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Effect of different crystallographic properties on the electrical conductivity of two polymorphs of a spin crossover complex
In this study, the structure and transport properties of two polymorphs, nanoparticles and nanorods, of the iron(II) triazole [Fe(Htrz) 2 (trz)](BF 4 ) spin crossover complex were compared. Conductive atomic force microscopy was used to map the electrical conductivity of individual nanoparticles and nanorods. The [Fe(Htrz) 2 (trz)](BF 4 ) nanorods showed significantly higher conductivity compared to nanoparticles. This difference in electrical conductivity is partially associated to the different Fe–N bond lengths in each of the polymorphs, with an inverse relationship between Fe–N bond length and conductivity. Transport measurements were done on the nanorods for both high spin (at 380 K) and low spin (at 320 K) states under dark and illuminated conditions. The conductance is highest for the low spin state under dark conditions. In illumination, the conductance change is much diminished.
Comparative Studies of Optoelectronic Properties, Structures, and Surface Morphologies for Phosphorus-Doped Poly-Si/SiOx Passivating Contacts
We investigated and compared optoelectronic properties, crystallographic structures, and nanoscale surface morphologies of ex-situ phosphorus-doped polycrystalline silicon (poly-Si)/SiO x passivating contacts, formed by different deposition methods (sputtering, plasma-enhanced chemical vapour deposition (PECVD), and low-pressure chemical vapour deposition (LPCVD)). Across all these deposition technologies, a similar trend is observed: higher diffusion temperatures yield films that are more crystalline but have rougher surface morphologies due to bigger surface crystal grains. Also, the recrystallization process of the as-deposited Si films starts from the SiO x interface, rather than from the film surface and bulk. However, there are some distinct differences among these technologies. Firstly, the LPCVD method yields the roughest surface and smallest degree of crystallinity on finished poly-Si films. In contrast, the PECVD method has the smoothest surface for both as-deposited Si and annealed poly-Si films. Secondly, as-deposited sputtered and PECVD Si films contain only an amorphous phase whereas as-deposited LPCVD films has already had some crystalline phase. Thirdly, the LPCVD phosphorus in-diffusion into the substrate depends strongly on the initial film thickness, whereas for the other two methods it is weakly dependent on thickness.
First-principles investigation of uranium mononitride (UN): Effect of magnetic ordering, spin-orbit interactions and exchange correlation functional
Uranium mononitride (UN) is a promising nuclear fuel that combines the advantageous properties of readily used UO 2 and uranium alloys. Various properties of UN have been previously studied using different density functional theory (DFT) methodologies; however, there are still inconsistencies when it comes to the dynamical stability and defect properties of UN. We address these inconsistencies by studying the UN phonons and defect properties using DFT calculations employing two generalized gradient approximation (GGA) exchange-correlation functionals: PBE and AM05, with and without an added on-site Coulomb repulsion term (+U). Furthermore, we investigate the importance of spin-orbit coupling (SOC) when calculating the properties of UN. We use the different methodologies to determine the preference of UN to have antiferromagnetic (AFM) ordering, as seen in experiments, or ferromagnetic (FM) ordering of the uranium spins. We compare the crystallographic properties, density of states, the DFT X-ray photoelectron spectra and phonon dispersions calculated using the different methodologies. We demonstrate that GGA + U reproduces the AFM ordering in UN, but the crystal structure is dynamically unstable. We also show that magnetic ordering is important in finding the lowest energy defective structure, and that SOC has a distinct influence on the energy of the different uranium interstitial defects. Lastly, we discuss the point defect formation energies under U-rich and N-rich conditions, and the stoichiometric formation energies calculated with the different methodologies, providing insight into the observed tendency for forming hypostoichiometric UN.
