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YO crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Y is bonded to five equivalent O atoms to form a mixture of corner and edge-sharing YO5 trigonal bipyramids. There are three shorter (2.32 Å) and two longer (2.42 Å) Y–O bond lengths. O is bonded to five equivalent Y atoms to form a mixture of corner and edge-sharing OY5 trigonal bipyramids.
Ak mwens pase 2% nan popilasyon riral la ki gen akse a elektrisite ak preske mwatye popilasyon an ap fe fas ak grangou egi, Ayiti fe fas a defi entekonekte nan povrete eneji ak ensekirite alimante. Yon solisyon pou ede abode povrete eneji ann Ayiti se devlopman mini-griy sole distribiye, sitou sole. Sepandan, souvan te ki pi byen adapte pou deplwaye jenerasyon sole se tou pi byen adapte pou agrikilti pa ti femye yo, kidonk kreye yon tansyon potansyelman konplike ant akse eneji ak sekirite manje. Pou adrese tansyon sa a, devlope sole yo, espesyalis agrikol yo ak cheche yo ap egzamine ansanm yon nouvo solisyon ki rele "agrivoltayik". Agrivoltaics se yon solisyon pataje te-itilize ki rapidman elaji nan mache sole etabli tankou Etazini, Ewop, ak Azi ki pe sole ak agrikilti, pwodwi elektrisite ak bay espas pou rekot ak bet patiraj anba ak ant panno. Nan kad Patenarya Akse Eneji pou Ayiti a ak Ajans Ameriken pou Devlopman Entenasyonal (USAID), Laboratwa Nasyonal Eneji Renouvlab (NREL) te fe yon premye analiz posibilite ak pwoje angajman pati konsene yo pou evalye potansyel agrivoltayik nan konteks mini-gri an Ayiti. Analiz la te konsidere mini-grid tipik 100-kW ak pi gwo 1-MW nan vil atrave Ayiti epi li te devlope de egzanp arketip agrivoltaik ki baze sou kontribisyon lokal kle yo, ki gen ladan irradians sole, done pwodiksyon ki soti nan resansman agrikol la, pri sou mache a, entevyou ak moun ki gen entere yo, ak ki egziste deja, rechech agrivoltayik. See NREL/TP-7A40-88444 for the English translation of this document.
The local crystal structure of Y 3 NbO 7 was probed using neutron total scattering. Five different structural models were fit to the experimental pair distribution function of this material up to 15 Å. Fits of defect fluorite (Fm$\overline{3}$m), pyrochlore (Fd$\overline{3}$m), and orthorhombic C weberite models (C222 1 ) were attempted based on prior structural investigations of Y 3 NbO 7 and related fluorite-based materials. Orthorhombic P weberite (P222 1 ) and monoclinic weberite models (P112 1 ) were derived via symmetry reduction of the orthorhombic C weberite structure. All models except monoclinic weberite failed to reproduce the experimental pair distribution function. Misfits were clearly visible for peaks arising from atom pairs within the first coordination shell (defect fluorite and pyrochlore) and beyond (orthorhombic C and P weberite). By contrast, monoclinic weberite (γ ≈ 91.8°) provided an excellent fit, with no major misfits observed. This unit cell features distorted YO 8 dodecahedra, YO 7 monocapped octahedra, and NbO 6 octahedra as building blocks. Comparison between the topologies of the cationic and anionic substructures in monoclinic weberite and its parent orthorhombic C structure revealed displacements of the metal atoms along the [100] direction and a less compact spatial distribution of oxygen atoms along the a axis. In conclusion, the monoclinic weberite model presented herein may serve as a starting point to revisit structural modeling of fluorite-based materials with challenging local structures.
Abstract Y 3 Al 5 O 12 (YAG) is a widely used phosphor host. Its optical properties are controlled by chemical substitution at its YO 8 or AlO 6 /AlO 4 sublattices, with emission wavelengths defined by rare‐earth and transition‐metal dopants that have been explored extensively. Nonstoichiometric compositions Y 3+x Al 5‐x O 12 ( x ≠ 0) may offer a route to new emission wavelengths by distributing dopants over two or more sublattices simultaneously, producing new local coordination environments for the activator ions. However, YAG typically behaves as a line phase, and such compositions are therefore challenging to synthesize. Here, a series of highly nonstoichiometric Y 3+x Al 5‐x O 12 with 0 ≤ x ≤ 0.40 is reported, corresponding to ≤20% of the AlO 6 sublattice substituted by Y 3+ , synthesized by advanced melt‐quenching techniques. This impacts the up‐conversion luminescence of Yb 3+ /Er 3+ ‐doped systems, whose yellow‐green emission differs from the red‐orange emission of their stoichiometric counterparts. In contrast, the YAG:Ce 3+ system has a different structural response to nonstoichiometry and its down‐conversion emission is only weakly affected. Analogous highly nonstoichiometric systems should be obtainable for a range of garnet materials, demonstrated here by the synthesis of Gd 3.2 Al 4.8 O 12 and Gd 3.2 Ga 4.8 O 12 . This opens pathways to property tuning by control of host stoichiometry, and the prospect of improved performance or new applications for garnet‐type materials.
