Construction of Uniform LiF Coating Layers for Stable High-Voltage LiCoO2 Cathodes in Lithium-Ion Batteries.

Stabilizing LiCoO2 (LCO) at 4.5 V rather than the common 4.2 V is important for the high specific capacity. In this study, we developed a simple and efficient way to improve the stability of LiCoO2 at high voltages. After a simple sol-gel method, we introduced trifluoroacetic acid (TA) to the surface of LCO via an afterwards calcination. Meanwhile, the TA reacted with residual lithium on the surface of LCO, further leading to the formation of uniform LiF nanoshells. The LiF nanoshells could effectively restrict the interfacial side reaction, hinder the transition metal dissolution and thus achieve a stable cathode-electrolyte interface at high working-voltages. As a result, the LCO@LiF demonstrated a much superior cycling stability with a capacity retention ratio of 83.54% after 100 cycles compared with the bare ones (43.3% for capacity retention), as well as high rate performances. Notably, LiF coating layers endow LCO with excellent high-temperature performances and outstanding full-cell performances. This work provides a simple and effective way to prepare stable LCO materials working at a high voltage.

Petal-like Mn-doped α-Ni(OH)2 nanosheets for high-performance Li-S cathode material.

Lithium-sulphur (Li-S) batteries are high-energy-density and cost-effective batteries. Herein, petal-like Ni1-x Mn x (OH)2 (x ≈ 0.04) nanosheets were synthesised using a hydrothermal method and the electrical conductivity of Ni(OH)2 was improved by applying the cathode functional materials in Li-S batteries. With up to 5 mg cm-2 of S content in the cathode, the fabricated Ni1-x Mn x (OH)2 electrode exhibited specific discharge capacities up to 1375 and 1150 mA h g-1 at 0.2 and 0.5C, and retained this capacity at 813 and 714 mA h g-1 after 200 cycles, respectively. Electrochemical measurement results show that Ni1-x Mn x (OH)2 plays a critical role in Li-S batteries as it has a larger specific surface area than Ni(OH)2, which has superior adsorption performance toward lithium polysulphides. Moreover, the conductivity performance of Ni1-x Mn x (OH)2 is significantly better than that of Ni(OH)2, which improves the electrochemical reaction kinetics of the Li-S batteries.

Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery.

Desert steppes are the last barrier to protecting the steppe ecosystem. However, existing grassland monitoring methods still mainly use traditional monitoring methods, which have certain limitations in the monitoring process. Additionally, the existing deep learning classification models of desert and grassland still use traditional convolutional neural networks for classification, which cannot adapt to the classification task of irregular ground objects, which limits the classification performance of the model. To address the above problems, this paper uses a UAV hyperspectral remote sensing platform for data acquisition and proposes a spatial neighborhood dynamic graph convolution network (SN_DGCN) for degraded grassland vegetation community classification. The results show that the proposed classification model had the highest classification accuracy compared to the seven classification models of MLP, 1DCNN, 2DCNN, 3DCNN, Resnet18, Densenet121, and SN_GCN; its OA, AA, and kappa were 97.13%, 96.50%, and 96.05% in the case of only 10 samples per class of features, respectively; The classification performance was stable under different numbers of training samples, had better generalization ability in the classification task of small samples, and was more effective for the classification task of irregular features. Meanwhile, the latest desert grassland classification models were also compared, which fully demonstrated the superior classification performance of the proposed model in this paper. The proposed model provides a new method for the classification of vegetation communities in desert grasslands, which is helpful for the management and restoration of desert steppes.

Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images.