Finite temperature properties of uranium mononitride
Uranium mononitride (UN) is a promising nuclear fuel that combines the advantageous properties of readily used UO 2 and uranium alloys, such as high melting temperature and high uranium density, and thermal conductivity, respectively. A better understanding of UN behavior at operating temperatures can be obtained from finite temperature data, such as elastic properties. To get this information, ab initio molecular dynamics (AIMD) simulations were performed at five different temperatures using constant volume (NVT) and constant pressure (NPT) ensembles. Initially, the performance of PBE functional in reproducing experimental crystallographic properties and magnetic ordering is assessed. The finite temperature phonon dispersions are calculated using NVT simulation results, which show a softening of the phonon modes with increasing temperature. The NPT results are used to obtain the thermal expansion of UN and finite temperature electronic properties. The calculated thermal expansion is compared with our measurements using neutron diffraction. Additionally, the temperature dependent elastic properties of UN are evaluated using the strain-stress method in AIMD simulations, indicating that UN becomes softer and more compressible with increasing temperature. Also, the calculated Young’s modulus slope is in very good agreement with the experiment. The finite temperature heat capacity and electronic thermal conductivity are calculated from AIMD simulations, which are in better agreement with the experiment than the heat capacity and thermal conductivity calculated using the structures relaxed at 0K. Finally, the thermal diffusivity from AIMD has opposite temperature dependence compared to experimental results, which we argued comes from the underestimated electronic thermal conductivity.
Computational design of thermoelectric alloys through optimization of transport and dopability
Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.
Crystallization by Amorphous Particle Attachment: On the Evolution of Texture
Abstract Crystallization by particle attachment (CPA) is a gradual process where each step has its own thermodynamic and kinetic constrains defining a unique pathway of crystal growth. An important example is biomineralization of calcium carbonate through amorphous precursors that are morphed into shapes and textural patterns that cannot be envisioned by the classical monomer‐by‐monomer approach. Here, a mechanistic link between the collective kinetics of mineral deposition and the emergence of crystallographic texture is established. Using the prismatic ultrastructure in bivalve shells as a model, a fundamental leap is made in the ability to analytically describe the evolution of form and texture of biological mineralized tissues and to design the structure and crystallographic properties of synthetic materials formed by CPA.
Morphological and crystallographic controls in the replacement of calcite and aragonite by cerussite and otavite
Mineral replacement reactions are essential solid-fluid interactions in natural and industrial processes including metasomatism, diagenesis, and metamorphism, and sequestration of metal toxins from polluted water. In this paper, we explore the morphological evolution of aragonite and calcite (polymorphs of CaCO 3 ) during their replacement by cerussite (PbCO 3 , an isomorph of aragonite) and otavite (CdCO 3 , an isomorph of calcite) in acidic aqueous solutions (with initial pH 3 to 4). Observations using scanning electron and synchrotron-based transmission X-ray microscopies reveal the formation of pseudomorphic shells (~5 to 10 µm thick) of cerussite and otavite when Pb 2+ is reacted with either calcite or aragonite and when Cd 2+ is reacted with aragonite. The formation of pore space at the substrate-precipitate interface and within the precipitate is found to be key in promoting chemical exchange between the dissolving phase and the solution. In contrast, minimal reactivity is observed when Cd 2+ is reacted with calcite. These results demonstrate that the lack of substrate-precipitate epitaxy (either because of inconsistent symmetries or due to large lattice constant mismatch) is a critical factor that enables replacement of primary CaCO 3 minerals by Pb- or Cd-bearing secondary minerals while the presence of epitaxial relationships inhibit those reactions. When calcite and aragonite coexist, the replacement of aragonite by cerussite and otavite occurs preferentially over calcite. We postulate that the relative reactivity of calcite and aragonite to Pb 2+ and Cd 2+ can be determined by interplay between morphological and crystallographic properties of CaCO 3 materials as well as solution chemistry. Our results demonstrate that coupled dissolution-precipitation processes of carbonate minerals play an important role in controlling mobility and bioavailability of lead and cadmium in environments where acidic metal-contaminated water interacts with carbonate-rich rocks and soils.
