High Thermoelectric Performance in Rhombohedral GeSe-LiBiTe2.

GeSe, an analogue of SnSe, shows promise in exhibiting exceptional thermoelectric performance in the Pnma phase. The constraints on its dopability, however, pose challenges in attaining optimal carrier concentrations and improving ZT values. This study demonstrates a crystal structure evolution strategy for achieving highly doped samples and promising ZTs in GeSe via LiBiTe2 alloying. A rhombohedral phase (R3m) can be stabilized in the GeSe-LiBiTe2 system, further evolving into a cubic (Fm3̅m) phase with a rising temperature. The band structures of GeSe-LiBiTe2 in the rhombohedral and cubic phases feature a similar multiple-valley energy-converged valence band of L and Σ bands. The observed high carrier concentration (∼1020 cm-3) reflects the effective convergence of these bands, enabling a high density-of-states effective mass and an enhanced power factor. Moreover, a very low lattice thermal conductivity of 0.6-0.5 W m-1 K-1 from 300 to 723 K is achieved in 0.9GeSe-0.1LiBiTe2, approaching the amorphous limit value. This remarkably low lattice thermal conductivity is related to phonon scattering from point defects, planar vacancies, and ferroelectric instability-induced low-energy Einstein oscillators. Finally, a maximum ZT value of 1.1 to 1.3 at 723 K is obtained, with a high average ZT value of over 0.8 (400-723 K) in 0.9GeSe-0.1LiBiTe2 samples. This study establishes a viable route for tailoring crystal structures to significantly improve the performance of GeSe-related compounds.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy.

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Implications and Optimization of Domain Structures in IV-VI High-Entropy Thermoelectric Materials.

High-entropy semiconductors are now an important class of materials widely investigated for thermoelectric applications. Understanding the impact of chemical and structural heterogeneity on transport properties in these compositionally complex systems is essential for thermoelectric design. In this work, we uncover the polar domain structures in the high-entropy PbGeSnSe1.5Te1.5 system and assess their impact on thermoelectric properties. We found that polar domains induced by crystal symmetry breaking give rise to well-structured alternating strain fields. These fields effectively disrupt phonon propagation and suppress the thermal conductivity. We demonstrate that the polar domain structures can be modulated by tuning crystal symmetry through entropy engineering in PbGeSnAgxSbxSe1.5+xTe1.5+x. Incremental increases in the entropy enhance the crystal symmetry of the system, which suppresses domain formation and loses its efficacy in suppressing phonon propagation. As a result, the room-temperature lattice thermal conductivity increases from κL = 0.63 Wm-1 K-1 (x = 0) to 0.79 Wm-1 K-1 (x = 0.10). In the meantime, the increase in crystal symmetry, however, leads to enhanced valley degeneracy and improves the weighted mobility from µw = 29.6 cm2 V-1 s-1 (x = 0) to 35.8 cm2 V-1 s-1 (x = 0.10). As such, optimal thermoelectric performance can be achieved through entropy engineering by balancing weighted mobility and lattice thermal conductivity. This work, for the first time, studies the impact of polar domain structures on thermoelectric properties, and the developed understanding of the intricate interplay between crystal symmetry, polar domains, and transport properties, along with the impact of entropy control, provides valuable insights into designing GeTe-based high-entropy thermoelectrics.

Influence of Composition and Structure on the Optoelectronic Properties of Photocatalytic Bi4NbO8Cl-Bi2GdO4Cl Intergrowths.

Mixed metal oxyhalides are an exciting class of photocatalysts, capable of the sustainable generation of fuels and remediation of pollutants with solar energy. Bismuth oxyhalides of the types Bi4MO8X (M = Nb and Ta; X = Cl and Br) and Bi2AO4X (A = most lanthanides; X = Cl, Br, and I) have an electronic structure that imparts photostability, as their valence band maxima (VBM) are composed of O 2p orbitals rather than X np orbitals that typify many other bismuth oxyhalides. Here, flux-based synthesis of intergrowth Bi4NbO8Cl-Bi2GdO4Cl is reported, testing the hypothesis that both intergrowth stoichiometry and M identity serve as levers toward tunable optoelectronic properties. X-ray scattering and atomically resolved electron microscopy verify intergrowth formation. Facile manipulation of the Bi4NbO8Cl-to-Bi2GdO4Cl ratio is achieved with the specific ratio influencing both the crystal and electronic structures of the intergrowths. This compositional flexibility and crystal structure engineering can be leveraged for photocatalytic applications, with comparisons to the previously reported Bi4TaO8Cl-Bi2GdO4Cl intergrowth revealing how subtle structural and compositional features can impact photocatalytic materials.

Ultrathin silicon nitride microchip for in situ/operando microscopy with high spatial resolution and spectral visibility.

