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A rare four-step spin crossover transition has been attained in a two-dimensional Hofmann-type material through the presence of an array of antagonistic host–host and host–guest interactions.
In this paper, we report a theoretical investigation of the electronic structure and transport properties of eleven Zintl compounds including nine 122 phases (AMg 2 X 2 , AZn 2 Sb 2 (A = Ca, Sr, Ba; X = Sb, Bi)) and two 212 phases (Ba 2 ZnX 2 (X = Sb, Bi)). The electronic structures and electrical transport properties are studied using ab initio calculations and semi-classical Boltzmann theory within the constant relaxation time approximation. All the compounds are semiconducting. We find that the n-type 122 phases with the CaAl 2 Si 2 structure type show better performance than p-type materials due to the multi-valley degeneracy with anisotropic carrier pockets at and near the conduction band minimum. The pocket anisotropy is beneficial in achieving high conductivity and Seebeck coefficient simultaneously. This mechanism yields substantial improvement in the power factor. Finally, the general performance of 212 phases is inferior to that of the 122 phases, with the Ba 2 ZnSb 2 compound showing better performance.
Because of its unique transport properties, CuInTe 2 has been considered as a promising p-type material for thermoelectric applications. However, its diamondoid structure gives it a high intrinsic lattice thermal conductivity that greatly limits its thermoelectric performance. In this study, we demonstrate that Ag alloying gives rise to an extremely low lattice thermal conductivity of 0.47 W m –1 K –1 for Cu 0.8 Ag 0.2 InTe 2 at 850 K. Moreover, we found Cu doping significantly improves the carrier mobility while simultaneously increasing the carrier concentraton. As a result, the power factor of Cu 0.8 Ag 0.2 InTe 2 increases and a maximum ZT of ~1.6 is achieved at 850 K. Both DFT calculations and low temperature heat capacity measurements suggest a strong interaction between low frequency optical phonons and heat carrying acoustic phonons, which is derived from the weak Ag–Te bonding. Here, this strong phonon coupling decreases the Debye temperature and induces a low sound velocity.
Sodium superionic conductor (NASICON)-type materials are widely explored as Li- and Na-ion cathodes and solid-state electrolytes but are largely ignored as anodes due to their lower capacities and higher intercalation voltages, which reduce the overall energy densities of Li- and Na-ion batteries (LIBs and SIBs). Herein, we unveil high capacity multi-redox empty NASICON-Nb 2 (PO 4 ) 3 as a potential anode material for LIBs and SIBs, which reversibly delivers 167 and 150 mA h g -1 at the average voltages of 1.86 V vs. Li + /Li 0 and 1.46 V vs. Na + /Na 0 , respectively. The Li and Na intercalation reactions proceed via multiple phase transitions, leading to short-range ordered Li 3 Nb 2 (PO 4 ) 3 and triclinic (P$\overline{1}$ with combining macron]) Na 3 Nb 2 (PO 4 ) 3 , as revealed by in situ X-ray diffraction studies. Our density functional theory calculations are also in agreement with the in situ measurements in predicting a stable Na 3 Nb 2 (PO 4 ) 3 composition in the Na–Nb 2 (PO 4 ) 3 pseudo-binary system. X-ray absorption spectroscopy confirms the participation of multi-redox Nb 5+ /Nb 4+ /Nb 3+ couples. The Nb 2 (PO 4 ) 3 anode delivers capacities greater than 124 and 106 mA h g -1 at 1C rate in Li and Na cells, respectively. In conclusion, pairing Nb 2 (PO 4 ) 3 with suitable cathodes and electrolytes can lead to high energy density batteries.
