No abstract

Graphite has dominated the market of anode materials for lithium-ion batteries in applications such as consumer electronic devices and electric vehicles. As commercial graphite anodes are approaching their theoretical capacity, significant efforts have been dedicated towards higher capacity by blending capacity-enhancing additives (e.g., Si) with graphite particles. In spite of the improved gravimetric capacity, the areal capacity of such composite anodes might decrease due to excess void spaces and an incompatible material size distribution. Herein, a rational design of compact graphite/Si/SiO2 ternary composites has been proposed to address the abovementioned issues. Si/SiO2 clusters with an optimal particle size are homogeneously dispersed in the interstitial spaces between graphite particles to promote the packing density, leading to a higher areal capacity than that of pure graphite with equivalent mass loading or electrode thickness. By taking the full intrinsic advantages of graphite, Si, and SiO2, the composite electrodes exhibit 553.6 mAh g–1 after 700 cycles with a capacity retention of 95.2%. Furthermore, the graphite/Si/SiO2 electrodes demonstrate a high coulombic efficiency with an average of 99.68% from 2nd to 200th cycles and areal capacities above 1.75 mAh cm–2 during 200 cycles with an areal mass loading as high as 4.04 mg cm–2. A packing model has been proposed and verified by experimental investigation as a design principle of densely compacted anodes. The effective strategy of introducing Si/SiO2 clusters into the void spaces between graphite particles provides an alternative solution for implementation of graphite–Si composite anodes in next-generation Li-ion cells.

Four novel heteropolyoxonibate-based inorganic–organic hybrids {Cu(en)2}6{GeNb12VIV 2O42}·20H2O (1), {Cu(en)2}3K2Na4{GeNb12VIV 2O42}·23H2O (2), {Cu(en)2}6{SiNb12VIV 2O42}·18H2O (3), and {Cu(en)2}3K2Na4{SiNb12VIV 2O42}·19H2O (4) (en = ethanediamine), composed of polyoxoanions [TNb12O40]16– (T = Si and Ge) and [Cu(en)2]2+ building blocks, were successfully synthesized under hydrothermal conditions by reaction of K7HNb6O19·13H2O, Cu(Ac)2·3H2O, Na2VO3, Na2SiO3, or GeO2 and en molecules. Polyoxoanion [TNb12VIV2O42]12– (T = Si and Ge) can be best described as a α-Keggin core [TNb12O40] with two [VO] units capping on its two “opened windows”. Compounds 1 and 3 are both composed of the bicapped heteropolyoxonibate core surrounded by a shell consisting of twelve [Cu(en)2]2+ groups, which represent a promising structural model toward core–shell nanostructures. Compounds 2 and 4 are also composed of a bicapped polyoxoanion [TNb12VIV 2O42]12– (T = Si, Ge) decorated by three metal–organic fragments [Cu(en)2]2+, forming a trisupporting polyoxoanion {[Cu(en)2]3[TNb12O42VIV 2]}6–. Antitumor, electrochemical study, and UV–vis spectra indicate that compounds 1–4 exhibit effective antitumor activity against SGC7901 cells and HepG2 cells and could keep the structural integrity in this process.

Background Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step. Results We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset. Conclusion The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.

Because of the large amount of energy consumed during symbiotic nitrogen fixation, legumes must balance growth and symbiotic nodulation. Both lateral roots and nodules form on the root system, and the developmental coordination of these organs under conditions of reduced nitrogen (N) availability remains elusive. We show that the Medicago truncatula COMPACT ROOT ARCHITECTURE2 (MtCRA2) receptor-like kinase is essential to promote the initiation of early symbiotic nodulation and to inhibit root growth in response to low N. C-TERMINALLY ENCODED PEPTIDE (MtCEP1) peptides can activate MtCRA2 under N-starvation conditions, leading to a repression of YUCCA2 (MtYUC2) auxin biosynthesis gene expression, and therefore of auxin root responses. Accordingly, the compact root architecture phenotype of cra2 can be mimicked by an auxin treatment or by overexpressing MtYUC2, and conversely, a treatment with YUC inhibitors or an MtYUC2 knockout rescues the cra2 root phenotype. The MtCEP1-activated CRA2 can additionally interact with and phosphorylate the MtEIN2 ethylene signaling component at Ser 643 and Ser 924 , preventing its cleavage and thereby repressing ethylene responses, thus locally promoting the root susceptibility to rhizobia. In agreement with this interaction, the cra2 low nodulation phenotype is rescued by an ein2 mutation. Overall, by reducing auxin biosynthesis and inhibiting ethylene signaling, the MtCEP1/MtCRA2 pathway balances root and nodule development under low-N conditions.

Red-to-near-infrared (NIR) fluorophores are highly desirable in bio-imaging study with advantages of high tissue penetration ability and less interference from auto-fluorescence. However, their preparation usually requires tedious synthetic procedures, which...

Phosphate is one of the major elements affecting agricultural production. The accurate determination of phosphate concentration essential for plant growth, especially in a hydroponics system, allows regulating the balanced and suitable range set of nutrients to plants efficiently. This study proposed a data fusion model based on 70 samples for calibration and 30 samples for predicting concentrations of phosphate in an eggplant nutrient solution. Three multivariate analysis methods i.e. partial least squares model (PLS), Gaussian process regression (GPR), and artificial neural network (ANN) were studied and compared for their performance efficiencies. The results showed that combining the multivariate standard addition method (MSAM) in acquiring data from cobalt electrodes and ANN data fusion model came up with satisfactory outcomes. Both the method provided good performance with R 2 values of 0.98 and 0.96, and the root mean square error (RMSE) of 50 and 66 mg. L −1 respectively in calibration and evaluation tests. These values were much higher than those of conventional processing techniques. Moreover, the normal direct calibration method in acquisition signal from cobalt electrodes was also applied, which provided R 2 values of 0.7 to 0.8. These high values are sufficient for development to measure phosphate concentration in hydroponic solutions. INDEX TERMS Phosphate sensing, multi-sensor data fusion, multivariate standard addition method (MSAM), partial least squares model (PLS), Gaussian process regression (GPR), neural network-ANN.

In this work, we introduce Ni nanopyramid arrays (NPAs) supported amorphous Ge anode architecture and demonstrate its effective improvement in sodium storage properties. The Ni−Ge NPAs are prepared by facile electrodeposition and sputtering method, which eliminates the need for any binder or conductive additive when used as a Na‐ion battery anode. The electrodes display stable cycling performance and enhanced rate capabilities in contrast with planar Ge electrodes, which can be owing to the rational design of the architectured electrodes and firm bonding between current collector and active material (i. e. Ni and Ge, respectively). To validate improvement of nanostructures on electrochemical performance, sodium insertion behavior of crystalline Ge derived from Mg 2 Ge precursor has been investigated, in which limited but effective enhancement of sodium storage properties are realized by introducing porous nanostructure in crystalline Ge. These results show that elaborately designed configuration of Ge electrodes may be a promising anode for Na‐ion battery applications.