Utilization of in situ/operando methods with broad beams and localized probes has accelerated our understanding of fluid-surface interactions in recent decades. The closed-cell microchips based on silicon nitride (SiNx) are widely used as "nanoscale reactors" inside the high-vacuum electron microscopes. However, the field has been stalled by the high background scattering from encapsulation (typically ~100 nanometers) that severely limits the figures of merit for in situ performance. This adverse effect is particularly notorious for gas cell as the sealing membranes dominate the overall scattering, thereby blurring any meaningful signals and limiting the resolution. Herein, we show that by adopting the back-supporting strategy, encapsulating membrane can be reduced substantially, down to ~10 nanometers while maintaining structural resiliency. The systematic gas cell work demonstrates advantages in figures of merit for hitherto the highest spatial resolution and spectral visibility. Furthermore, this strategy can be broadly adopted into other types of microchips, thus having broader impact beyond the in situ/operando fields.

Probing Structural Transformations and Degradation Mechanisms by Direct Observation in SIFSIX-3-Ni for Direct Air Capture.

Despite global efforts to reduce carbon dioxide (CO2) emissions, continued industrialization threatens to exacerbate climate change. This work investigates methods to capture CO2, with a focus on the SIFSIX-3-Ni metal-organic framework (MOF) as a direct air capture (DAC) sorbent. SIFSIX-3-Ni exhibits promising CO2 adsorption properties but suffers from degradation processes under accelerated aging, which are akin to column regeneration conditions. Herein, we have grown the largest SIFSIX-3-Ni single crystals to date, facilitating single crystal X-ray diffraction analyses that enabled direct observation of the H2O and CO2 dynamics through adsorption and desorption. In addition, a novel space group (I4/mcm) for the SIFSIX-3-Ni is identified, which provided insights into structural transitions within the framework and elucidated water's role in degrading CO2 uptake performance as the material ages. In situ X-ray scattering methods revealed long-range and local structural transformations associated with CO2 adsorption in the framework pores as well as a temperature-dependent desorption mechanism. Pair distribution function analysis revealed a partial decomposition to form nonporous single-layer nanosheets of edge-sharing nickel oxide octahedra upon aging. The formation of these nanosheets is irreversible and reduces the amount of active material for the CO2 sorption. These findings provide crucial insights for the development of efficient and stable DAC sorbents, effectively reducing greenhouse gases, and suggest avenues for enhancing MOF stability under practical DAC conditions.

Ultrasmooth Epitaxial Pt Thin Films Grown by Pulsed Laser Deposition.

Platinum (Pt) thin films are useful in applications requiring high-conductivity electrodes with excellent thermal and chemical stability. Ultrasmooth and epitaxial Pt thin films with single-crystalline domains have the added benefit of providing ideal templates for the subsequent growth of heteroepitaxial structures. Here, we grow epitaxial Pt (111) electrodes (ca. 30 nm thick) on sapphire (α-Al2O3 (0001)) substrates with pulsed laser deposition. This versatile technique allows control of the growth process and fabrication of films with carefully tailored parameters. X-ray scattering, atomic-force microscopy, and electron microscopy provide structural characterization of the films. Various gaseous atmospheres and temperatures were explored to achieve epitaxial growth of films with low roughness. A two-step (500 °C/300 °C) growth process was developed, yielding films with improved epitaxy without compromising roughness. The resulting films possess ultrasmooth interfaces (<3 Å) and high electrical conductivity (6.9 × 106 S/m). Finally, Pt films were used as current collectors and templates to grow lithium manganese oxide (LiMn2O4 (111)) epitaxial thin films, a cathode material used in Li-ion batteries. Using a solid-state ionogel electrolyte, the films were highly stable when electrochemically cycled in the 3.5-4.3 V vs Li/Li+ range.

Discovering polyelemental nanostructures with redistributed plasmonic modes through combinatorial synthesis.

Coupling plasmonic and functional materials provides a promising way to generate multifunctional structures. However, finding plasmonic nanomaterials and elucidating the roles of various geometric and dielectric configurations are tedious. This work describes a combinatorial approach to rapidly exploring and identifying plasmonic heteronanomaterials. Symmetry-broken noble/non-noble metal particle heterojunctions (~100 nanometers) were synthesized on multiwindow silicon chips with silicon nitride membranes. The metal types and the interface locations were controlled to establish a nanoparticle library, where the particle morphology and scattering color can be rapidly screened. By correlating structural data with near- and far-field single-particle spectroscopy data, we found that certain low-energy plasmonic modes could be supported across the heterointerface, while others are localized. Furthermore, we found a series of triangular heteronanoplates stabilized by epitaxial Moiré superlattices, which show strong plasmonic responses despite largely comprising a lossy metal (~70 atomic %). These architectures can become the basis for multifunctional and cost-effective plasmonic devices.

Unlocking the mysterious polytypic features within vaterite CaCO3.

Calcium carbonate (CaCO3), the most abundant biogenic mineral on earth, plays a crucial role in various fields such as hydrosphere, biosphere, and climate regulation. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO3, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase has a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can contain three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition with a critical point of ~190 K. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer advanced perspectives on the biomineralization process of calcium carbonate.

