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Although BiAgOSe, an analogue of a well-studied thermoelectric material BiCuOSe, is thermodynamically stable, its synthesis is complicated by the low driving force of formation from the stable binary and ternary intermediates. Here we have developed a "subtraction strategy" to suppress byproducts and produce pure phase BiAgOSe using hydrothermal methods. Electronic structure calculations and optical characterization show that BiAgOSe is an indirect bandgap semiconductor with a bandgap of 0.95 eV. The prepared sample exhibits lower lattice thermal conductivities (0.61 W·m

Heteroanionic materials are a burgeoning class of compounds that offer new properties via the targeted selection of anions. However, understanding the design principles and achieving successful syntheses of new materials in this class are in their infancy. To obtain mechanistic insight and a panoramic view of the reaction progression from beginning to end of the formation of a heteroanionic material, we selected BiOCuSe, a well-known thermoelectric compound, and utilized in situ synchrotron powder diffraction as a function of temperature and time. BiOCuSe is a layered material, which crystallizes in a common mixed anion structure type: ZrSiAsFe. Two reactions of starting materials (Bi2O2Se + Cu2Se and Bi2O3 + Bi + 3Cu + 3Se) were studied to determine the effect of precursors on the reaction pathway. Our in situ investigation shows that the ternary-binary Bi2O2Se + Cu2Se reaction proceeds without intermediates to directly form BiOCuSe, while the binary-elemental Bi2O3 + Bi + 3Cu + 3Se reaction generates many intermediates before the final product forms. These intermediates include CuSe, Bi3Se4, Bi2Se3, and Cu2Se. While the stoichiometric loading of the precursors necessarily dictates the identity of the first intermediates, kinetics also plays a contributing role in stabilizing unexpected intermediates such as CuSe and Bi3Se4. Understanding and establishing a link between the selection of precursors and the reaction pathways improves the potential for rational synthesis of heteroanionic materials and solid-state reactions in general.

Fluoridation of HfO2 was carried out with three commonly used solid-state fluoridation agents: PVDF, PTFE, and NH4HF2. Clear and reproducible differences are observed in the reaction products of the fluoropolymer reagents and NH4HF2 with the latter more readily reacting in air. Strong evidence of distinct, previously unreported hafnium oxyfluoride phases is produced by both reactions, and efforts to isolate them were successful for the air-NH4HF2 reaction. Synchrotron XRD, 19F NMR, and elemental analysis were employed to characterize the phase-pure material which appears to be analogous to known Zr-O-F phases with anion-deficient α-UO3 structures such as Zr7O9F10. Comparison with the hydrolysis of β-HfF4 under identical conditions depicts that the NH4HF2 route produces the oxyfluoride with greater selectivity and at lower temperatures. Thermodynamic calculations were employed to explain this result. Potential reaction pathways for the NH4HF2 fluoridation of HfO2 are discussed.

Solid-state synthesis has historically focused on reactants and end products; however, knowledge of reaction pathways, intermediate phases and their formation may provide mechanistic insight of solid-state reactions. With an increased understanding of reaction progressions, design principles can be deduced, affording more predictive power in materials synthesis. In pursuit of this goal, in situ powder X-ray diffraction is employed to observe crystalline phase evolution over the course of the reaction, thereby constructing a “panoramic” view of the reaction from beginning to end. We conducted in situ diffraction studies in the K–Bi–Q (Q = S, Se) system to understand the formation of phases occurring in this system in the course of their reactions. Powder mixtures of K2Q to Bi2Q3 in 1 : 1 and 1.5 : 1 ratios were heated to 800 °C or 650 °C, while simultaneously collecting diffraction data. Three new phases, K3BiS3, β-KBiS2, and β-KBiSe2, were discovered. Panoramic synthesis showed that K3BiQ3 serves an important mechanistic role as a structural intermediate in both chalcogen systems (Q = S, Se) in the path to form the KBiQ2 structure. Thermal analysis and calculations at the density functional theory (DFT) level show that the cation-ordered β-KBiQ2 polymorphs are the thermodynamically stable phase in this compositional space, while Pair Distribution Function (PDF) analysis shows that all α-KBiQ2 structures have local disorder due to stereochemically active lone pair expression of the bismuth atoms. The formation of the β-KBiQ2 structures, both of which crystallize in the α-NaFeO2 structure type, show a boundary where the structure can be disordered or ordered with regards to the alkali metal and bismuth. A cation radius tolerance for six-coordinate cation site sharing of ∼ 1.3 is proposed. The mechanistic insight the panoramic synthesis technique provides in the K–Bi–Q system is progress towards the overarching goal of synthesis-by-design.

Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

Semiconductor nanowires (NWs) capped with metal nanoparticles (NPs) show multifunctional and synergistic properties, which are important for applications in the fields of catalysis, photonics, and electronics. Conventional colloidal syntheses of this class of hybrid structures require complex sequential seeded growth, where each section requires its own set of growth conditions, and methods for preparing such wires are not universal. Here, we report a new and general method for synthesizing metal-semiconductor nanohybrids based on particle catalysts, prepared by scanning probe block copolymer lithography, and chemical vapor deposition. In this process, metallic heterodimer NPs were used as catalysts for NW growth to form semiconductor NWs capped with metallic particles (Au, Ag, Co, Ni). Interestingly, the growth processes for NWs on NPs are regioselective and controlled by the chemical composition of the metallic heterodimer used. Using a systematic experimental approach, paired with density functional theory calculations, we were able to postulate three different growth modes, one without precedent.

Heteroanionic compounds that contain more than one type of anion have many unique and attractive properties, which make them desirable for numerous applications. However, due to challenges in synth...

We use scanning probe block copolymer lithography in a two-step sequential manner to explore the deposition of secondary metals on nanoparticle seeds. When single element nanoparticles (Au, Ag, Cu, Co, or Ni) were used as seeds, both heterogeneous and homogeneous growth occurred, as rationalized using the thermodynamic concepts of bond strength and lattice mismatch. Specifically, heterogeneous growth occurs when the heterobond strength between the seed and growth atoms is stronger than the homobond strength between the growth atoms. Moreover, the resulting nanoparticle structure depends on the degree of lattice mismatch between the seed and growth metals. Specifically, a large lattice mismatch (e.g., 13.82% for Au and Ni) typically resulted in heterodimers, whereas a small lattice mismatch (e.g., 0.19% for Au and Ag) resulted in core-shell structures. Interestingly, when heterodimer nanoparticles were used as seeds, the secondary metals deposited asymmetrically on one side of the seed. By programming the deposition conditions of Ag and Cu on AuNi heterodimer seeds, two distinct nanostructures were synthesized with (1) Ag and Cu on the Au domain and (2) Ag on the Au domain and Cu on the Ni domain, illustrating how this technique can be used to predictively synthesize structurally complex, multimetallic nanostructures.

We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM′Q3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κl), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κl using Peierls–Boltzmann transport equation, which reveals ultralow κl (−1K−1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds.

In the context of metal particle catalysts, composition, shape, exposed facets, crystal structure, and atom distribution dictate activity. While techniques have been developed to control each of these parameters, there is no general method that allows one to optimize all parameters in the context of polyelemental systems. Herein, by combining a solid-state, Bi-influenced, high-index facet shape regulation strategy with thermal annealing, we achieve control over crystal structure and atom distribution on the exposed high-index facets, resulting in an unprecedentedly diverse library of chemically disordered and ordered multimetallic (Pt, Co, Ni, Cu, Fe, and Mn) tetrahexahedral (THH) nanoparticles. Density functional theory calculations show that surface Bi modification stabilizes the {210} high-index facets of the nanoparticles, regardless of their internal atomic ordering. Moreover, we find that the ordering transition temperatures for the nanoparticles are dependent on their composition, and, in the case of Pt(3)Fe(1) THH nanoparticles, increasing Ni substitution leads to an order-to-disorder transition at 900 °C. Finally, we have discovered that ordered intermetallic THH Pt(1)Co(1) nanocatalysts exhibit a catalytic performance superior to disordered THH Pt(1)Co(1) nanoparticles and commercial Pt/C catalysts toward methanol electrooxidation, highlighting the importance of crystal structure and atom distribution control on high-index facets in nanoscale catalysts.

