantify the effects of different dopant elements. In an interdisciplinary study within IRG-2 of the Northwestern University MRSEC, in situ synchrotron X-ray characterization was performed to characterize the isochronal crystallization process of oxide thin films synthesized by pulsed laser deposition.
X-ray standing wave characterization resolves the atomic structure of the synthetic 2D material borophene.
X-ray standing wave characterization resolves the atomic structure of the synthetic 2D material borophene.
Jun 5, 2018
Northwestern University

Atomic-Scale Characterization of Synthetic Two-Dimensional Materials

Atomically thin two-dimensional (2D) materials exhibit superlative properties dictated by their intralayer atomic structure, which is typically derived from a limited number of thermodynamically stable bulk layered crystals (e.g., graphene from graphite). The growth of entirely synthetic 2D crystals – those with no corresponding bulk allotrope – would circumvent this dependence upon bulk thermodynamics and substantially expand the phase space available for structure-property engineering of 2D materials.
Design of the two self-assembled molecular dielectrics used in this study. The orientation of the molecular dipole is inverted between the two cases, allowing control over dielectric polarization.
Design of the two self-assembled molecular dielectrics used in this study. The orientation of the molecular dipole is inverted between the two cases, allowing control over dielectric polarization.
Jun 5, 2018
Northwestern University

Controlling Dielectric Polarization via Molecular Design

Dielectric materials play a critical role in determining the operating voltage in modern-day electronics. In particular, highly polarizable and ultrathin dielectrics enable low operating voltages and thus low power consumption.
Schematic of atomically-thin superlattices where two different transition-metal dichalcogenide monolayers are “seamlessly stitched together” without dislocations at the interfaces. S. Xie et al., Science 359, p. 1131-1136 (2018)
Schematic of atomically-thin superlattices where two different transition-metal dichalcogenide monolayers are “seamlessly stitched together” without dislocations at the interfaces. S. Xie et al., Science 359, p. 1131-1136 (2018)
Jun 5, 2018
Cornell University

Three-Atom Thick Fabrics Made by Seamless Stitching of Single-Layer Crystals

Joining different materials can lead to all kinds of breakthroughs. In electronics, this produces heterojunctions — the most fundamental components in solar cells and computer chips. The smoother the seam between two materials, the better the electronic devices will function.
Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as Hybrid Organic-Inorganic Perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional" space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a novel Bayesian optimization method that overcomes challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10 per cent) needed to complete an exhaustive search. We find an optimal composition within 15 iterations (plus or minus 10) in a HOIP compositional space containing 72 combinations, and within 29 iterations (plus or minus 11) when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. These proofs of concept demonstrate the undeniable promise of the novel Bayesian optimization methodology, presented here, to the field of materials discovery. H. Herbol et al., Nature Comp. Materials, under review (2018)
Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as Hybrid Organic-Inorganic Perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional" space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a novel Bayesian optimization method that overcomes challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10 per cent) needed to complete an exhaustive search. We find an optimal composition within 15 iterations (plus or minus 10) in a HOIP compositional space containing 72 combinations, and within 29 iterations (plus or minus 11) when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. These proofs of concept demonstrate the undeniable promise of the novel Bayesian optimization methodology, presented here, to the field of materials discovery. H. Herbol et al., Nature Comp. Materials, under review (2018)
Jun 5, 2018
Cornell University

Using Math to Search for a 'Needle in a Haystack' to Make Better Solar Cells

CCMR researchers have used mathematical methods, typically used in business forecasting, to suggest which combination of components will make the best solar cell materials in a “perovskite” arrangement. These materials are made in solution, essentially in a beaker, at room temperature. This makes them far more energy-conservative than traditional silicon solar cells. But researchers are spoiled for choice in terms of components that could be put into the “soup;” too many to make in the lab.
Enhancement of the Quality Factor of Metallic Glass Resonators via Cyclic Shear Training
Enhancement of the Quality Factor of Metallic Glass Resonators via Cyclic Shear Training
May 30, 2018
Yale University

Enhancement of the Quality Factor of Metallic Glass Resonators via Cyclic Shear Training

O’Hern and Schroers, Yale University  
Metallic glass resonators can possess larger quality factors (i.e., slower rates of energy dissipation) than typical polycrystalline metals, since metallic glasses are spatially homogeneous without dislocations and other topological defects. Using numerical simulations, we studied the energy dissipation mechanisms and  measured the quality factor Q in model metallic glass cantilevers (panel (a)). We bend the cantilever to a given strain ε, release it, and measure Q from the Fourier transform of the cantilever displacement as a function of time.
Teachers gained first hand experience on manufacturing equipment at Platt Technical HS, learning what skills their students need.
Teachers gained first hand experience on manufacturing equipment at Platt Technical HS, learning what skills their students need.
May 30, 2018
Yale University

Materials & Manufacturing Summer Teachers’ Institute (MMSTI)

Center for Research on Interface Structures and Phenomena, Yale University & Southern Connecticut State University  
The Materials and Manufacturing Summer Teachers’ Institute is a school-to-career initiative that targets STEM skills instruction for grades 7-12 in the New Haven and Bridgeport Public Schools. Three-day workshop designed to:
Simulation snapshots of meso-phases formed by H(CHOH)x(CH2)yH oligomers (polar in red and nonpolar in cyan) including Lamellae, Perforated Lamellae, Cylinders, and Disordered Micelles. The numbers indicate the domain periods.
Simulation snapshots of meso-phases formed by H(CHOH)x(CH2)yH oligomers (polar in red and nonpolar in cyan) including Lamellae, Perforated Lamellae, Cylinders, and Disordered Micelles. The numbers indicate the domain periods.
May 25, 2018
University of Minnesota - Twin Cities

Computational Design of High-χ Block Oligomers for Accessing 1-nm Features

Marc Hillmyer, Timothy Lodge, Ilja Siepmann University of Minnesota
The ability to precisely predict how molecular structure influences the microstructure of polymeric materials is the key towards the custom tailoring of desirable materials properties. Molecular dynamics simulations with atomistic level models were performed to design “high-χ” block oligomers that can self-assemble into 1-5 nm domains for next generation microelectronics applications. Simulations show that the microstructures formed by these oligomers can be tuned by varying the molecular weight and the chain architecture.

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