Remote epitaxy through graphene enables two-dimensional material-based layer transfer.

Epitaxy-the growth of a crystalline material on a substrate-is crucial for the semiconductor industry, but is often limited by the need for lattice matching between the two material systems. This strict requirement is relaxed for van der Waals epitaxy, in which epitaxy on layered or two-dimensional (2D) materials is mediated by weak van der Waals interactions, and which also allows facile layer release from 2D surfaces. It has been thought that 2D materials are the only seed layers for van der Waals epitaxy. However, the substrates below 2D materials may still interact with the layers grown during epitaxy (epilayers), as in the case of the so-called wetting transparency documented for graphene. Here we show that the weak van der Waals potential of graphene cannot completely screen the stronger potential field of many substrates, which enables epitaxial growth to occur despite its presence. We use density functional theory calculations to establish that adatoms will experience remote epitaxial registry with a substrate through a substrate-epilayer gap of up to nine ångströms; this gap can accommodate a monolayer of graphene. We confirm the predictions with homoepitaxial growth of GaAs(001) on GaAs(001) substrates through monolayer graphene, and show that the approach is also applicable to InP and GaP. The grown single-crystalline films are rapidly released from the graphene-coated substrate and perform as well as conventionally prepared films when incorporated in light-emitting devices. This technique enables any type of semiconductor film to be copied from underlying substrates through 2D materials, and then the resultant epilayer to be rapidly released and transferred to a substrate of interest. This process is particularly attractive in the context of non-silicon electronics and photonics, where the ability to re-use the graphene-coated substrates allows savings on the high cost of non-silicon substrates.

Single Atomic Layer Ferroelectric on Silicon.

A single atomic layer of ZrO2 exhibits ferroelectric switching behavior when grown with an atomically abrupt interface on silicon. Hysteresis in capacitance-voltage measurements of a ZrO2 gate stack demonstrate that a reversible polarization of the ZrO2 interface structure couples to the carriers in the silicon. First-principles computations confirm the existence of multiple stable polarization states and the energy shift in the semiconductor electron states that result from switching between these states. This monolayer ferroelectric represents a new class of materials for achieving devices that transcend conventional complementary metal oxide semiconductor (CMOS) technology. Significantly, a single atomic layer ferroelectric allows for more aggressively scaled devices than bulk ferroelectrics, which currently need to be thicker than 5-10 nm to exhibit significant hysteretic behavior (Park, et al. Adv. Mater. 2015, 27, 1811).

Reduced overpotentials for electrocatalytic water splitting over Fe- and Ni-modified BaTiO3.

Water electrolysis is a key technology for the replacement of fossil fuels by environmentally friendly alternatives, but state-of-the-art water oxidation catalysts rely on rare elements such as Pt groups and other noble metals. In this article, we employ first-principles calculations to explore the potential of modified barium titanate (BaTiO3), an inexpensive perovskite oxide that can be synthesized from earth-abundant precursors, for the design of efficient water oxidation electrocatalysts. Our calculations identify Fe and Ni doping as a means to improve the electrical conductivity and to reduce the overpotential required for water oxidation over BaTiO3. Based on computed Pourbaix diagrams and pH/potential-dependent surface phase diagrams, we further show that BaTiO3 is stable under reaction conditions and is not sensitive with respect to poisoning by reaction intermediates and hydrogen adsorption. This proof of concept demonstrates that even minor compositional modifications of existing materials may greatly improve their catalytic activity, a fact that is often neglected when larger composition spaces are screened.

Control of valence and conduction band energies in layered transition metal phosphates via surface functionalization.

Layered transition metal phosphates and phosphites (TMPs) are a class of materials composed of layers of 2D sheets bound together via van der Waals interactions and/or hydrogen bonds. Explored primarily for use in proton transfer, their unique chemical tunability also makes TMPs of interest for forming large-scale hybrid materials. Further, unlike many layered materials, TMPs can readily be solution exfoliated to form single 2D sheets or bilayers, making them exciting candidates for a variety of applications. However, the electronic properties of TMPs have largely been unstudied to date. In this work, we use first-principles computations to investigate the atomic and electronic structure of TMPs with a variety of stoichiometries. We demonstrate that there exists a strong linear relationship between the band gap and the ionic radius of the transition metal cation in these materials, and show that this relationship, which opens opportunities for engineering new compositions with a wide range of band gaps, arises from constraints imposed by the phosphorus-oxygen bond geometry. In addition, we find that the energies of the valence and conduction band edges can be systematically tuned over a range of ∼3 eV via modification of the functional group extending from the phosphorus. Based on the Hammett constant of this functional group, we identify a simple, predictive relationship for the ionization potential and electron affinity of layered TMPs. Our results thus provide guidelines for systematic design of TMP-derived functional materials, which may enable new approaches for optimizing charge transfer in electronics, photovoltaics, electrocatalysts, and other applications.

Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.

The shape, size, and composition of catalyst nanoparticles can have a significant influence on catalytic activity. Understanding such structure-reactivity relationships is crucial for the optimization of industrial catalysts and the design of novel catalysts with enhanced properties. In this letter, we employ a combination of first-principles computations and large-scale Monte-Carlo simulations with highly accurate neural network potentials to study the equilibrium surface structure and composition of bimetallic Au/Cu nanoparticles (NPs), which have recently been of interest as stable and efficient CO2 reduction catalysts. We demonstrate that the inclusion of explicit water molecules at a first-principles level of accuracy is necessary to predict experimentally observed trends in Au/Cu NP surface composition; in particular, we find that Au-coated core-shell NPs are thermodynamically favored in vacuum, independent of Au/Cu chemical potential and NP size, while NPs with mixed Au-Cu surfaces are preferred in aqueous solution. Furthermore, we show that both CO and O2 adsorption energies differ significantly for NPs with the equilibrium surface composition found in water and those with the equilibrium surface composition found in vacuum, suggesting large changes in CO2 reduction activity. Our results emphasize the importance of understanding and being able to predict the effects of catalytic environment on catalyst structure and activity. In addition, they demonstrate that first-principles-based neural network potentials provide a promising approach for accurately investigating the relationships between solvent, surface composition and morphology, surface electronic structure, and catalytic activity in systems composed of thousands of atoms.

Hybrid chromophore/template nanostructures: a customizable platform material for solar energy storage and conversion.

Challenges with cost, cyclability, and/or low energy density have largely prevented the development of solar thermal fuels, a potentially attractive alternative energy technology based on molecules that can capture and store solar energy as latent heat in a closed cycle. In this paper, we present a set of novel hybrid photoisomer/template solar thermal fuels that can potentially circumvent these challenges. Using first-principles computations, we demonstrate that these fuels, composed of organic photoisomers bound to inexpensive carbon-based templates, can reversibly store solar energy at densities comparable to Li-ion batteries. Furthermore, we show that variation of the template material in combination with the photoisomer can be used to optimize many of the key performance metrics of the fuel-i.e., the energy density, the storage lifetime, the temperature of the output heat, and the efficiency of the solar-to-heat conversion. Our work suggests that the solar thermal fuels concept can be translated into a practical and highly customizable energy storage and conversion technology.

Azobenzene-functionalized carbon nanotubes as high-energy density solar thermal fuels.

Solar thermal fuels, which reversibly store solar energy in molecular bonds, are a tantalizing prospect for clean, renewable, and transportable energy conversion/storage. However, large-scale adoption requires enhanced energy storage capacity and thermal stability. Here we present a novel solar thermal fuel, composed of azobenzene-functionalized carbon nanotubes, with the volumetric energy density of Li-ion batteries. Our work also demonstrates that the inclusion of nanoscale templates is an effective strategy for design of highly cyclable, thermally stable, and energy-dense solar thermal fuels.

X-ray transient absorption and picosecond IR spectroscopy of fulvalene(tetracarbonyl)diruthenium on photoexcitation.

Caught in the light: The fulvalene diruthenium complex shown on the left side of the picture captures sun light, causing initial Ru-Ru bond rupture to furnish a long-lived triplet biradical of syn configuration. This species requires thermal activation to reach a crossing point (middle) into the singlet manifold on route to its thermal storage isomer on the right through the anti biradical.

Predicting HSE band gaps from PBE charge densities via neural network functionals.

Density functional theory (DFT) has become a standard method for ab initio calculations of material properties. However, it has a number of shortcomings, particularly in predicting key properties, such as band gap and optical spectra, which are dependent on excited states. To treat such properties, more accurate approaches such as GW or DFT with hybrid functionals (including HSE, PBE0, and B3LYP, to name a few) can be employed; however, these approaches are unfeasible for many large and/or complex systems due to their high computational cost and large memory requirements. In this work, we investigate the ability to train neural networks of the traditional DFT charge density computed with a standard PBE functional to accurately predict HSE band gaps. We show that a single network PBE charge density functional can predict the HSE band gap of seven different materials-silicon, gallium arsenide, molybdenum disulfide, germanium, tin phosphate, titanium phosphate, and zirconium phosphate-under a wide variety of conditions with an RSME of 172.6 meV, which is 34% better accuracy than standard regression between the PBE and HSE band gaps. This approach, which, in principle, can be used to map PBE charge densities to band gaps or other properties computed with any higher accuracy method, has the potential to decrease computational costs, increase prediction accuracy, and enable accurate high-throughput screening for a wide variety of complex materials systems.

Publisher Correction: First-principles design of nanostructured hybrid photovoltaics based on layered transition metal phosphates.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Electronic Origin and Kinetic Feasibility of the Lattice Oxygen Participation During the Oxygen Evolution Reaction on Perovskites.

