Universal formula for the density of states with continuous symmetry
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Two broadband seismometers were installed on the 4100 level and recorded for the duration of EGS Collab Experiment #2. Inspired by published data from similar instruments installed in the Aspo Hard Rock Lab, these long-period instruments aimed to measure the tilting of the drift in response to the injection of fluid into the testbed. One instrument was installed underneath the wellheads in Site A (aka the "battery" alcove) and the other was installed along the east wall of the drift, south of Site B. Due to the feet of gravel (ballast) laid along the floor of the drift, we were unable to anchor the sensors directly to the rock. As a result, the coupling of the sensors to the experiment rock volume is likely poor. In addition, there are a number of noise sources that complicate the interpretation of the data. For example, sensor BBB is installed adjacent (within 3 ft) to the rail line that runs towards the Ross shaft. Trains (motors) run along this line almost daily and produce a large signal in these data. Careful extraction of periods of interest, as well as filtering for specific signals, is necessary. The sensors are Nanometrics Trillium Compact Posthole seismometers, sensitive down to 120 seconds period. They were installed as close to the drift wall and as deep as we could manually excavate (only about 1 ft or so). The holes were leveled with sand and the sensors were placed on a paver before backfilling with sand. The hole was then covered by a bucket filled with insulation to improve the sensor's isolation from daily temperature variations, which are minor but present due to drift ventilation from the surface. Data were recorded on Nanometrics Centaur digitizers at 100 Hz. The full response information is available in the StationXML file provided here, or by querying the sensors through the IRIS DMC (see links below). These instruments were provided free of charge through the IRIS PASSCAL instrument center. The network code is XP and the station codes are BBA and BBB. The waveform data can be queried through the IRIS FDSN server using any method the user likes. One convenient option is to use the Obspy python package: https://docs.obspy.org/packages/obspy.clients.fdsn.html
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Abstract Although numerous efforts are made to synthesize active electrocatalysts for green hydrogen production; catalyst stability, and facile synthesis to scale up the production are still challenging. Herein, the production of novel non‐PGM catalysts for the oxygen reduction reaction (OER) in an alkaline aqueous medium is reported, which is based on the synthesis of a trimetallic metal–organic framework (MOF) precursors. Fine‐tuning of the composition of the metal centers (Ni, Co, and Fe) shows a great effect on OER activity after the MOF undergoes dynamic chemical and structural transformations under OER conditions. In situ characterization reveals the origin of OER activity enhancement as metals’ oxidation state increases, inducing compressive mechanical strain on metal centers, enhancing the electronic conductivity through the formation of oxygen vacancies, and stronger metal–oxygen covalency. Catalysts are used in membrane electrode assembly (MEA) setup within an industrial full‐cell anion exchange membrane electrolyzer (AEMEC), showing a stable performance for 550 h without noticeable decay at 750 and 1000 mA cm −2 industrial level current densities.
Dysfunctional mitochondrial activity can lead to a variety of different diseases. As such, there exists a need to quantify changes in mitochondria function as it relates to these specific diseased states. Here, we present the use of resonance Raman (RR) spectroscopy as a tool to determine changes in isolated mitochondrial activity. RR spectroscopy, using 532 nm as the excitation source, specifically provides information on the reduction and oxidation (RedOx) state of cytochrome c, which is determined by the activity of protein complexes in the electron transport chain. In this model, injection of the substrate succinate into the mitochondrial sample is used to drive the electron transport chain, which causes a subsequent change in cytochrome c RedOx state. This change in RedOx state is tracked by RR spectroscopy. We report this tool gives real-time information on the rise and fall of the amount of reduced cytochrome c within the mitochondrial sample, providing a method for rapid assessment of mitochondrial metabolism that has broad applications in both basic science and medical research.
Application of high-frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land-use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site-specific models were developed using data from a hillslope-dominated forest or a low-relief wetland-pond-dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L −1 , mean = 8.38 mg C L −1 , training RMSE = 0.34 mg C L −1 , internal validation RMSE = 0.50 mg C L −1 , external validation RMSE = 2.43 mg C L −1 ). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T 2 and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T 2 statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA 254 values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L −1 . The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. In conclusion, implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.
Constraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost-affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1-m depth interval were high (52.0 ± 15.1 kg C m −2 and 2.74 ± 0.82 kg N m −2 ) and linearly related ( R 2 = 0.74, p < 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m −2 at 2 m; 117.1 ± 10.4 kg C m −2 at 3 m). The unique small-scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.
Abstract Refractory high‐entropy alloys (RHEAs) are considered promising candidate materials for next‐generation nuclear reactors due to their superior mechanical strength, irradiation resistance, and thermal stability at high temperatures. However, the significant positive heat of mixing between refractory alloying elements and Cu, commonly used in cooling systems, poses challenges in forming composite structures. This study addresses the issue using a liquid metal dealloying (LMD) process. A precursor alloy (WTaVTi) with a directional dendrite‐interdendrite structure is fabricated and reacted with molten Cu at 1200 °C for 96 h. This approach produced a RHEA‐Cu composite with a stable interface between RHEA (W 31.5 Ta 30.9 V 21.4 Ti 14.3 ) and Cu, featuring a spontaneously formed W‐rich interlayer that enhances interfacial bonding. The composite showed excellent irradiation resistance, with 30% less swelling under α‐ion irradiation than pure W. It also exhibited low thermal conductivity at room temperature, but reached ≈120 W m −1 ·K −1 at ≈650 °C, surpassing pure W. This temperature‐dependent rise in κ, with a positive gradient of +0.075 W m −1 ·K − 2 , is attributed to decreasing diffuse mismatch at elevated temperatures. The large‐scale reaction and stable microstructure achieved through LMD process highlight its industrial potential. This work offers a strategy for developing high‐performance materials by combining RHEA's radiation resistance with Cu's thermal conductivity for extreme environments.
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In this study, a suite of experimental tools and fast-acting, numerical-simulation techniques was used to quantify the precipitation behavior of three nickel-base superalloys: IN-100, LSHR, and 718. Experimental methods comprised differential scanning calorimetry (DSC) to establish the specific heat as a function of temperature and selected direct-resistance heating trials (using a Gleeble ® machine) to obtain samples for microstructural analysis. For the DSC experiments, each alloy was cooled at a prescribed constant rate (between 5 and 20 K/min) after an initial soak/equilibration in the high-temperature, single-phase (supersolvus) temperature regime. On-heating DSC trials beginning at ambient temperature were also performed on alloy 718 in three different starting conditions: super- δ -solvus solution treated and water quenched (denoted as ST), solution treated and aged (STA), and solution treated and overaged (STOA). DSC results, revealing the thermal signatures associated with the kinetics of precipitation of γ ' (IN-100, LSHR) or γ ' and γ " (718), were interpreted using a previously-developed fast-acting routine that treats concurrent nucleation, growth, coarsening, and dissolution. For these simulations, special attention was paid to various thermo-kinetic input parameters including equilibrium solvus-approach curves, bulk free energies of transformation, matrix-precipitate interface energies, and effective diffusivities. For the γ - γ ' superalloys (IN-100 and LSHR), estimates of precipitate volume fraction as a function of temperature from the specific-heat data revealed semi-quantitative agreement with simulation predictions. For the γ - γ '- γ " superalloy (718), simulation predictions of precipitate volume fractions were converted to specific heat as a function of temperature and showed semi-quantitative agreement with the direct measurements.
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