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.

Expanding the Library of Ions for Moisture-Swing Carbon Capture.

Developing materials that can more efficiently and cheaply capture carbon dioxide from ambient atmospheric conditions is essential for improving negative emission technologies. This study builds on the promising moisture-swing modality for direct air capture of carbon dioxide by investigating the use of several new anions─orthosilicate, borate, pyrophosphate, tripolyphosphate, and dibasic phosphate─that when introduced into ion-exchange resins allow for the cyclable capture of CO2 under dry conditions and its release under wet conditions. These ions, as well as many others that failed to show moisture-swing performance, are tested and directly compared thermodynamically and kinetically to understand their differences. This includes the use of analytical approaches new to the carbon capture field, such as the correlation of adsorption isotherms to moisture-swing performance, the use of phase lag kinetics, the examination of the humidity-carbon capture hysteresis of the sorbents, and the precise quantification of ion loading using inductively coupled plasma-optical emission spectroscopy. Phosphate dibasic was found to have the largest mass-normalized CO2 moisture-swing capacity, whereas phosphate tribasic had the best performance when factoring in kinetics, and pyrophosphate had the highest swing capacity when normalizing on a per-ion or per-unit-charge basis. This work not only sheds light on ways to improve DAC but also provides insights pertinent to the advancement of gas separation, negative emission technologies, and sorbent materials.

Design of Electrostatic Aberration Correctors for Scanning Transmission Electron Microscopy.

In a scanning transmission electron microscope (STEM), producing a high-resolution image generally requires an electron beam focused to the smallest point possible. However, the magnetic lenses used to focus the beam are unavoidably imperfect, introducing aberrations that limit resolution. Modern STEMs overcome this by using hardware aberration correctors comprised of many multipole elements, but these devices are complex, expensive, and can be difficult to tune. We demonstrate a design for an electrostatic phase plate that can act as an aberration corrector. The corrector is comprised of annular segments, each of which is an independent two-terminal device that can apply a constant or ramped phase shift to a portion of the electron beam. We show the improvement in image resolution using an electrostatic corrector. Engineering criteria impose that much of the beam within the probe-forming aperture be blocked by support bars, leading to large probe tails for the corrected probe that sample the specimen beyond the central lobe. We also show how this device can be used to create other STEM beam profiles such as vortex beams and probes with a high degree of phase diversity, which improve information transfer in ptychographic reconstructions.

Suitability of a diamine functionalized metal-organic framework for direct air capture.

The increase in the atmospheric carbon dioxide level is a significant threat to our planet, and therefore the selective removal of CO2 from the air is a global concern. Metal-organic frameworks (MOFs) are a class of porous materials that have shown exciting potential as adsorbents for CO2 capture due to their high surface area and tunable properties. Among several implemented technologies, direct air capture (DAC) using MOFs is a promising strategy for achieving climate targets as it has the potential to actively reduce the atmospheric CO2 concentration to a safer levels. In this study, we investigate the stability and regeneration conditions of N,N'-dimethylethylenediamine (mmen) appended Mg2(dobpdc), a MOF with exceptional CO2 adsorption capacity from atmospheric air. We employed a series of systematic experiments including thermogravimetric analysis (TGA) coupled with Fourier transformed infrared (FTIR) and gas chromatography mass spectrometer (GCMS) (known as TGA-FTIR-GCMS), regeneration cycles at different conditions, control and accelerated aging experiments. We also quantified CO2 and H2O adsorption under humid CO2 using a combination of data from TGA-GCMS and coulometric Karl-Fischer titration techniques. The quantification of CO2 and H2O adsorption under humid conditions provides vital information for the design of real-world DAC systems. Our results demonstrate the stability and regeneration conditions of mmen appended Mg2(dobpdc). It is stable up to 50% relative humidity when the adsorption temperature varies from 25-40 °C and the best regeneration condition can be achieved at 120 °C under dynamic vacuum and at 150 °C under N2.

Defect Contrast with 4D-STEM: Understanding Crystalline Order with Virtual Detectors and Beam Modification.

Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure-property relationships. 4D-STEM, a technique where diffraction patterns are acquired at a grid of points on the sample, provides a versatile method for highlighting defects. Computational analysis of the diffraction patterns with virtual detectors produces images that can map material properties. Here, using multislice simulations, we explore different virtual detectors that can be applied to the diffraction patterns that go beyond the binary response functions that are possible using ordinary STEM detectors. Using graphene and lead titanate as model systems, we investigate the application of virtual detectors to study local order and in particular defects. We find that using a small convergence angle with a rotationally varying detector most efficiently highlights defect signals. With experimental graphene data, we demonstrate the effectiveness of these detectors in characterizing atomic features, including vacancies, as suggested in simulations. Phase and amplitude modification of the electron beam provides another process handle to change image contrast in a 4D-STEM experiment. We demonstrate how tailored electron beams can enhance signals from short-range order and how a vortex beam can be used to characterize local symmetry.