Orientation-tunable local crystallization of Si films enabled by atomic imprint crystallization
Here, in this paper, we demonstrate area-selective crystallization of amorphous Si into tunable crystal orientations enabled by atomic imprint crystallization (AIC), where an amorphous Si layer is crystallized by solid phase epitaxy (SPE) from an externally impressed single-crystalline Si template. Using micro-patterned single-crystalline Si templates, a limited area of an amorphous Si film, where the film surface and patterned template surface are in contact, is crystallized via SPE to create an array of crystallographically aligned dots embedded in amorphous matrix. Combining AIC from the top surface and conventional SPE from the substrate, we demonstrate the fabrication of an array of crystalline dots embedded in single-crystalline matrix with tunable in-plane rotation angle. The results indicate the high tunability of the crystallization process enabled by AIC, allowing precise control of crystallographic properties of thin films with area-selectivity; such capability opens opportunities for the design of new materials for a wide range of applications in materials science.
Towards the extraction of the crystal cell parameters from pair distribution function profiles
The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k -nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%.
OPEN ALPHADIFFRACT
Open-source release of the AlphaDiffract data generation and training system. Includes only the public Materials Project dataset retrievers.AlphaDiffract is a deep learning framework that achieves state-of-the-art performance in predicting the crystal system, space group, and lattice parameters directly from PXRD patterns. AlphaDiffract utilizes a 1D adaptation of the ConvNeXt architecture, a modern convolutional neural network that integrates key design principles from transformers, coupledwith dedicated prediction heads for each crystallographic property.
Furthering Advancement to Address Tough Problems Facing the Solar Energy Industry, Spurring the Next Wave of Solar Technology Innovations through Three Progressive Challenges and Cash Incentives (Final Report)
The American-Made Solar Prize is a prize competition from the U.S. Department of Energy that is designed to revitalize U.S. solar manufacturing. The competition aims to support the growth of U.S. solar manufacturing and reenergize American energy innovation by tapping into American's competitive spirit and the nation's unparalleled innovation ecosystem leveraging national labs, incubators, facilities and more. National Renewable Energy Lab (NREL) will provide technical and business advisement to the noted Solar Prize finalists. The CRADA Participant is in the process of developing a new continuous process for making silicon wafers for solar cells in a novel approach to horizontal ribbon growth. Under this CRADA, NREL will characterize the chemical, electrical, and crystallographic properties of the as-produced wafers to and work with the CRADA Participant to improve and modify processing conditions.
Correlating Crystallographic Orientation and Ferroic Properties of Twin Domains in Metal Halide Perovskites
Metal halide perovskite (MHP) solar cells have attracted worldwide research interest. Although it has been well established that grain, grain boundary, and grain facet affect MHPs optoelectronic properties, less is known about subgrain structures. Recently, MHP twin stripes, a subgrain feature, have stimulated extensive discussion due to the potential for both beneficial and detrimental effects of ferroelectricity on optoelectronic properties. Furthermore, connecting the ferroic behavior of twin stripes in MHPs with crystal orientation will be a vital step to understand the ferroic nature and the effects of twin stripes. In this work, we studied the crystallographic orientation and ferroic properties of CH 3 NH 3 PbI 3 twin stripes, using electron backscatter diffraction (EBSD) and advanced piezoresponse force microscopy (PFM), respectively. Using EBSD, we discovered that the orientation relationship across the twin walls in CH 3 NH 3 PbI 3 is a 90° rotation about ($\bar{11}0$), with the (030) and (111) directions parallel to the direction normal to the surface. By careful inspection of CH 3 NH 3 PbI 3 PFM results including in-plane and out-of-plane PFM measurements, we demonstrate some nonferroelectric contributions to the PFM responses of this CH 3 NH 3 PbI 3 sample, suggesting that the PFM signal in this CH 3 NH 3 PbI 3 sample is affected by nonferroelectric and nonpiezoelectric forces. If there is piezoelectric response, it is below the detection sensitivity of our interferometric displacement sensor PFM (<0.615 pm/V). Overall, this work offers an integrated picture describing the crystallographic orientations and the origin of PFM signal of MHPs twin stripes, which is critical to understanding the ferroicity in MHPs.
