Highlights

  • MARVEL researchers investigate how MOF structures affect dieletric properties

    The modern microelectronics industry has a huge need for highly efficient electric insulators. Structures built around the medium with the lowest possible dielectric constant, that is, a vacuum, or air, may be their best bet. Metal-organic frameworks (MOFs) feature, among other advantages, large pores and poorly correlated scaffolding and so may serve this purpose well. Despite the huge potential, few studies have pursued either systematic experimental measurements or simulations to estimate the dielectric constant of MOFs. This lack of data makes it difficult to link properties and performance and hinders the design of an optimal structure–property correlation. NCCR MARVEL researchers looked to fill this gap in knowledge with a study into how the atomic and electronic structures of MOFs affect their dieletric properties. The results of their work were recently published in the Journal of the American Chemical Society. 

  • MARVEL researchers introduce a novel heat transport theory in quest for more efficient thermoelectrics

    MARVEL researchers have developed a novel microscopic theory that is able to describe heat transport in very general ways, and applies equally well to ordered or disordered materials such as crystals or glasses and to anything in between. This is not only a significant first—no transport equation has been able so far to account simultaneously for these two regimes—it also shows, surprisingly, that heat can tunnel, quantum-mechanically, rather than diffuse away, like an atomic vibration. The new equation will also allow the accurate prediction of the performance of thermoelectric materials for the first time. With ultralow, glass-like, thermal conductivity, such materials are one of the holy grails of energy research: they can turn heat into electricity or use electricity for cooling without needing to resort to pumps and environmentally harmful gases. The article was published in Nature Physics.

  • Strain modifies the valley structure of 2D materials, leads to remarkable improvements in conductivity

    NCCR MARVEL researchers have investigated strain as a means of engineering the valley structure of 2D materials. They found that it leads to remarkable improvements in conductivity—in the example of arsenene, they showed the enhancement factor to be on the order of 600%—by suppressing intervalley scattering and thus enhancing electron mobility. The approach could be used to design or discover 2D materials that combine both good carrier mobility and the energy gap needed for use in logical devices. The research has been published in Nano Letters. 

  • New study gives compelling evidence that tungsten diphosphide is a type-II Weyl semimetal

    Researchers at NCCR MARVEL have combined first principles calculations with soft X-ray angle-resolved photoemission spectroscopy to examine tungsten diphosphide’s electronic structure, characterizing its Weyl nodes for the very first time. In agreement with density functional theory calculations, the results revealed two pairs of Weyl nodes lying at different binding energies. The observation of the Weyl nodes, as well as the tilted cone-like dispersions in the vicinity of the nodal points, provides compelling evidence that the material is a robust type-II Weyl semimetal with broken Lorentz invariance. This is as MARVEL researchers predicted two years ago. The research has been published in Physical Review Letters as an Editor's Suggestion.

  • Researchers discover self-healing catalyst for potential large-scale use in safe hydrogen production and storage

    Researchers working within NCCR MARVEL have discovered a self-healing catalyst that can be used to release hydrogen through the hydrolytic dehydrogenation of ammonia borane. The catalyst, SION-X, is based on the abundant mineral Jacquesdietrichite, is sustainable, air stable and can be easily regenerated, stored and handled. These characteristics mean that it may offer significant advantages over existing catalysts used in the production of the clean and renewable energy carrier hydrogen. The research has been published in the Journal of Materials Chemistry A. 

  • New biologically derived metal-organic framework mimics DNA

    EPFL chemical engineers led by Kyriakos Stylianou, experimental group leader in MARVEL Design and Discovery Project 4, have synthesized a biologically-derived metal-organic framework on which the hydrogen bonding that forms the DNA double helix can be mimicked and studied like never before. The paper, Nucleobase pairing and photodimerization in a biologically derived metal-organic framework nanoreactor, has been published in Nature Communications.

  • Novel MD simulation sheds light on mystery of hydrated electron's structure

    Scientists have known about the existence of the hydrated electron -- extra electrons solvated in liquid water -- for more than fifty years. They haven't been too sure of its structure though. MARVEL researchers at the University of Zurich, ETH and the Swiss National Supercomputing Center CSCS have now taken a huge step towards solving the mystery. Their paper, 'Dynamics of the Bulk Hydrated Electron from Many Body Wave Function Theory," has been published in Angewandte Chemie.

  • Study shows pressure induces unusually high electrical conductivity in polyiodide

    A study into the effects of high mechanical pressure on the polyiodide TEAI showed that it brings unusually high electrical conductivity starting from insulating state, suggesting that the material may be useful as a switchable semiconductor. This system could represent an alternative to gel electrolytes and ionic liquids in dye-synthesized solar cells. The paper, Pressure-induced Polymerization and Electrical Conductivity of a Polyiodide, has been published as a Very Important Paper in Angewandte Chemie.

  • MARVEL researchers improve description of defective oxides with first principles calculation of site-dependent +U correction parameters

    Understanding how defects can affect ground-state properties, promote phase transitions, or enable entirely new functionalities in some strongly correlated oxides has become a subject of major interest in the field of design and discovery of novel functional materials. SrMnO3 (SMO) is a particularly interesting example, but better characterization is needed. MARVEL researchers have now a developed a method that may lead to more accurate predictions of the energetics of defects associated with in-gap states in semiconductors or insulators.

  • MARVEL DFT calculations underpin theoretical work on novel water-splitting catalyst

    EPFL chemists have developed a new iron-nickel oxide catalyst for water splitting, the reaction that produces hydrogen fuel. The patent-pending catalyst shows significantly higher activity in the oxygen-evolution part of reaction than conventional nickel iron oxide catalysts. The work, now published in ACS Central Science, was supported by the density functional theory (DFT) computations of NCCR MARVEL's Clémence Corminboeuf and her postdoctoral student Michael Busch: their work underpinned the possible theoretical explanations.

  • Researchers develop a recyclable catalyst that uses CO2 to produce benzimidazoles

    Transforming emitted CO2 into valuable products has been proposed as a way of reducing the amount of this greenhouse gas released into the atmosphere—using it as a raw material could help both close the carbon cycle and reduce the consumption of petrochemicals. Dr. Kyriakos C. Stylianou of EPFL and NCCR MARVEL and EPFL's Professor Paul Dyson have developed a recyclable catalyst that can be used to produce valuable products. The research has been published in Angewandte Chemie.

  • MARVEL labs develop a machine learning model for the electron density

    NCCR MARVEL’s Michele Ceriotti and Clemence Corminboeuf have joined forces to develop an innovative machine learning model for the electron density. Knowledge of a system’s electron density gives access in principle to all its ground state properties. However, the computations needed to determine the electronic structure from first principles remain costly. A machine learning approach promises to lighten this computational burden significantly.