Effect of Li-deficiency impurities on the electron-overdoped LiFeAs superconductor

We use transport, inelastic neutron scattering, and angle resolved photoemission experiments to demonstrate that the stoichiometric LiFeAs is an intrinsically electron-overdoped superconductor similar to those of the electron-overdoped NaFe1-xTxAs and BaFe2-xTxAs2 (T = Co,Ni). Furthermore, we show that although transport properties of the stoichiometric superconducting LiFeAs and Li-deficient nonsuperconducting Li1-xFeAs are different, their electronic and magnetic properties are rather similar. Therefore, the nonsuperconducting Li1-xFeAs is also in the electron overdoped regime, where small Li deficiencies near the FeAs octahedra can dramatically suppress superconductivity through the impurity scattering effect.Comment: 5 figures,5 page

Normal-State Hourglass Dispersion of the Spin Excitations in FeSex_{x}Te1−x_{1-x}

We use cold neutron spectroscopy to study the low-energy spin excitations of superconducting (SC) FeSe$_{0.4}$Te$_{0.6}$ and essentially non-superconducting (NSC) FeSe$_{0.45}$Te$_{0.55}$. In contrast to BaFe$_{2-x}$(Co,Ni)$_{x}$As$_2$, where the low-energy spin excitations are commensurate both in the SC and normal state, the normal-state spin excitations in SC FeSe$_{0.4}$Te$_{0.6}$ are incommensurate and show an hourglass dispersion near the resonance energy. Since similar hourglass dispersion is also found in the NSC FeSe$_{0.45}$Te$_{0.55}$, we argue that the observed incommensurate spin excitations in FeSe$_{1-x}$Te$_{x}$ are not directly associated with superconductivity. Instead, the results can be understood within a picture of Fermi surface nesting assuming extremely low Fermi velocities and spin-orbital coupling.Comment: 4 pages, 4 figure

A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm

Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates

Experimental and Numerical Study of Jet Controlled Compression Ignition on Combustion Phasing Control in Diesel Premixed Compression Ignition Systems

In order to directly control the premixed combustion phasing, a Jet Controlled Compression Ignition (JCCI) for diesel premixed compression ignition systems is investigated. Experiments were conducted on a single cylinder natural aspirated diesel engine without EGR at 3000 rpm. Numerical models were validated by load sweep experiments at fixed spark timing. Detailed combustion characteristics were analyzed based on the BMEP of 2.18 bar. The simulation results showed that the high temperature jets of reacting active radical species issued from the ignition chamber played an important role on the onset of combustion in the JCCI system. The combustion of diesel pre-mixtures was initiated rapidly by the combustion products issued from the ignition chamber. Moreover, the flame propagation was not obvious, similar to that in Pre-mixed Charge Compression Ignition (PCCI). Consequently, spark timing sweep experiments were conducted. The results showed a good linear relationship between spark timing in the ignition chamber and CA10 and CA50, which indicated the ability for direct combustion phasing control in diesel PCCI. The NOx and soot emissions gradually changed with the decrease of spark advance angle. The maximum reduction of NOx and soot were both over 90%, and HC and CO emissions were increased

Plastic shed production systems: The migration of heavy metals from soil to vegetables and human health risk assessment

Plastic shed production system (PSPS) provide abundant vegetable products for human consumption. Comprehensive and accurate heavy metal (HM) risk assessment of soil and vegetable under plastic sheds is crucial for human health. Pollution assessment, bioavailability and mobility evaluation and health risk assessment of Cd, Cr, Cu, Zn Ni, Pb, and As were performed in a presentative Plastic shed production system. The concentrations of the Cd, Cu and Zn exceeded their background value. Positive Igeo values suggested that soil under plastic sheds was widely contaminated with Cd. The bioavailability of heavy metals in soils was evaluated using DTPA extraction and DGT methods. The results of both methods demonstrated that Cd, Cu, and Zn have high bioavailability, especially Cd. Analogically, the results of mobility assignment based on DIFS showed that Cd has a high migration risk due to the large available pool. Based on specific cultivation and management patterns of plastic shed production system, pH reduction and salt and nutrient accumulation may increase the heavy metals migration risk in soil under plastic sheds, while a high organic matter content may reduce the heavy metals migration risk. The average concentrations of Cd, Cr, Cu, Zn, Ni, Pb, and As in vegetables were 0.023, 0.226, 0.654, 2.984, 0.329, 0.041, and 0.010 mg/kg, respectively. All samples were well below the threshold. The order of target hazard quotient of different heavy metals caused by vegetable consumption was Cd > Cr > As > Cu, Ni, Pb, Zn, and the average total hazard index value was below 1, which demonstrated that risk of vegetable consumption in the study area. However, due to its high concentration and transfer coefficient in spinach, Cd might pose a health risk to humans, which requires special attention. In this study, Cd caused a significant issue than other HMs, whether pollution level, health risk and migration risk. DGT and DIFS can be used as an effective evaluation tool in the research of controlling heavy metals migration in soil-crop systems

The Availability and Accumulation of Heavy Metals in Greenhouse Soils Associated with Intensive Fertilizer Application

In China, greenhouse agriculture, which provides abundant vegetable products for human consumption, has been rapidly developed in recent decades. Heavy metal accumulation in greenhouse soil and products obtained have received increasing attention. Therefore, the availability and accumulation of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) and their association with soil pH, soil organic matter (SOM), inorganic nitrogen (IN), total nitrogen (TN), available phosphorus (AP), and planting year (PY) in greenhouse soils were analyzed. The results showed that the mean concentrations of available Cd, Cu, Ni, Pb, and Zn were 17.25 μg/kg, 2.89, 0.18, 0.36, and 5.33 mg/kg, respectively, while their suggested levels in China are 0.6, 100, 100, 120, and 250 mg/kg. Cd, Cu, and Zn might be mainly originated from fertilizer application. A lower soil pH significantly increased the available Cu, Ni, and Zn concentrations and reduced Cd, Cu, Ni, and Zn accumulation. A higher AP significantly increased the proportions of available Cu, Ni, and Zn and elevated Cd, Cu, and Zn accumulation. There was a strong positive correlation between Cd, Pb, and Zn availability and TN, while IN was negatively related to the availability and accumulation of Cu and Zn. It was concluded that chemical fertilizer application increased the availability of Cu, Ni, Pb, and Zn and the accumulation of Cd, Cu, and Zn. Manure application clearly elevated the accumulation and availability of Cd and Zn in greenhouse soil