Solid-state synthesis has historically focused on reactants and end products; however, knowledge of reaction pathways, intermediate phases and their formation may provide mechanistic insight of solid-state reactions. With an increased understanding of reaction progressions, design principles can be deduced, affording more predictive power in materials synthesis. In pursuit of this goal, in situ powder X-ray diffraction is employed to observe crystalline phase evolution over the course of the reaction, thereby constructing a “panoramic” view of the reaction from beginning to end. We conducted in situ diffraction studies in the K–Bi–Q (Q = S, Se) system to understand the formation of phases occurring in this system in the course of their reactions. Powder mixtures of K2Q to Bi2Q3 in 1 : 1 and 1.5 : 1 ratios were heated to 800 °C or 650 °C, while simultaneously collecting diffraction data. Three new phases, K3BiS3, β-KBiS2, and β-KBiSe2, were discovered. Panoramic synthesis showed that K3BiQ3 serves an important mechanistic role as a structural intermediate in both chalcogen systems (Q = S, Se) in the path to form the KBiQ2 structure. Thermal analysis and calculations at the density functional theory (DFT) level show that the cation-ordered β-KBiQ2 polymorphs are the thermodynamically stable phase in this compositional space, while Pair Distribution Function (PDF) analysis shows that all α-KBiQ2 structures have local disorder due to stereochemically active lone pair expression of the bismuth atoms. The formation of the β-KBiQ2 structures, both of which crystallize in the α-NaFeO2 structure type, show a boundary where the structure can be disordered or ordered with regards to the alkali metal and bismuth. A cation radius tolerance for six-coordinate cation site sharing of ∼ 1.3 is proposed. The mechanistic insight the panoramic synthesis technique provides in the K–Bi–Q system is progress towards the overarching goal of synthesis-by-design., This work uses in situ powder X-ray diffraction studies to observe crystalline phase evolution over the course of multiple K-Bi-Q (Q = S, Se) reactions, thereby constructing a “panoramic” view of each reaction from beginning to end.

Fluoridation of HfO

Density functional theory (DFT) has been widely applied in modern materials discovery and many materials databases, including the open quantum materials database (OQMD), contain large collections of calculated DFT properties of experimentally known crystal structures and hypothetical predicted compounds. Since the beginning of the OQMD in late 2010, over one million compounds have now been calculated and stored in the database, which is constantly used by worldwide researchers in advancing materials studies. The growth of the OQMD depends on project-based high-throughput DFT calculations, including structure-based projects, property-based projects, and most recently, machine-learning-based projects. Another major goal of the OQMD is to ensure the openness of its materials data to the public and the OQMD developers are constantly working with other materials databases to reach a universal querying protocol in support of the FAIR data principles.

As a promising thermoelectric material, BiCuSeO is of great interest for energy conversion. A higher figure of merit in n-type BiCuSeO than that in the p-type was predicted from theory, suggesting a need of in-depth investigations on the doping effects. In this work, the influences of group IV elements (Si, Ge, Sn, and Pb) on the electronic structures of BiCuSeO are studied from first principles. Despite the similar electronegativities of the group IV elements, Si is found to be an n-type dopant, being distinctly different from Ge, Sn, and Pb, which exhibit typical p-type behaviors. Detailed analysis on the doping effects is performed based on a recently developed band unfolding technique. Furthermore, Si-doped BiCuSeO is shown to have a higher power factor than p-type BiCuSeO from the Boltzmann transport theory.