Microplastic particles are of increasing environmental concern due to the widespread uncontrolled degradation of various commercial products made of plastic and their associated waste disposal. Recently, common technology used to repair sewer pipes was reported as one of the emission sources of airborne microplastics in urban areas. This research presents results of the multi-modal comprehensive chemical characterization of the microplastic particles related to waste discharged in the pipe repair process and compares particle composition with the components of uncured resin and cured plastic composite used in the process. Analysis of these materials employs complementary use of surface-enhanced Raman spectroscopy, scanning transmission X-ray spectro-microscopy, single particle mass spectrometry, and direct analysis in real-time high-resolution mass spectrometry. Further, it is shown that the composition of the relatively large (100 μm) microplastic particles resembles components of plastic material used in the process. In contrast, the composition of the smaller (micrometer and sub-micrometer) particles is significantly different, suggesting their formation from unintended polymerization of water-soluble components occurring in drying droplets of the air-discharged waste. In addition, resin material type influences the composition of released microplastic particles. Results are further discussed to guide the detection and advanced characterization of airborne microplastics in future field and laboratory studies pertaining to sewer pipe repair technology.
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.
The magnetic system of a Dresden electron beam ion source (EBIS) generating the necessary magnetic field with a new type of permanent magnet made of high energy density NdFeB-type material operable at temperatures above 100 °C has been investigated and tested. The employment of such kind of magnets provides simplified operation without the time-consuming installation and de-installation procedures of the magnets for the necessary baking of the ion source after commissioning and maintenance work. Furthermore, with the use of a new magnetization technique the geometrical filling factor of the magnetic Dresden EBIS design could be increased to a filling factor of 100% leading to an axial magnetic field strength of approximately 0.5 T exceeding the old design by 20%. Simulations using the finite element method software Field Precision and their results compared with measurements are presented as well. It could be shown that several baking cycles at temperatures higher than 100 °C did not change the magnetic properties of the setup.
The thermoelectric properties of Heusler-type Fe{sub 2−x}V{sub 1+x}Al{sub 1−y}Si{sub y} and Fe{sub 2−x}V{sub 1+x−y}Ti{sub y}Al alloys have been investigated to clarify which off-stoichiometric alloy, i.e., V-rich (x > 0) or V-poor (x < 0), is more effective in enhancing the Seebeck coefficient when doped by Si and Ti, while retaining a low electrical resistivity. Large Seebeck coefficients of −182 μV/K and 110 μV/K at 300 K are obtained for n-type Fe{sub 1.95}V{sub 1.05}Al{sub 0.97}Si{sub 0.03} and p-type Fe{sub 2.04}V{sub 0.93}Ti{sub 0.03}Al, respectively. When the Seebeck coefficient is plotted as a function of valence electron concentration (VEC), the VEC dependence for the doped off-stoichiometric alloys falls on characteristic curves depending on the off-stoichiometric composition x. It is concluded that a larger Seebeck coefficient with a negative sign can be obtained for the V-rich alloys rather than the V-poor alloys, whilst good p-type materials are always derived from the V-poor alloys. Substantial enhancements in the Seebeck coefficient for the off-stoichiometric alloys could be achieved by a favorable modification in the electronic structure around the Fermi level through the antisite V or Fe defect formation.
Raman measurements of a Fano-type surface phonon mode associated with Dirac surface states (SS) in Bi{sub 2}Se{sub 3} topological insulator thin films allowed an unambiguous determination of the electron-phonon coupling strength in Dirac SS as a function of film thickness ranging from 2 to 40 nm. A non-monotonic enhancement of the electron-phonon coupling strength with maximum for the 8–10 nm thick films was observed. The non-monotonicity is suggested to originate from plasmon-phonon coupling which enhances electron-phonon coupling when free carrier density in Dirac SS increases with decreasing film thickness and becomes suppressed for thinnest films when anharmonic coupling between in-plane and out-of-plane phonon modes occurs. The observed about four-fold enhancement of electron-phonon coupling in Dirac SS of the 8–10 nm thick Bi{sub 2}Se{sub 3} films with respect to the bulk samples may provide new insights into the origin of superconductivity in this-type materials and their applications.
