Tribological Properties of Alkylphenyl Diphosphates as High-Performance Antiwear Additive in Lithium Complex Grease and Polyurea Grease for Steel/Steel Contacts at Elevated Temperature

The alkylphenyl diphosphates pentaerythritol tetrakis­(diphenyl phosphate) (PDP) and trimethylolpropane tris­(diphenyl phosphate) (TDP) were evaluated as the antiwear additives in lithium complex grease and polyurea grease at 200 °C. The results indicated that both additives may effectively reduce the sliding friction and wear as compared to the base greases. The tribological performances were generally better than the normally used molybdenum disulfide (MoS₂)-based additive package in lithium complex grease and also in polyurea grease. Boundary lubrication films composed of Fe­(OH)­O, Fe₃O₄, FePO₄, and compounds containing the P–O bonds were formed on the worn surface, which resulted in excellent friction reduction and antiwear performance

Ferroelectricity in Covalently functionalized Two-dimensional Materials: Integration of High-mobility Semiconductors and Nonvolatile Memory

Realization of ferroelectric semiconductors by conjoining ferroelectricity with semiconductors remains a challenging task because most present-day ferroelectric materials are unsuitable for such a combination due to their wide bandgaps. Herein, we show first-principles evidence toward the realization of a new class of two-dimensional (2D) ferroelectric semiconductors through covalent functionalization of many prevailing 2D materials. Members in this new class of 2D ferroelectric semiconductors include covalently functionalized germanene, and stanene (<i>Nat. Commun.</i> <b>2014</b>, <i>5</i>, 3389), as well as MoS₂ monolayer (<i>Nat. Chem.</i> <b>2015</b>, <i>7</i>, 45), covalent functionalization of the surface of bulk semiconductors such as silicon (111) (<i>J. Phys. Chem. B</i> <b>2006</b>, <i>110</i> , 23898), and the substrates of oxides such as silica with self-assembly monolayers (<i>Nano Lett.</i> <b>2014</b>, <i>14</i>, 1354). The newly predicted 2D ferroelectric semiconductors possess high mobility, modest bandgaps, and distinct ferroelectricity that can be exploited for developing various heterostructural devices with desired functionalities. For example, we propose applications of the 2D materials as 2D ferroelectric field-effect transistors with ultrahigh on/off ratio, topological transistors with Dirac Fermions switchable between holes and electrons, ferroelectric junctions with ultrahigh electro-resistance, and multiferroic junctions for controlling spin by electric fields. All these heterostructural devices take advantage of the combination of high-mobility semiconductors with fast writing and nondestructive reading capability of nonvolatile memory, thereby holding great potential for the development of future multifunctional devices

Attention mechanism-based deep learning approach for wheat yield estimation and uncertainty analysis from remotely sensed variables

Rapid and accurate crop yield estimation is an imperative aspect of agricultural planning that is important for crop management, food security and commodity trading. There are many related factors affecting wheat yield and the relationship between them and the yield is complicated, with nonlinear spatial-temporal characteristics that are difficult to describe accurately with mathematical functions. Deep learning models can fit complex nonlinear functions efficiently and transform input data into high-dimensional features automatically. However, the feature learning process does not produce transparent information. There has been considerable evidence that the ability of attention mechanism for modeling interpretation has been demonstrated in many fields. Therefore, an attention mechanism-based multi-level crop network (AMCN) was proposed to estimate the county level wheat yield based on remote sensing data and meteorological data. To explore the difference in spatio-temporal feature extraction ability under parallel and series structures when combining CNN with LSTM, we designed the AMCN models with two forms of structure, one is a parallel module of LSTM and CNN (AMCN1) and the other is a serial connection module between LSTM and CNN (AMCN2). Our results showed that the AMCN1 model provided an improved estimation accuracy as compared to that of the AMCN2 model. We also found remote sensing data contributed significantly to crop yield estimation mainly at the late growth stages, meteorological data provided additional information mainly at the early growth stage. We assessed the estimated uncertainty using Monte Carlo dropout, and the results indicated that the uncertainty level decreased gradually as the growth stages proceeded. In addition, extreme events such as drought and uneven distribution characteristics of the samples were associated with much higher estimated uncertainties. The study highlighted that the proposed model provided more accurate yield estimations by taking advantage of multi-level crop networks while considering the uncertainty involved in model estimations

Combining Sentinel-1 and -3 Imagery for Retrievals of Regional Multitemporal Biophysical Parameters Under a Deep Learning Framework