International audience; Variations of the lattice parameter can significantly change the properties of a material, and, in particular, its electronic behaviour. In the case of graphene, however, variations of the lattice constant with respect to graphite have been limited to less than 2.5% due to its well-established high in-plane stiffness. Here, through systematic electronic and lattice structure studies, we report regions where the lattice constant of graphene monolayers grown on copper by chemical vapour deposition increases up to ~7.5% of its relaxed value. Density functional theory calculations confirm that this expanded phase is energetically metastable and driven by the enhanced interaction between the substrate and the graphene adlayer. We also prove that this phase possesses distinctive chemical and electronic properties. The inherent phase complexity of graphene grown on copper foils revealed in this study may inspire the investigation of possible metastable phases in other seemingly simple heterostructure systems.

High-symmetry thermoelectric materials usually have the advantage of very high band degeneracy, while low-symmetry thermoelectrics have the advantage of very low lattice thermal conductivity. If the symmetry breaking of band degeneracy is small, both effects may be realized simultaneously. Here we demonstrate this principle in rhombohedral GeTe alloys, having a slightly reduced symmetry from its cubic structure, to realize a record figure of merit (zT ∼ 2.4) at 600 K. This is enabled by the control of rhombohedral distortion in crystal structure for engineering the split low-symmetry bands to be converged and the resultant compositional complexity for simultaneously reducing the lattice thermal conductivity. Device ZT as high as 1.3 in the rhombohedral phase and 1.5 over the entire working temperature range of GeTe alloys make this material the most efficient thermoelectric to date. This work paves the way for exploring low-symmetry materials as efficient thermoelectrics. Thermoelectric materials enable a heat flow to be directly converted to a flow of charge carriers for generating electricity. The crystal structure symmetry is one of the most fundamental parameters determining the properties of a crystalline material including thermoelectrics. The common belief currently held is that high-symmetry materials are usually good for thermoelectrics, leading to great efforts having historically been focused on GeTe alloys in a high-symmetry cubic structure. Here we show a slight reduction of crystal structure symmetry of GeTe alloys from cubic to rhombohedral, enabling a rearrangement in electronic bands for more transporting channels of charge carriers and many imperfections for more blocking centers of heat-energy carriers (phonons). This leads to the discovery of rhombohedral GeTe alloys as the most efficient thermoelectric materials to date, opening new possibilities for low-symmetry thermoelectric materials. Cubic GeTe thermoelectrics have been historically focused on, while this work utilizes a slight symmetry-breaking strategy to converge the split valence bands, to reduce the lattice thermal conductivity and therefore realize a record thermoelectric performance, all enabled in GeTe in a rhombohedral structure. This not only promotes GeTe alloys as excellent materials for thermoelectric power generation below 800 K, but also expands low-symmetry materials as efficient thermoelectrics.

The authors unanimously wish to retract this Article due to their concerns about the interpretation of the low-energy electron microscopy (LEEM) and diffraction (LEED) patterns reported in the manuscript. In this study, the authors used spatial and angle-resolved photoemission spectroscopy (ARPES) to characterize graphene monolayers grown on copper foils, and observed regions of graphene adlayers with enhanced graphene/Cu interaction, higher Dirac cone doping level, moire mini Dirac cones and large lattice expansion. All these properties have been clearly verified and reproduced by photoemission spectroscopy as well as explained by density functional theory. LEEM and LEED characterization were also carried out to confirm the existence of a moire superlattice and lattice expansion, and the results were included in the main manuscript and Supplementary Information. On further analysis of the LEEM/LEED data, it seems that while the existence of a moire superlattice can be corroborated, the conclusion of graphene lattice expansion (7%) based on spatially resolved ARPES determinations cannot be confirmed by the LEEM/LEED measurements. The authors realized that these measurements were collected from statistically non-representative areas of the sample. Moreover, the fact that the raw microLEED images bear an asymmetry factor of as much as 5% due to the instrumental aberration makes it impossible to estimate any compression or expansion of the same order. Consequently, their conclusion on the graphene lattice expansion can only be supported by the photoemission data. In view that more complete and reliable structural determinations should be conducted, all authors wish to retract this Article.