Facile Fabrication of Polymer Electrolytes with Branched Structure via Deep Eutectic Electrolyte-Enabled In Situ Polymerizations.

The demand for higher energy density in energy storage devices drives further research on lithium metal batteries (LMBs) because of the high theoretical capacity and low voltage of lithium metal anode. Polymer electrolytes (PEs) exhibit obvious advantages in combating volatilization and leakage compared with liquid electrolytes, which improves the safety of LMBs. However, it is still difficult to construct PEs with a stable electrolyte-electrode interface for high-performance and long-term life LMBs. Herein, the gel polymer electrolyte (GPE-SL) containing deep eutectic electrolyte (DEE) and branchlike polymer skeleton are designed and prepared by the DEE-induced in situ cationic and radical polymerizations. The DEE provides a smooth Li+ migration pathway to ensure the electrochemical properties, and the multibrominated polymer matrix formed in situ enables a LiBr-rich solid electrolyte interphase (SEI) layer on lithium metal anode and prolongs the life span of LMBs. Hence, the Li|GPE-SL|LiFePO4 battery displays an excellent cycling stability with 84% capacity retention after 1200 cycles at 1C. This simple deep eutectic electrolyte-induced polymerization method provides a promising direction for high-performance LMBs with improved anode-electrolyte compatibility through the construction of a stable SEI layer in situ.

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Automatic segmentation of cotton roots in high-resolution minirhizotron images based on improved OCRNet.

Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an accuracy of 0.9866, a recall of 0.9419, a precision of 0.8887, an F1 score of 0.9146 and an Intersection over Union (IoU) of 0.8426. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images.

Cotton leaf segmentation with composite backbone architecture combining convolution and attention.

Plant leaf segmentation, especially leaf edge accurate recognition, is the data support for automatically measuring plant phenotypic parameters. However, adjusting the backbone in the current cutting-edge segmentation model for cotton leaf segmentation applications requires various trial and error costs (e.g., expert experience and computing costs). Thus, a simple and effective semantic segmentation architecture (our model) based on the composite backbone was proposed, considering the computational requirements of the mainstream Transformer backbone integrating attention mechanism. The composite backbone was composed of CoAtNet and Xception. CoAtNet integrated the attention mechanism of the Transformers into the convolution operation. The experimental results showed that our model outperformed the benchmark segmentation models PSPNet, DANet, CPNet, and DeepLab v3+ on the cotton leaf dataset, especially on the leaf edge segmentation (MIoU: 0.940, BIoU: 0.608). The composite backbone of our model integrated the convolution of the convolutional neural networks and the attention of the Transformers, which alleviated the computing power requirements of the Transformers under excellent performance. Our model reduces the trial and error cost of adjusting the segmentation model architecture for specific agricultural applications and provides a potential scheme for high-throughput phenotypic feature detection of plants.

Abundant Hydrogen Bonds Formed in a Urea-Based Gel Polymer Electrolyte Improve Interfacial Stability in Lithium Metal Batteries.

As an emerging and potential replacement system for liquid electrolytes, polymer electrolytes (PEs) exhibit unique capacity in suppressing metal dendrite formation and leakage risks. However, the most used polymer matrix, including polyether, polyester, and polysiloxane, still cannot meet the practical demands for metal electrode compatibility and long lifespan. In this study, gel polymer electrolytes consisting of a polyurea network with abundant hydrogen bonds and deep eutectic electrolyte (DEE) are designed and prepared in-situ. The hydrogen bonding between polyurea chains and polyurea-DEE provides good interfacial stability between PEs and lithium metal. As a result, the assembled Li/LiFePO4 cells based on this electrolyte deliver a long cycle life with 90 % retention after 500 cycles and 76.5 % retention after 1000 cycles at 1 C. In addition, the flexible design characteristics of polyurea structure permit easy operation for performance optimization by modulating the composition of hard and soft segments, and enhanced ionic conductivity and self-healing efficiency are obtained. This study provides a novel method for preparing advanced polymer electrolytes for lithium metal batteries.

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning.

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376-1044 nm) and near-infrared (NIR) (915-1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

[A study on visual development among preschool children].

OBJECTIVE: To evaluate the visual development and prevalence of amblyopia, strabismus among preschool children. METHODS: A random sample survey was performed in 4 610 preschool children from both urban and rural, aged 3 to 6 years. Participants underwent eye examination including visual acuity, refractive status, eye position, strabismus and amblyopia. RESULTS: Percentage of visual acuity above 1.0 was 28.4%, 39.3%, 46.2% and 76.5% in children of 3, 4, 5 and 6-year-old group, respectively. The mean visual acuity of each group was 0.63 ± 0.19 in 3-years old, 0.69 ± 0.16 in 4-year-old, 0.71 ± 0.22 in 5-year-old, 0.79 ± 0.29 in 6-year-old. Rural children have better vision acuity compared with those from urban. Hypermetropic was frequent refractive errors. Manifeststrabismus was found in 2.21%, with exotropia being more prevalence than esotropia; detection rate of recessive strabismus was 33.52%, mainly being exophoria; Based on current diagnostic criteria, the prevalence of amblyopia were 2.93% in 6 year-old group, 4.81% in 5-year-old group, 16.21% in 4-year-old group, 33.33% in 3-year-old group. CONCLUSION: Vision acuity is increasing with age in preschool population. A diagnosis standard of amblyopia suitable for each age group should be established to substitute the current one which has a high visual standard for amblyopia. Refractive error, strabismus and amblyopia are the leading causes of visual impairment among preschool-aged children, which represent the focus of prevention of blindness in preschool children.