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Leakage of hydrocarbon fuel (light nonaqueous-phase liquid, LNAPL) from petroleum processing facilities and storage tanks may result in significant subsurface contamination. Remediating the contaminated areas represent considerable challenges, especially when remediation resources are limited and site data are incomplete. A reasonable management strategy under this scenario may be to identify sites where LNAPL recovery operations should be located that would provide the largest LNAPL recovery initially while minimizing the LNAPL remaining in the subsurface (entrapped and residual LNAPL), which may serve as future sources for groundwater contamination. To accomplish this objective, we use estimates of subsurface recoverable and total LNAPL specific volumes and LNAPL transmissivities to generate GIS maps that can be combined to highlight locations where to develop LNAPL recovery operations. When the approach is applied to a LNAPL-contaminated area in Iran, we were able to narrow the locations for potential LNAPL recovery operations. Specifically, we combine maps of the LNAPL specific yield, an introduced term, and the LNAPL transmissivity where the LNAPL specific yield is the ratio of the recoverable to total LNAPL specific volumes. The LNAPL specific yield is a relative measure of the amount of LNAPL that potentially can be recovered while minimizing residual LNAPL in soils. The approach can be applied to sites where the recoverable and total LNAPL specific volumes and LNAPL transmissivities can be estimated using data from boreholes in the contaminated area.

High cell densities in animal cell culture can be obtained by continuous perfusion of fresh culture medium across hollow fiber membranes that retain the cells. Careful selection of the membrane type and cut-off allows to control accumulation of target molecules and removal of low molecular weight compounds. In this report, perfusion with the scalable ATF (alternating tangential filtration, Refine Technology) system was evaluated for two suspension cell lines, the avian cell line AGE1.CR and the human cell line CAP. Both were cultivated in chemically defined media optimized for batch cell growth in a 1L stirred tank bioreactor connected to the smallest ATF unit (ATF2) and infected with cell line-adapted human influenza A virus (A/PR/8/34 (H1N1), typical diameter: 80–100nm). At concentrations of about 25 million cells/mL three different membrane cut-offs (50kDa, 0.2μm and 0.5μm) were tested and compared to batch cultivations performed at 5 million cells/mL. For medium and large cut-offs no cell-density effect could be observed with cell-specific virus yields of 1428–1708 virions/AGE1.CR cell (infected with moi 0.001) and 1883–4086 virions/CAP cell (moi of 0.025) compared to 1292 virions/AGE1.CR cell and 3883 virions/CAP cell in batch cultures. Even at a concentration of 48 million AGE1.CR cells/mL (cut-off: 0.2μm) a cell-specific yield of 1266 virions/cell was reached. Only for the small cut-off (50kDa) used with AGE1.CR cells a decrease in cell-specific yield was measured with 518 virions/cell. Surprisingly, the ratio of infectious to total virions seemed to be increased in ATF compared to batch cultures. AGE1.CR cell-derived virus particles were present in the permeate (0.2 and 0.5μm cut-off), whereas CAP cell-derived virions were not, suggesting possible differences in morphology, aggregation or membrane properties of the virions released by the two cell lines. To our knowledge, this is the first study that illustrates the potential of ATF-based perfusion of chemically defined media across cell-retaining membranes for production of an influenza A vaccine.

Spatially distributed values of the specific yield, a fundamental parameter for transient groundwater mass balance calculations, were obtained by means of three independent methods for the Crau plain, France. In contrast to its traditional use to assess recharge based on a given specific yield, the water-table fluctuation (WTF) method, applied using major recharging events, gave a first set of reference values. Then, large infiltration processes recorded by monitored boreholes and caused by major precipitation events were interpreted in terms of specific yield by means of a one-dimensional vertical numerical model solving Richards’ equations within the unsaturated zone. Finally, two gravity field campaigns, at low and high piezometric levels, were carried out to assess the groundwater mass variation and thus alternative specific yield values. The range obtained by the WTF method for this aquifer made of alluvial detrital material was 2.9– 26%, in line with the scarce data available so far. The average spatial value of specific yield by the WTF method (9.1%) is consistent with the aquifer scale value from the hydro-gravimetric approach. In this investigation, an estimate of the hitherto unknown spatial distribution of the specific yield over the Crau plain was obtained using the most reliable method (the WTF method). A groundwater mass balance calculation over the domain using this distribution yielded similar results to an independent quantification based on a stable isotope-mixing model. This agreement reinforces the relevance of such estimates, which can be used to build a more accurate transient hydrogeological model.

While laser-printed metals do not tend to match the mechanical properties and thermal stability of conventionally-processed metals, incorporating and dispersing nanoparticles in them should enhance their performance. However, this remains difficult to do during laser additive manufacturing. Here, we show that aluminum reinforced by nanoparticles can be deposited layer-by-layer via laser melting of nanocomposite powders, which enhance the laser absorption by almost one order of magnitude compared to pure aluminum powders. The laser printed nanocomposite delivers a yield strength of up to 1000 MPa, plasticity over 10%, and Young’s modulus of approximately 200 GPa, offering one of the highest specific Young’s modulus and specific yield strengths among structural metals, as well as an improved specific strength and thermal stability up to 400 °C compared to other aluminum-based materials. The improved performance is attributed to a high density of well-dispersed nanoparticles, strong interfacial bonding between nanoparticles and Al matrix, and ultrafine grain sizes. Incorporating and dispersing dense nanoparticles into metals remains a challenge. Here, the authors use nanocomposite powders containing very dense nanoparticles to print an aluminium nanocomposite with one of the highest specific modulus and yield strength among all structural materials.