Solid-State Syntheses, Crystallographic Spatial Disorders, Thermal Behaviors, and Bandgaps of Hybrid Organic-Inorganic Manganese Halides: A2Mn(Cl/Br)4 (A = NH4+, C(NH2)3+, & C3H4N2+)
By systematically optimizing solid-state synthesis via the concerted use of differential scanning calorimetry and in-situ variable-temperature X-ray diffraction, we report the discovery of four new hybrid organic-inorganic manganese halides A2Mn(Cl/Br)4 (A = NH4+, C(NH2)3+, C3H4N2+), with emphasis on how the organic fragment geometry, polarity, and electron-donating properties influence crystallographic spatial disorders, thermal behaviors, and optical band gaps. With a nonpolar tetrahedral cation, as in (NH4)2MnCl4 (P21/c, mP30) or (NH4)2MnBr4 (C2/c, mC180), the direct optical bandgaps (4.36–4.28 eV) are wider than those with an organic nonpolar planar geometry, as in (C(NH2)3)2MnBr4 (P21/c, mP300, 4.07 eV), or an organic polar planar geometry, as in (C3H4N2+)2MnBr4 (I41/a, tI384, 4.05 eV). Furthermore, the organic components affect the spatial arrangement and dimensionality of the resulting inorganic frameworks comprised of Mn(Cl/Br)6 octahedra, with varying distortions, or spatially disordered MnBr4 tetrahedra. These resulting trends, coupled with the systematic investigation into synthesis, and the motivation behind elucidating the nuanced role of the organic cation, altogether expand upon the phase-space of hybrid materials beyond perovskites and demonstrate the potential for a rational design of hybrid materials with tailored optoelectronic functionalities for advanced energy applications.
Evidence for In-Plane Electrical Polarization in 3R-β’-In 2 Se 3 Thin Films Grown by Molecular Beam Epitaxy
β-In 2 Se 3 has been identified as a potential ferroelectric. This work describes the growth of β-In 2 Se 3 via molecular beam epitaxy along with a description of its properties, including the search for switchable polarization in 3R-β’-In2Se3. The thin-film morphology, crystal structure, and phase maps of β-In 2 Se 3 on Si(111) and Al 2 O 3 (0001) were evaluated by atomic force microscopy, X-ray diffraction, and Raman spectroscopy as a function of the atomic Se/In flux ratio and growth temperature. Smooth β-In 2 Se 3 thin films were successfully realized on Si(111) at a substrate temperature of 150 °C using a Se/In flux ratio of 5.7, as well as on Al 2 O 3 (0001) at 450 °C using a Se/In flux ratio of 5.5. Scanning transmission electron microscopy (STEM) confirms the 3R polytype of β-In 2 Se 3 in films on both substrates, with a minor disorder associated with the 2H polytype at the interface. Indications of in-plane Se atom displacements characteristic of the 3R-β’-In 2 Se 3 polytype were found by STEM and second harmonic generation analysis in films on Al 2 O 3 (0001), but not in films on Si(111). Furthermore, attempts at electrical polarization switching did not produce compelling evidence for ferroelectricity. Instead, electrical transport measurements demonstrated locally varying anisotropic responses with the applied electric fields along different in-plane directions, with some hysteresis associated with the trapping of charges.
A practical guide for crystal growth of van der Waals layered materials
This Tutorial provides an overview of the techniques that are most commonly utilized to grow bulk van der Waals crystals. The materials discussed were selected to highlight various challenges that are often encountered during crystal growth. Additionally, in relatively equal parts, the text covers melt-based techniques, vapor transport growths, and the characterization of crystal quality with an emphasis on structural and chemical homogeneities. Furthermore, pertinent details are given regarding the growth and characterization of many specific compounds, with examples mostly drawn from our own research, and an effort is made to highlight cases where the growths offer a particular lesson or the conditions have a significant impact on the crystal’s physical properties. A primary goal is to motivate more researchers to grow crystals by providing general descriptions and considerations for different growth techniques and equipment while sharing some of our own lessons learned and best practices for the growth and characterization of layered van der Waals crystals. The Tutorial is not written solely for aspiring crystal growers, however, because any researcher who collaborates with a crystal grower can benefit from having a greater understanding and appreciation of the processes of crystal growth and materials development.
Can a deep-learning model make fast predictions of vacancy formation in diverse materials?
The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.