Regions with excessive cloud cover lead to limited feasibility of applying optical images to monitor crop growth. In this article, we built an upsampling moving window network for regional crop growth monitoring (UMRCGM) model to estimate the two key biophysical parameters (BPs), leaf area index (LAI), and canopy chlorophyll content (CCC) during the main growth period of winter wheat by using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-3 optical images. Sentinel-1 imagery is unaffected by cloudy weather and Sentinel-3 imagery has a wide width and short revisit period, the organic combination of the two will greatly improve the ability to monitor crop growth at a regional scale. The impact of two different types of SAR information (intensity and polarization) on the estimation of the two BPs was further analyzed. The UMRCGM model optimized the correspondence between inputs and outputs, it had more accurate LAI and CCC estimates compared with the three classical machine learning models, and had the highest accuracy at the green-up stage of winter wheat, followed by the jointing stage and the heading-filling stage, and the lowest accuracy was found at the milk maturity stage. The estimation accuracies of CCC were slightly higher than that of LAI for the first three growth stages of winter wheat, while lower than that of LAI for the milk maturity stage. This article proposes a new method for regional BPs (especially for CCC) estimation by combining SAR and optical imagery with large differences in spatial resolution under a deep learning framework.</p

A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables

Timely and accurate yield prediction before wheat harvest is of great significance for food policy formulation and national economic development. Deep learning method gains importance on crop yield estimation and growth monitoring with the rapid development of deep learning models combined with remotely sensed data. However, the problems of nonlinear parameter optimization and long-term dependence in time series data have restricted the improvement of yield estimation accuracy. Transformer model completely abandons the traditional deep learning architecture and is gradually applied to time series tasks due to the advantage in modeling long-term dependence. This study introduced a novel transformer-based deep learning framework to estimate winter wheat yield in the Guanzhong Plain utilizing remotely sensed multi-variables, namely leaf area index (LAI), fraction of photosynthetically active radiation (FPAR) and vegetation temperature condition index (VTCI). The proposed model, called SSA-LSTM-transformer (SLTF), was developed for dealing with above problems under the automatic optimization capability of the sparrow search algorithm (SSA) and the long-term memory advantage of the long short-term memory (LSTM) structure. The findings demonstrated that the SLTF model achieved better estimation accuracy at the county level (R2 = 0.72, RMSE = 488.68 kg/ha) compared with the single transformer model (R2 = 0.61, RMSE = 573.99 kg/ha). The analysis on SLTF model's performance at sampling sites over different disasters suggested that the model can effectively learn the effects of diseases, lodge and pests on crop yield estimation, which had good generalization ability at all sampling sites (R2 = 0.45, RMSE = 738.63 kg/ha). The Shapley Additive exPlanations (SHAP) approach was employed to evaluate the relative importance of remotely sensed multi-variables to yield and to understand how each input variable influences the estimated yield for global interpretability and local interpretability of the SLTF model. It was found that FPAR played the largest roles in yield estimation with the highest importance value, and FPAR and LAI from late April to late May and VTCI from late March to mid-April were regarded as important features for the estimated yield with high importance value. In conclusion, our findings indicated the potential of SLTF model for winter wheat yield estimation, which contributes to promoting further application of remotely sensed technology for agricultural production

Spatiotemporal Data Fusion of Index-Based VTCI Using Sentinel-2 and -3 Satellite Data for Field-Scale Drought Monitoring

Due to climate change, the impact of drought on field crop production is extremely important. This study focuses on the vegetation temperature condition index (VTCI), an index-based drought monitoring index that can characterize drought conditions in near real time (at ten-day intervals), and explores the applicability of different spatial and temporal data fusion schemes to it. It also proposes a field-scale VTCI fusion framework based on the Sentinel-3 VTCI calculation and the land surface temperature (LST) downscaling. First, based on analyzing the computational characteristics of VTCI, multiyear VTCI based on Sentinel data sources was obtained, which further expands the diversity of data sources for VTCI. On this basis, a combination of qualitative and quantitative methods was used to compare the applicability of two schemes: Scheme 1, based on the 'blend-then-index' (BI) strategy, which first fuses normalized difference vegetation index (NDVI) and LST, and then calculated the fused VTCIs; and Scheme 2, based on the 'index-then-blend' (IB) strategy, which directly fuses the VTCIs based on the calculated VTCIs. It was found that all the fused VTCIs remained highly correlated with the ten-day cumulative precipitation. Compared with the fused VTCIs obtained by Scheme 2, the VTCIs obtained by Scheme 1 were able to display more spatial details. In addition, the VTCIs of Scheme 1 were more consistent with the Sentinel-3 VTCIs, and the accuracy of field yield estimation using the fused VTCIs was higher ( $r$ of 0.58 and root-mean-square error (RMSE) of 783.27 kg/ha).</p

Macroscopic Piezoelectricity of Halide Perovskite Single Crystals and Their Highly Sensitive Self-Powered X‑ray Detectors

