Direct Observation of Transient Structural Dynamics of Atomically Thin Halide Perovskite Nanowires.

Halide perovskite is a unique dynamical system, whose structural and chemical processes happening across different timescales have significant impact on its physical properties and device-level performance. However, due to its intrinsic instability, real-time investigation of the structure dynamics of halide perovskite is challenging, which hinders the systematic understanding of the chemical processes in the synthesis, phase transition, and degradation of halide perovskite. Here, we show that atomically thin carbon materials can stabilize ultrathin halide perovskite nanostructures against otherwise detrimental conditions. Moreover, the protective carbon shells enable atomic-level visualization of the vibrational, rotational, and translational movement of halide perovskite unit cells. Albeit atomically thin, protected halide perovskite nanostructures can maintain their structural integrity up to an electron dose rate of 10,000 e-/Å2·s while exhibiting unusual dynamical behaviors pertaining to the lattice anharmonicity and nanoscale confinement. Our work demonstrates an effective method to protect beam-sensitive materials during in situ observation, unlocking new solutions to study new modes of structure dynamics of nanomaterials.

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride.

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.

Lead-free Cesium Europium Halide Perovskite Nanocrystals.

Because of the toxicity of lead, searching for a lead-free halide perovskite semiconducting material with comparable optical and electronic properties is of great interest. Rare-earth-based halide perovskite represents a promising class of materials for this purpose. In this work, we demonstrate the solution-phase synthesis of single-crystalline CsEuCl3 nanocrystals with a uniform size distribution centered around 15 nm. The CsEuCl3 nanocrystals have photoluminescence emission centered at 435 nm, with a full width at half-maximum of 19 nm. Furthermore, CsEuCl3 nanocrystals can be embedded in a polymer matrix that provides enhanced stability under continuous laser irradiation. Lead-free rare-earth cesium europium halide perovskite nanocrystals represent a promising candidate to replace lead halide perovskites.

Self-Assembly of Two-Dimensional Perovskite Nanosheet Building Blocks into Ordered Ruddlesden-Popper Perovskite Phase.

The self-assembly of nanoparticles, a process whereby nanocrystal building blocks organize into even more ordered superstructures, is of great interest to nanoscience. Here we report the layer-by-layer assembly of 2D perovskite nanosheet building blocks. Structural analysis reveals that the assembled superlattice nanocrystals match with the layered Ruddlesden-Popper perovskite phase. This assembly proves reversible, as these superlattice nanocrystals can be reversibly exfoliated back into their building blocks via sonication. This study demonstrates the opportunity to further understand and exploit thermodynamics to increase order in a system of nanoparticles and to study emergent optical properties of a superlattice from 2D, weakly attracted, perovskite building blocks.

Data-Driven Investigation of Tellurium-Containing Semiconductors for CO2 Reduction: Trends in Adsorption and Scaling Relations.

Light-assisted conversion of CO2 into liquid fuels is one of several possible approaches to combating the rise of carbon dioxide emissions. Unfortunately, there are currently no known materials that are efficient, selective, or active enough to facilitate the photocatalytic CO2 reduction reaction (CO2RR) at an industrial scale. In this work, we employ density functional theory to explore potential tellurium-containing photocathodes for the CO2RR by observing trends in adsorption properties arising from over 350 *H, 200 *CO, and 110 *CHO surface-adsorbate structures spanning 39 surfaces of 11 materials. Our results reveal a scaling relationship between *CHO and *H chemisorption energies and charge transfer values, while the scaling relation (typically found in transition metals) between *CO and *CHO adsorption energies is absent. We hypothesize the scaling relation between *H and *CHO to be related to the lone electron located on the bonding carbon atom, while the lack of scaling relation in *CO we attribute to the ability of the lone pair on the C atom to form multiple bonding modes. We compute two predominant orbital-level interactions in the *CO-surface bonds (either using s or p orbitals) in addition to bonding modes involving both σ and π interactions using the Crystal Orbital Hamiltonian Population analysis. We demonstrate that bonds involving the C s orbital are more chemisorptive than the C p orbitals of CO. In general, chemisorption trends demonstrate decreased *H competition with respect to *CO adsorption and enhanced *CHO stability. Finally, we uncover simple element-specific design rules with Te, Se, and Ga sites showing increased competition and Zn, Yb, Rb, Br, and Cl sites showing decreased competition for hydrogen adsorption. We anticipate that these trends will help further screen these materials for potential CO2RR performance.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis.

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.