Magnesium is light but not very strong; here the addition of silicon carbide nanoparticles uniformly dispersed to 14 per cent by volume, achieved through a nanoparticle self-stabilization mechanism in a molten metal alloy, yields improved strength, stiffness, plasticity and high-temperature stability. A new route to magnesium alloy composites The best magnesium alloys currently available are remarkably light but lack the strength offered by other structural metals. Magnesium-based composites could provide a way of retaining lightness while adding strength. Here Xiao-Chun Li and colleagues demonstrate the production of a dense uniform dispersion of silicon carbide nanoparticles (14 per cent by volume) in magnesium via nanoparticle self-stabilization in molten metal. The resulting composite has improved strength, stiffness, plasticity and high-temperature stability. By overcoming the long-standing challenge of dispersing nanoparticles in metal matrices, this new approach may offer a widely applicable route to high-performance light-metal composites. Magnesium is a light metal, with a density two-thirds that of aluminium, is abundant on Earth and is biocompatible; it thus has the potential to improve energy efficiency and system performance in aerospace, automobile, defence, mobile electronics and biomedical applications 1 , 2 , 3 , 4 , 5 . However, conventional synthesis and processing methods (alloying and thermomechanical processing) have reached certain limits in further improving the properties of magnesium and other metals 6 . Ceramic particles have been introduced into metal matrices to improve the strength of the metals 7 , but unfortunately, ceramic microparticles severely degrade the plasticity and machinability of metals 7 , and nanoparticles, although they have the potential to improve strength while maintaining or even improving the plasticity of metals 8 , 9 , are difficult to disperse uniformly in metal matrices 10 , 11 , 12 , 13 , 14 . Here we show that a dense uniform dispersion of silicon carbide nanoparticles (14 per cent by volume) in magnesium can be achieved through a nanoparticle self-stabilization mechanism in molten metal. An enhancement of strength, stiffness, plasticity and high-temperature stability is simultaneously achieved, delivering a higher specific yield strength and higher specific modulus than almost all structural metals.

A simplified method to determine specific yield (i.e., effective porosity) from hydraulic conductivity data obtained through pumping tests is proposed. This new method derives from a reprocessing of literature data and a subsequent calibration with results from pumping tests performed in different hydrogeological contexts. The use of the algorithm allows obtaining values of specific yield (Sy), which could be useful for the resolution of problems concerning the water balance and the transport of contamints in groundwater. The proposed algorithm is applied to a large-scale area (Milan and its suburbs, northwestern Italy) to determine a map of the specific yield of a sandy-gravel aquifer and the effects on the estimation of water volumes stored in the subsoil from a hydrogeological point of view, considering about seventy years of measures. It is demonstrated that the great variation in water volumes reflects the socio-economic history of the territory.

This study aimed to investigate the microstructure and mechanical properties of Ti x ZrVNb ( x = 1, 1.5, 2) refractory high-entropy alloys at room and elevated temperatures. The TiZrVNb alloy consisted of the body-centered cubic (bcc) matrix with a small amount of V 2 Zr phase. The Ti 1.5 ZrVNb and Ti 2 ZrVNb alloys exhibited a single-phase bcc structure. At room temperature, the tensile ductility of the as-cast alloys increased from 3.5% to 12.3% with the increase in the Ti content. The Ti x ZrVNb alloys exhibited high yield strength at 600°C, and the ultimate yield strength was more than 900 MPa. Softening occurred at 800°C, but the ultimate yield strength could still exceed 200 MPa. Moreover, the Ti x ZrVNb alloys displayed low densities but high specific yield strengths (SYSs). The lowest density of Ti x ZrVNb alloys was only 6.12 g/cm 3 , but the SYS could reach about 180 MPa·cm 3 ·g −1 , which is better than those of most reported high-entropy alloys (HEAs).

A hierarchical microstructure strengthened high entropy superalloy (HESA) with superior cost specific yield strength from room temperature up to 1,023 K is presented. By phase transformation pathway through metastability, HESA possesses a hierarchical microstructure containing a dispersion of nano size disordered FCC particles inside ordered L1 2 precipitates that are within the FCC matrix. The average tensile yield strength of HESA from room temperature to 1,023 K could be 120 MPa higher than that of advanced single crystal superalloy, while HESA could still exhibit an elongation greater than 20%. Furthermore, the cost specific yield strength of HESA can be 8 times that of some superalloys. A template for lighter, stronger, cheaper, and more ductile high temperature alloy is proposed.

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data.

Developing light yet strong aluminum (Al)-based alloys has been attracting unremitting efforts due to the soaring demand for energy-efficient structural materials. However, this endeavor is impeded by the limited solubility of other lighter components in Al. Here, we propose to surmount this challenge by converting multiple brittle phases into a ductile solid solution in Al-based complex concentrated alloys (CCA) by applying high pressure and temperature. We successfully develop a face-centered cubic single-phase Al-based CCA, Al 55 Mg 35 Li 5 Zn 5 , with a low density of 2.40 g/cm 3 and a high specific yield strength of 344×10 3  N·m/kg (typically ~ 200×10 3  N·m/kg in conventional Al-based alloys). Our analysis reveals that formation of the single-phase CCA can be attributed to the decreased difference in atomic size and electronegativity between the solute elements and Al under high pressure, as well as the synergistic high entropy effect caused by high temperature and high pressure. The increase in strength originates mainly from high solid solution and nanoscale chemical fluctuations. Our findings could offer a viable route to explore lightweight single-phase CCAs in a vast composition-temperature-pressure space with enhanced mechanical properties. By overcoming the limited solubility of other lighter components in aluminum using high pressure and high temperature, a low-density, high specific strength, and single-phase aluminum-based complex concentrated alloy is developed.