The FAxMA1–xPbI3 single crystal has excellent semiconductor photoelectric performance and good stability; however, there have been conflicting opinions regarding its macroscopic piezoelectricity. Here, the FAxMA1–xPbI3 (x = 0–0.1) single crystals (FAx SCs) exhibit a high macroscopic piezoelectric d33 coefficient of over 10 pC/N. The single crystal transforms from a tetragonal ferroelectric phase to a cubic paraelectric phase at x = 0.1–0.125. Furthermore, the fully polarized MAPbI3 and FA0.05 SCs were applied to prepare self-powered X-ray detectors with vertical structures. The sensitivity of the detector reaches 5.1 × 104 μC·Gy–1·cm–2 under a 0 V bias voltage, and its detection limit is as low as 50 nGy/s. This work provides an approach to designing self-powered and high-quality detectors with piezoelectric semiconductors

Ultrathin Alumina Mask-Assisted Nanopore Patterning on Monolayer MoS₂ for Highly Catalytic Efficiency in Hydrogen Evolution Reaction

Nanostructured molybdenum disulfide (MoS₂) has been considered as one of the most promising catalysts in the hydrogen evolution reaction (HER), for its approximately intermediate hydrogen binding free energy to noble metals and much lower cost. The catalytically active sites of MoS₂ are along the edges, whereas thermodynamically MoS₂ favors the presence of a two-dimensional (2-D) basal plane and the catalytically active atoms only constitute a small portion of the material. The lack of catalytically active sites and low catalytic efficiency impede its massive application. To address the issue, we have activated the basal plane of monolayer 2H MoS₂ through an ultrathin alumina mask (UTAM)-assisted nanopore arrays patterning, creating a high edge density. The introduced catalytically active sites are identified by Cu electrochemical deposition, and the hydrogen generation properties are assessed in detail. We demonstrate a remarkably improved HER performance as well as the identical catalysis of the artificial edges and the pristine metallic edges of monolayer MoS₂. Such a porous monolayer nanostructure can achieve a much higher edge atom ratio than the pristine monolayer MoS₂ flakes, which can lead to a much improved catalytic efficiency. This controllable edge engineering can also be extended to the basal plane modifications of other 2-D materials, for improving their edge-related properties

Transparent Glass with the Growth of Pyramid-Type MoS₂ for Highly Efficient Water Disinfection under Visible-Light Irradiation

Water disinfection is of great importance for human health and daily life. Photocatalysts with high efficiency, environmental protection, and narrow bandgaps are critical for practical water treatment. Here, a general approach is reported for the direct growth of pyramid-type MoS₂ (pyramid MoS₂) on transparent glass by chemical vapor deposition (CVD). The pyramid MoS₂ exhibits a smaller bandgap and higher bactericidal activity than most TiO₂-based photocatalysts. The adjustable-bandgap nature of two-dimensional (2-D) MoS₂ can harvest a wide spectrum of sunlight and provide more active sites with which to generate reactive oxygen species (ROS) for bacterial death in water. Furthermore, silver (Ag) with several nanometers thicknesses is thermally evaporated on the pyramid MoS₂, which can greatly facilitate electron–hole pair separation to generate more ROS and has a certain bactericidal effect. With our established approach, under simulated visible light, more than 99.99% of <i>Escherichia coli</i> can be successfully deactivated in 40 min, with an effective mass per unit of less than 0.7 mg L^–1 in a 0.9 wt % NaCl solution. Besides, for the first time, the generation of ROS is confirmed with in situ Raman spectroscopy on pyramid MoS₂@Ag glass, and the related bactericidal mechanism is present as well

Magnetoelectric Coupling in Well-Ordered Epitaxial BiFeO₃/CoFe₂O₄/SrRuO₃ Heterostructured Nanodot Array

Multiferroic magnetoelectric (ME) composites exhibit sizable ME coupling at room temperature, promising applications in a wide range of novel devices. For high density integrated devices, it is indispensable to achieve a well-ordered nanostructured array with reasonable ME coupling. For this purpose, we explored the well-ordered array of isolated epitaxial BiFeO₃/CoFe₂O₄/SrRuO₃ heterostructured nanodots fabricated by nanoporous anodic alumina (AAO) template method. The arrayed heterostructured nanodots demonstrate well-established epitaxial structures and coexistence of piezoelectric and ferromagnetic properties, as revealed by transmission electron microscopy (TEM) and peizoeresponse/magnetic force microscopy (PFM/MFM). It was found that the heterostructured nanodots yield apparent ME coupling, likely due to the effective transfer of interface couplings along with the substantial release of substrate clamping. A noticeable change in piezoelectric response of the nanodots can be triggered by magnetic field, indicating a substantial enhancement of ME coupling. Moreover, an electric field induced magnetization switching in these nanodots can be observed, showing a large reverse ME effect. These results offer good opportunities of the nanodots for applications in high-density ME devices, <i>e.g.</i>, high density recording (>100 Gbit/in.²) or logic devices