Orientation control of {100}-oriented epitaxial orthorhombic 0.07YO{sub 1.5}-0.93HfO{sub 2} films grown by pulsed laser deposition was investigated. To achieve in-plane lattice matching, indium tin oxide (ITO) and yttria-stabilized zirconia (YSZ) were selected as underlying layers. We obtained (100)- and (001)/(010)-oriented films on ITO and YSZ, respectively. Ferroelastic domain formation was confirmed for both films by X-ray diffraction using the superlattice diffraction that appeared only for the orthorhombic symmetry. The formation of ferroelastic domains is believed to be induced by the tetragonal–orthorhombic phase transition upon cooling the films after deposition. The present results demonstrate that the orientation of HfO{sub 2}-based ferroelectric films can be controlled in the same manner as that of ferroelectric films composed of conventional perovskite-type material such as Pb(Zr, Ti)O{sub 3} and BiFeO{sub 3}.
Residual stress is a long-standing issue in thin film growth. Better understanding and control of film stress would lead to enhanced performance and reduced failures. In this work, we review how thin film stress is measured and interpreted. The results are used to describe a comprehensive picture that is emerging of what controls stress evolution. Examples from multiple studies are discussed to illustrate how the stress depends on key parameters (e.g., growth rate, material type, temperature, grain size, morphology, etc.). The corresponding stress-generating mechanisms that have been proposed to explain the data are also described. To develop a fuller understanding, we consider the kinetic factors that determine how much each of these processes contributes to the overall stress under different conditions. This leads to a kinetic model that can predict the dependence of the stress on multiple parameters. The model results are compared with the experiments to show how this approach can explain many features of stress evolution.
Deviations of local structure and chemistry from the average crystalline unit cell are increasingly recognized to have a significant influence on the properties of many technologically important materials. Here, we present the vector pair correlation function (vPCF) as a new real-space crystallographic analysis method, which can be applied to atomic-resolution scanning transmission electron microscopy (STEM) images to quantify and analyze structural order/disorder correlations. Our STEM-based vPCFs have several advantages over radial PCFs and/or 3D pair distribution functions from x-ray total scattering: vPCFs explicitly retain crystallographic orientation information, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any material that can be imaged with STEM. To show the utility of our approach, we measure partial vPCFs in Ba 5 SmSn 3 Nb 7 O 30 (BSSN), a tetragonal tungsten bronze (TTB) structured complex oxide. Many TTBs are known to be classical or relaxor ferroelectrics, and these properties have been correlated with the presence of superlattice ordering. BSSN, specifically, exhibits relaxor behavior and an incommensurate structural modulation. From the vPCF data, we observe that, of the cation sites, only the Ba (A2) sublattice is structurally modulated. We then infer the local modulation vector and reveal a marked anisotropy in its correlation length. Finally, short-range correlated polar displacements on the B2 cation sites are observed. This work introduces the vPCF as a powerful real-space crystallography technique, which enables direct, robust quantification of short-to-long range order on a sublattice-specific basis and is applicable to a wide range of complex material types.
A series of six shock initiation experiments have been carried out on low density LX-14 powder in order to simulate a shock insult on the heavily damage polymer bonded explosive, LX-14. Two distinct morphologies were studied (tap density molding powder and machined swarf), both at the same density of 0.942 g/cm 3 , or 50.1% theoretical maximum density. The purpose of these experiments was to provide data to help make an assessment of the effects that damage has on the material sensitivity to a planar shock. This was achieved primarily by providing shock sensitivity data in the form of a Pop plot, and also reactants equation of state data, which aids in the determination of input conditions for both the experiments performed in this work and also future experiments of this material type. The experiments were of a cut-back format, consisting of four sample heights on each shot and diagnosed with optical velocimetry. The experiments were carried out at the Technical Area 40 Chamber 9 gas gun facility at the Los Alamos National Laboratory, where the LX-14 targets were subjected to Al 6061 and Oxygen Free High Conductivity copper impactors launched to velocities up to 2.14 km/s. Time corrected reactive growth wave profiles are presented in this paper along with the derivation of the following Hugoniot parameters for this explosive, where the molding powder and machine swarf are represented by the following linear equations, respectively, U s = 1.64 (±0.64)u p + 2.25 (±0.49) and U s = 0.87 (±.0.60)u p + 4.08 (±0.49). The results show that the steady increase in shock sensitivity with increasing void fraction reaches an inflection point beyond which the shock sensitivity begins to decease. This inflection point lies between 65% and 50% of the theoretical maximum density of the LX-14. In both cases, the damaged LX-14 was not as sensitive as expected, with a relative shock sensitivity of the molding powder being less than the pressed LX-14, and the machined swarf having a shock sensitivity that is comparable to pressed LX-14.