Density functional theory is employed to investigate the electronic origin and feasibility of surface lattice oxygen (Osurf) participation during the oxygen evolution reaction (OER) on perovskites. Osurf participation occurs via the nonelectrochemical pathway in which adsorbed atomic oxygen (O*) diffuses from the transition-metal site to the oxygen site, and then Osurf shifts out of the surface plane to react with O* to form Osurf-O* and a surface oxygen vacancy. The different thermodynamic driving forces of Osurf participation on LaMO3-δ (M = Ni, Co, and Cu) are explained by the changes in the oxidation state of the transition-metal site throughout the reaction. We show that Osurf participation on LaNiO3 cannot be hindered by Osurf protonation in the OER potential range. By including the coverage effect and utilizing the implicit solvent model, we finally show that lattice oxygen mechanism is more feasible than the conventional mechanism for OER on LaNiO3.

Exceptional electrocatalytic oxygen evolution via tunable charge transfer interactions in La0.5Sr1.5Ni1-xFexO4±Î´ Ruddlesden-Popper oxides.

The electrolysis of water is of global importance to store renewable energy and the methodical design of next-generation oxygen evolution catalysts requires a greater understanding of the structural and electronic contributions that give rise to increased activities. Herein, we report a series of Ruddlesden-Popper La0.5Sr1.5Ni1-xFexO4±Î´ oxides that promote charge transfer via cross-gap hybridization to enhance electrocatalytic water splitting. Using selective substitution of lanthanum with strontium and nickel with iron to tune the extent to which transition metal and oxygen valence bands hybridize, we demonstrate remarkable catalytic activity of 10 mA cm-2 at a 360 mV overpotential and mass activity of 1930 mA mg-1ox at 1.63 V via a mechanism that utilizes lattice oxygen. This work demonstrates that Ruddlesden-Popper materials can be utilized as active catalysts for oxygen evolution through rational design of structural and electronic configurations that are unattainable in many other crystalline metal oxide phases.

Impact of thermally dead volume on phonon conduction along silicon nanoladders.

Thermal conduction in complex periodic nanostructures remains a key area of open questions and research, and a particularly provocative and challenging detail is the impact of nanoscale material volumes that do not lie along the optimal line of sight for conduction. Here, we experimentally study thermal transport in silicon nanoladders, which feature two orthogonal heat conduction paths: unobstructed line-of-sight channels in the axial direction and interconnecting bridges between them. The nanoladders feature an array of rectangular holes in a 10 µm long straight beam with a 970 nm wide and 75 nm thick cross-section. We vary the pitch of these holes from 200 nm to 1100 nm to modulate the contribution of bridges to the net transport of heat in the axial direction. The effective thermal conductivity, corresponding to reduced heat flux, decreases from ∼45 W m-1 K-1 to ∼31 W m-1 K-1 with decreasing pitch. By solving the Boltzmann transport equation using phonon mean free paths taken from ab initio calculations, we model thermal transport in the nanoladders, and experimental results show excellent agreement with the predictions to within ∼11%. A combination of experiments and calculations shows that with decreasing pitch, thermal transport in nanoladders approaches the counterpart in a straight beam equivalent to the line-of-sight channels, indicating that the bridges constitute a thermally dead volume. This study suggests that ballistic effects are dictated by the line-of-sight channels, providing key insights into thermal conduction in nanostructured metamaterials.

First-principles design of nanostructured hybrid photovoltaics based on layered transition metal phosphates.

The performance of bulk organic and hybrid organic-inorganic heterojunction photovoltaics is often limited by high carrier recombination arising from strongly bound excitons and low carrier mobility. Structuring materials to minimize the length scales required for exciton separation and carrier collection is therefore a promising approach for improving efficiency. In this work, first-principles computations are employed to design and characterize a new class of photovoltaic materials composed of layered transition metal phosphates (TMPs) covalently bound to organic absorber molecules to form nanostructured superlattices. Using a combination of transition metal substitution and organic functionalization, the electronic structure of these materials is systematically tuned to design a new hybrid photovoltaic material predicted to exhibit very low recombination due to the presence of a local electric field and spatially isolated, high mobility, two-dimensional electron and hole conducting channels. Furthermore, this material is predicted to have a large open-circuit voltage of 1.7 V. This work suggests that hybrid TMPs constitute an interesting class of materials for further investigation in the search for achieving high efficiency, high power, and low cost photo Zirconium phosphate was chosen, in part, due to previous experiment voltaics.

Directional Phonon Suppression Function as a Tool for the Identification of Ultralow Thermal Conductivity Materials.

