An ultrathin and continuous Li_4Ti_5O_(12) coated carbon nanofiber interlayer for high rate lithium sulfur battery

Severe capacity fading and poor high rate performance of lithium sulfur(Li–S) battery caused by "shuttle effect" and low conductivity of sulfur hampers its further developments and applications. Li_4Ti_5O_(12) (LTO)possesses high lithium ion conductivity, and it is also can be used as an active adsorbent for polysulfide. Herein, fine LTO particle coated carbon nanofibers(CNF) were prepared and a conductive network both for electron and lithium ion was built, which can greatly promote the electrochemical conversion of polysulfide and improve the rate performance of Li–S batteries. Meanwhile, a quantity of adsorption sites is constructed by defects of the surface of LTO-CNF membrane to effectively immobilize polysulfide. The multifunctional LTO-CNF interlayer could impede the shuttle effect and enhance comprehensive electrochemical performance of Li–S batteries, especially high rate performance. With such LTO-CNF interlayer,the Li–S battery presents a specific capacity of 641.9 mAh/g at 5 C rate. After 400 cycles at 1 C, a capacity of 618.0 mAh/g is retained. This work provides a feasible strategy to achieve high performance of Li–S battery for practical utilization.

基于OFDM导频信号的测距测速方法

实现车辆的距离和速度估计是车载通信与网络领域的重要方向,针对导频信号具有功率大、良好的无源探测性能等优点,本文在OFDM信号的基础上,提出了一种利用OFDM导频信号进行测距测速的方法。首先推导得到了OFDM导频信号接收信号频域矩阵,对其行、列向量频谱进行搜索,经过处理得到目标的距离和速度,然后通过仿真实验及性能分析,验证了OFDM导频信号能够满足车载通信网络中车辆测距测速要求。

Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits

Nutrient-efficient root system architecture(RSA)is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency.Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition.Here,we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water.We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers,which can be erroneously quantified as root tips.This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms.There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips(95%correlation),when the background was cleaned of nonroot materials compared to root images without the background corrected(79%).Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips.Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth,total root length and surface area,and root system volume and surface area of thicker roots(DOF1-like zinc finger transcription factor,protein of unknown function,and C2H2 zinc finger protein).We also identified a putative negative regulator(gibberellin 20 oxidase 3)of the total number of lateral roots.

In-situ polymerized cross-linked binder for cathode in lithium-sulfur batteries

Volume expansion and polysulfide shuttle effect are the main barriers for the commercialization of lithium-sulfur(Li-S) battery.In this work,we in-situ polymerized a cross-linked binder in sulfur cathode to solve the aforementioned problems using a facile method under mild conditions.Polycarbonate diol(PCDL),triethanolamine(TEA) and hexamethylene diisocyanate(HDI) were chosen as precursors to prepare the cross-linked binder.The in-situ polymerized binder(PTH) builds a strong network in sulfur cathode,which could restrain the volume expansion of sulfu r.Moreover,by adopting functional groups of oxygen atoms and nitrogen atoms,the binder could effectively facilitate transportation of Li-ion and adsorb polysulfide chemically.The Li-S battery with bare sulfur and carbon/sulfur composite cathodes and cross-linked PTH binder displays much better electrochemical performance than that of the battery with PVDF.The PTH-bare S cathode with a mass loading of 5.97 mg/cm^2 could deliver a capacity of 733.3 mAh/g at 0.2 C,and remained 585.5 mAh/g after 100 cycles.This in-situ polymerized binder is proved to be quite effective on restraining the volume expansion and suppressing polysulfide shuttle effect,then improving the electrochemical performance of Li-S battery.

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction.Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs.Conventionally,soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress,which is labor-intensive and subjective to human error.Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses.The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights.The flooding injury score(FIS)of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms.Aerial images were taken on the same day using a five-band multispectral and an infrared(IR)thermal camera at 20,50,and 80 m above ground.Five image features,i.e.,canopy temperature,normalized difference vegetation index,canopy area,width,and length,were extracted from the images at three flight heights.A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features.Results show that the image features were significantly different at three flight heights.The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS.The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.