High Al mole fraction AlGaN is an ultrawide bandgap semiconductor with potential applications in power electronics and deep UV detectors. Although n-type material is achievable with Si-doping, the role of Si is controversial, particularly for AlxGa1−xN with x > 0.8. For this paper, AlGaN films were grown by plasma-assisted molecular beam epitaxy onto bulk AlN substrates and doped with 1018–1020 cm−3 Si. We examine electron transport in heavily Si-doped AlxGa1−xN with x ≥ 0.65 using magnetic resonance, which allows us to probe the neutral donors directly rather than the free carriers and avoids complications due to electrical contacts. Transport was studied through the temperature-dependent linewidth of the electron paramagnetic resonance (EPR) signature for the neutral donor. Analysis shows evidence of hopping conductivity in the most lightly doped samples and impurity band formation in the most heavily doped ones. The EPR results, which are consistent with Hall measurements performed on the same samples, are promising for the development of highly conducting high Al content AlGaN.
Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.
Here, the aim of this study is to present several approaches that have been used to model the behavior of radioactive materials (specifically Pu) in contaminated wounds. We also review some attempts by the health physics community to validate and revise the National Council on Radiation Protection and Measurements (NCRP) 156 biokinetic model for wounds, and present some general recommendations based on the review. Modeling of intake via the wound pathway is complicated because of a large array of wound characteristics (e.g. solubility and chemistry of the material, type and depth of the tissue injury, anatomical location of injury). Moreover, because a majority of the documented wound cases in humans are medically treated (excised or treated with chelation), the data to develop biokinetic models for unperturbed wound exposures are limited. Since the NCRP wound model was largely developed from animal data, it is important to continue to validate and improve the model using human data whenever plausible.
Non-structural carbohydrates (NSCs), the stored products of photosynthesis, building blocks for growth, and fuel for respiration, are central to plant metabolism, but their measurement is challenging. Differences in methods and procedures among laboratories can cause results to vary widely, limiting our ability to integrate and generalize patterns in plant C balance among studies. A recent assessment found that NSC concentrations measured for a common set of samples can vary by an order of magnitude, but sources for this variability were unclear. We measured a common set of nine plant material types, and two synthetic samples with known NSC concentrations, using a common protocol for sugar extraction and starch digestion, and three different sugar quantification methods (ion chromatography, enzyme, acid) in six laboratories. We also tested how sample handling, extraction solvent, and centralizing parts of the procedure in one laboratory affected results. NSC concentrations measured for synthetic samples were within about 11.5% of known values for all three methods. However, differences among quantification methods were the largest source of variation in NSC measurements for natural plant samples because the three methods quantify different NSCs. The enzyme method quantified only glucose, fructose, and sucrose, with ion chromatography we additionally quantified galactose, while the acid method quantified a large range of mono- and oligosaccharides. For some natural samples, acid method quantification doubled sugar relative to the other methods, demonstrating that trees allocate C to a range of sugar molecules. Sample handling had little effect on measurements, while ethanol sugar extraction improved accuracy over water extraction. Our results demonstrate that reasonable accuracy of NSC measurements can be achieved when different methods are used, as long as protocols are robust and standardized. Thus, we provide detailed protocols for the extraction, digestion and quantification of NSCs in plant samples, which should improve the comparability of NSC measurements among laboratories.