In this paper, the synthesis, structure, and physical properties of Y 5 Ru 2 O 12 , a new member of the Ln 5 B 2 O 12 class (Ln = lanthanide, B = Ru, Re, Mo) are reported. Single crystals of Y 5 Ru 2 O 12 are synthesized using a high-temperature, high-pressure (700 °C, 200 MPa) hydrothermal method. The structure features edge-sharing Ru—O—Ru 1D chains of distorted RuO6 octahedra. The YO n (n = 6 and 7) polyhedral groups bridge Ru—O—Ru chains across the unit cell by forming a complex 3D structure. The Ru—O—Ru chains contain alternating shorter and longer Ru—Ru distances along the chain. Single crystal neutron diffraction shows streak-like diffuse scattering along the H-direction, due to stacking faults. Temperature-dependent anisotropic magnetic measurements reveal a broad feature over a wide temperature range, with a maximum at ≈70 K indicating potential short-range ordering, with canted antiferromagnetic behavior below 30 K. The dominant magnetic interactions are antiferromagnetic with a large negative Weiss temperature θ ≈ –200 K. The compound has an effective magnetic moment that is anomalously large for this class of compounds (≈4 μ B Ru –1 ), and a phenomenological spin Hamiltonian describing the unusual anisotropic magnetization of Y 5 Ru 2 O 12 is proposed.
This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo
Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%–20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.
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Electrochemical impedance spectroscopy (EIS) is a key tool across various scientific disciplines, including energy sciences, chemistry, and biology, enabling the analysis of electrochemical systems. However, conventional methods for interpreting EIS data are often complex and model dependent. The distribution of relaxation times (DRT) offers a non-parametric approach that simplifies the interpretation process by providing a timescale interpretation of EIS data. This article provides a comprehensive review of current methods for DRT inversion. Additionally, a survey of practitioners highlights key challenges in the field. Here, the findings underscore the need for standardized DRT analysis and benchmarks, as well as the development of automated analysis tools. These advancements would improve the usability and interpretability of EIS data. Ultimately, implementing these improvements could not only propel the field forward but also expand the application of DRT in scientific research by making it accessible to a broader range of researchers, including those without specialized expertise in programming or statistics.
The Intermediate Silicon Tracker (INTT), a two-layer barrel silicon strip tracker, is a key component of the tracking system for sPHENIX at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. The INTT is designed to enable the association of reconstructed tracks with individual RHIC bunch crossings. To evaluate the performance of preproduction INTT ladders and the readout chain, a beam test was conducted at the Research Center for Accelerator and Radioisotope Science, Tohoku University, Japan. This paper presents the performance of the INTT evaluated through studies of the signal-to-noise ratio, residual distribution, spatial resolution, hit-detection efficiency, and multiple track reconstruction.
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
Here we coupled compound-specific isotopic analyses of nitrogen (N) in amino acids (δ 15 N Glu , δ 15 N Phe ) and mercury stable isotopes (δ 202 Hg, Δ 199 Hg) to quantify ecological traits governing the concentration, variability, and source of Hg in largemouth bass (LB) and pike gudgeon (PG) across four rivers, South Korea. PG displayed uniform Hg concentration (56–137 ng/g), trophic position (TP corrected ; 2.6–3.0, n = 9), and N isotopes in the source amino acid (δ 15 N Phe ; 7–13‰), consistent with their specialist feeding on benthic insects. LB showed wide ranges in Hg concentration (45–693 ng/g), TP corrected (2.8–3.8, n = 14), and δ 15 N Phe (1.3–16‰), reflecting their opportunistic feeding behavior. Hg sources assessed using Hg isotopes reveal low and uniform Δ 199 Hg in PG (0.20–0.49‰), similar to Δ 199 Hg reported in sediments. LB displayed site-specific δ 202 Hg (-0.61 to -0.04‰) and Δ 199 Hg (0.53–1.09‰). At the Yeongsan River, LB displayed elevated Δ 199 Hg and low δ 15 N Phe , consistent with Hg and N sourced from the atmosphere. LB at the Geum River displayed low Δ 199 Hg and high δ 15 N Phe , both similar to the isotope values of anthropogenic sources. Our results suggest that a specialist fish (PG) with consistent ecological traits and Hg concentration is an effective bioindicator species for Hg. When accounting for Hg sources, however, LB better captures site-specific Hg sources.
Electrochemical water splitting, a promising method for green hydrogen production, is currently hindered by the high cost of precious-group metal (PGM)-based catalysts for the oxygen evolution reaction (OER). This study addresses this challenge by advancing the development of catalysts for OER, focusing on the development of high-performance PGM-free catalysts using nickel-iron-cobalt (NiFeCo)-based aerogels. The catalysts were synthesized via a sol-gel method and critical point drying to achieve a highly porous structure with an exceptionally high surface area. The designed catalyst structure provides an ideal platform for maximizing catalytic active sites and enhancing mass transport kinetics. Co has been systematically incorporated into the current PGM-free state-of-the-art, NiFeOx catalyst, and the metal ratios have been optimized. In addition to the experimental studies, density functional theory calculations were performed to study the material’s properties of this ternary catalyst and the effect of Co addition on enhancing OER catalysis.
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Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
It has become common knowledge that phonons can generate thermal Hall effect in a wide variety of materials, although the underlying mechanism is still controversial. We study longitudinal κ xx and transverse κ xy thermal conductivity in Pr 2 Ir 2 O 7 , which is a metallic analog of spin ice. Despite the presence of mobile charge carriers, we find that both κ xx and κ xy are dominated by phonons. A T/H scaling of κ xx unambiguously reveals that longitudinal heat current is substantially impeded by resonant scattering of phonons on paramagnetic spins. Upon cooling, the resonant scattering is strongly affected by a development of spin ice correlation and κ xx deviates from the scaling in an anisotropic way with respect to field directions. Strikingly, a set of the κ xx and κ xy data clearly shows that κ xy correlates with κ xx in its response to magnetic field including a success of the T/H scaling and its failure at low temperature. This remarkable correlation provides solid evidence that an indispensable role is played by spin-phonon scattering not only for hindering the longitudinal heat conduction, but also for generating the transverse response.
Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.