Boundary-engineering in nanostructures has the potential to dramatically impact the development of materials for high- efficiency conversion of thermal energy directly into electricity. In particular, nanostructuring of semiconductors can lead to strong suppression of heat transport with little degradation of electrical conductivity. Although this combination of material properties is promising for thermoelectric materials, it remains largely unexplored. In this work, we introduce a novel concept, the directional phonon suppression function, to unravel boundary-dominated heat transport in unprecedented detail. Using a combination of density functional theory and the Boltzmann transport equation, we compute this quantity for nanoporous silicon materials. We first compute the thermal conductivity for the case with aligned circular pores, confirming a significant thermal transport degradation with respect to the bulk. Then, by analyzing the information on the directionality of phonon suppression in this system, we identify a new structure of rectangular pores with the same porosity that enables a four-fold decrease in thermal transport with respect to the circular pores. Our results illustrate the utility of the directional phonon suppression function, enabling new avenues for systematic thermal conductivity minimization and potentially accelerating the engineering of next-generation thermoelectric devices.

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods.

Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. This work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet's ability to create exciting post-processing analysis tools and to open the door to improving ab initio methods themselves with these powerful machine learning techniques.

Phonon Conduction in Silicon Nanobeam Labyrinths.

Here we study single-crystalline silicon nanobeams having 470 nm width and 80 nm thickness cross section, where we produce tortuous thermal paths (i.e. labyrinths) by introducing slits to control the impact of the unobstructed "line-of-sight" (LOS) between the heat source and heat sink. The labyrinths range from straight nanobeams with a complete LOS along the entire length to nanobeams in which the LOS ranges from partially to entirely blocked by introducing slits, s = 95, 195, 245, 295 and 395 nm. The measured thermal conductivity of the samples decreases monotonically from ~47 W m-1 K-1 for straight beam to ~31 W m-1 K-1 for slit width of 395 nm. A model prediction through a combination of the Boltzmann transport equation and ab initio calculations shows an excellent agreement with the experimental data to within ~8%. The model prediction for the most tortuous path (s = 395 nm) is reduced by ~14% compared to a straight beam of equivalent cross section. This study suggests that LOS is an important metric for characterizing and interpreting phonon propagation in nanostructures.

PbTiO3(001) Capped with ZnO(112Ì 0): An ab Initio Study of Effect of Substrate Polarization on Interface Composition and CO2 Dissociation.

Catalytic conversion of CO2 into useful chemicals is an attractive alternative to expensive physical carbon sequestration methods. However, this approach is challenging because current chemical conversion methods employ high temperatures or pressures, thereby increasing cost and potentially leading to net carbon positive processes. In this paper, we examine the interface properties of ZnO(112̅0)/PbTiO3 and its surface interaction with CO2, CO and O. We show that the stoichiometry of the stable interface is dependent on the substrate polarization and can be controlled by changing the growth conditions. Using a model reaction, we demonstrate that a dynamically tuned catalysis scheme could enable significantly lower-energy approaches for CO2 conversion.

Water electrolysis on La(1-x)Sr(x)CoO(3-δ) perovskite electrocatalysts.

Perovskite oxides are attractive candidates as catalysts for the electrolysis of water in alkaline energy storage and conversion systems. However, the rational design of active catalysts has been hampered by the lack of understanding of the mechanism of water electrolysis on perovskite surfaces. Key parameters that have been overlooked include the role of oxygen vacancies, B-O bond covalency, and redox activity of lattice oxygen species. Here we present a series of cobaltite perovskites where the covalency of the Co-O bond and the concentration of oxygen vacancies are controlled through Sr(2+) substitution into La(1-x)Sr(x)CoO(3-δ) . We attempt to rationalize the high activities of La(1-x)Sr(x)CoO(3-δ) through the electronic structure and participation of lattice oxygen in the mechanism of water electrolysis as revealed through ab initio modelling. Using this approach, we report a material, SrCoO2.7, with a high, room temperature-specific activity and mass activity towards alkaline water electrolysis.

Ab Initio Approach for Prediction of Oxide Surface Structure, Stoichiometry, and Electrocatalytic Activity in Aqueous Solution.

The design of efficient, stable, and inexpensive catalysts for oxygen evolution and reduction is crucial for the development of electrochemical energy conversion devices such as fuel cells and metal-air batteries. Currently, such design is limited by challenges in atomic-scale experimental characterization and computational modeling of solid-liquid interfaces. Here, we begin to address these issues by developing a general-, first-principles-, and electrochemical-principles-based framework for prediction of catalyst surface structure, stoichiometry, and stability as a function of pH, electrode potential, and aqueous cation concentration. We demonstrate the approach by determining the surface phase diagram of LaMnO3, which has been studied for oxygen evolution and reduction and computing the reaction overpotentials on the relevant surface phases. Our results illustrate the critical role of solvated cation species in governing the catalyst surface structure and stoichiometry, and thereby catalytic activity, in aqueous solution.