Theoretical Investigation of the Lattice Thermal Conductivities of II-IV-V2 Pnictide Semiconductors.

Ternary pnictide semiconductors with II-IV-V2 stoichiometry hold potential as cost-effective thermoelectric materials with suitable electronic transport properties, but their lattice thermal conductivities (κ) are typically too high. Insights into their vibrational properties are therefore crucial to finding strategies to reduce κ and achieve improved thermoelectric performance. We present a theoretical exploration of the lattice thermal conductivities for a set of pnictide semiconductors with ABX2 composition (A = Zn, Cd; B = Si, Ge, Sn; and X = P, As) using machine-learning-based regression algorithms to extract force constants from a reduced number of density functional theory simulations and then solving the Boltzmann transport equation for phonons. Our results align well with available experimental data, decreasing the mean absolute error by ∼3 W m-1 K-1 with respect to the best previous set of theoretical predictions. Zn-based ternary pnictides have, on average, more than double the thermal conductivity of the Cd-based compounds. Anisotropic behavior increases with the mass difference between A and B cations, but while the nature of the anion does not affect the structural anisotropy, the thermal conductivity anisotropy is typically higher for arsenides than for phosphides. We identify compounds such as CdGeAs2, for which nanostructuring to an affordable range of particle sizes could lead to κ values low enough for thermoelectric applications.

Design Principles Guided by DFT Calculations and High-Throughput Frameworks for the Discovery of New Diamond-like Chalcogenide Thermoelectric Materials.

Rational design principles are one pathway to discovering new materials. However, technological breakthroughs rarely occur in this way because these design principles are usually based on incremental advances that seldom lead to disruptive applications. The emergence of machine-learning (ML) and high-throughput (HT) techniques has changed the paradigm, opening up new possibilities for efficiently screening large chemical spaces and creating on-the-fly design principles for the discovery of novel materials with desired properties. In this work, the approach is used to discover novel thermoelectric (TE) materials based on quaternary diamond-like chalcogenides. A HT framework that integrates density functional theory calculations, ML, and the solution of the Boltzmann transport equation is used to efficiently rationalize the transport properties of these compounds and identify those with potential as TE materials, achieving ZT values above 2.

Emerging properties from mechanical tethering within a post-synthetically functionalised catenane scaffold.

Maintaining close spatial proximity of functional moieties within molecular systems can result in fascinating emergent properties. Whilst much work has been done on covalent tethering of functional units for myriad applications, investigations into mechanically linked systems are relatively rare. Formation of the mechanical bond is usually the final step in the synthesis of interlocked molecules, placing limits on the throughput of functionalised architectures. Herein we present the synthesis of a bis-azide [2]catenane scaffold that can be post-synthetically modified using CuAAC 'click' chemistry. In this manner we have been able to access functionalised catenanes from a common precursor and study the properties of electrochemically active, emissive and photodimerisable units within the mechanically interlocked system in comparison to non-interlocked analogues. Our data demonstrates that the greater (co-)conformational flexibility that can be obtained with mechanically interlocked systems compared to traditional covalent tethers paves the way for developing new functional molecules with exciting properties.

Orientational self-sorting in cuboctahedral Pd cages.

Cuboctahedral coordination cages of the general formula [Pd12L24]24+ (L = low-symmetry ligand) were analyzed theoretically and experimentally. With 350 696 potential isomers, the structural space of these assemblies is vast. Orientational self-sorting refers to the preferential formation of particular isomers within the pool of potential structures. Geometric and computational analyses predict the preferred formation of cages with a cis arrangement at the metal centers. This prediction was corroborated experimentally by synthesizing a [Pd12L24]24+ cage with a bridging 3-(4-(pyridin-4-yl)phenyl)pyridine ligand. A crystallographic analysis of this assembly showed exclusive cis coordination of the 3- and the 4-pyridyl donor groups at the Pd2+ ions.

Into the Unknown: How Computation Can Help Explore Uncharted Material Space.

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

The closed-edge structure of graphite and the effect of electrostatic charging.

The properties of graphite, and of few-layer graphene, can be strongly influenced by the edge structure of the graphene planes, but there is still much that we do not understand about the geometry and stability of these edges. We present an experimental and theoretical study of the closed edges of graphite crystals, and of the effect of an electric field on their structure. High-resolution transmission electron microscopy is used to image the edge structure of fresh graphite and of graphite that has been exposed to an electric field, which experiences a separation of the graphene layers. Computer simulations based on density functional theory are used to rationalise and quantify the preference for the formation of multiple concentric loops at the edges. A model is also presented to explain how the application of an electric field leads to the separation of the folded edges.

Operando spectroscopy study of the carbon dioxide electro-reduction by iron species on nitrogen-doped carbon.

The carbon-carbon coupling via electrochemical reduction of carbon dioxide represents the biggest challenge for using this route as platform for chemicals synthesis. Here we show that nanostructured iron (III) oxyhydroxide on nitrogen-doped carbon enables high Faraday efficiency (97.4%) and selectivity to acetic acid (61%) at very-low potential (-0.5 V vs silver/silver chloride). Using a combination of electron microscopy, operando X-ray spectroscopy techniques and density functional theory simulations, we correlate the activity to acetic acid at this potential to the formation of nitrogen-coordinated iron (II) sites as single atoms or polyatomic species at the interface between iron oxyhydroxide and the nitrogen-doped carbon. The evolution of hydrogen is correlated to the formation of metallic iron and observed as dominant reaction path over iron oxyhydroxide on oxygen-doped carbon in the overall range of negative potential investigated, whereas over iron oxyhydroxide on nitrogen-doped carbon it becomes important only at more negative potentials.