PURPOSE: To develop and validate a deep transfer learning (DTL) algorithm for detecting abnormalities in fundus images from non-mydriatic fundus photography examinations. METHODS: A total of 1295 fundus images were collected to develop and validate a DTL algorithm for detecting abnormal fundus images. After removing 366 poor images, the DTL model was developed using 929 (370 normal and 559 abnormal) fundus images. Data preprocessing was performed to normalize the images. The inception-ResNet-v2 architecture was applied to achieve transfer learning. We tested our model using a subset of the publicly available Messidor dataset (using 366 images) and evaluated the testing performance of the DTL model for detecting abnormal fundus images. RESULTS: In the internal validation dataset (n = 273 images), the area under the curve (AUC), sensitivity, accuracy, and specificity of DTL for correctly classified fundus images were 0.997%, 97.41%, 97.07%, and 96.82%, respectively. For the test dataset (n = 273 images), the AUC, sensitivity, accuracy, and specificity of the DTL for correctly classifying fundus images were 0.926%, 88.17%, 87.18%, and 86.67%, respectively. CONCLUSION: DTL showed high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of DTL in community health-care centers.

Selective solid-phase extraction and analysis of trace-level Cr(III), Fe(III), Pb(II), and Mn(II) Ions in wastewater using diethylenetriamine-functionalized carbon nanotubes dispersed in graphene oxide colloids.

Multi-walled carbon nanotubes (MCNTs) were dispersed in graphene oxide (GO) colloids to be further functionalized with diethylenetriamine (DETA), resulting in GO-MCNTs-DETA nanocomposites for the solid-phase extraction and analysis of Cr(III), Fe(III), Pb(II), and Mn(II) ions at the trace levels in wastewater. Inductively coupled plasma optical emission spectrometry (ICP-OES) indicates that this new solid-phase sorbent could facilitate the maximum static adsorption capacities of 5.4, 13.8, 6.6 and 9.5 mg g(-1) for Cr(III), Fe(III), Pb(II), and Mn(II) ions, respectively, showing the adsorption capacity up to 95% within about 30 min. Moreover, the detection limits of the GO-MCNTs-DETA-based analysis method were found to be 0.16, 0.50, 0.24 and 0.38 ng mL(-1) for Cr(III), Fe(III), Pb(II), and Mn(II) ions, respectively, with the relative standard deviation of lower than 3.0% (n=5). Importantly, common coexisting ions showed no significant interference on the separation and pre-concentration of these heavy metal ions at pH 4.0. Subsequently, the GO-MCNTs-DETA sorbent was successfully employed for the separation and analysis of trace-level Cr(III), Fe(III), Pb(II), and Mn(II) ions in wastewater samples yielding 75-folds concentration factors.

High-throughput colorimetric assays for mercury(II) in blood and wastewater based on the mercury-stimulated catalytic activity of small silver nanoparticles in a temperature-switchable gelatin matrix.

A catalysis-based, label-free, and high-throughput colorimetric protocol has been initially proposed for detecting mercury(II) in blood and wastewater with 96-cell plates, based on the mercury-enhanced catalytic activity of small silver nanoparticles synthesized in a gelatin matrix with unique temperature switchable sol-gel transition.

Chemically-modified activated carbon with ethylenediamine for selective solid-phase extraction and preconcentration of metal ions.

A new method that utilizes ethylenediamine-modified activated carbon (AC-EDA) as a solid-phase extractant has been developed for simultaneous preconcentration of trace Cr(III), Fe(III), Hg(II) and Pb(II) prior to the measurement by inductively coupled plasma optical emission spectrometry (ICP-OES). The new sorbent was prepared by oxidative surface modification. Experimental conditions for effective adsorption of trace levels of Cr(III), Fe(III), Hg(II) and Pb(II) were optimized with respect to different experimental parameters using batch and column procedures in detail. The optimum pH value for the separation of metal ions simultaneously on the new sorbent was 4.0. Complete elution of absorbed metal ions from the sorbent surface was carried out using 3.0 mL of 2% (%w/w) thiourea and 0.5 mol L(-1) HCl solution. Common coexisting ions did not interfere with the separation and determination of target metal ions. The maximum static adsorption capacity of the sorbent at optimum conditions was found to be 39.4, 28.9, 60.5 and 49.9 mg g(-1) for Cr(III), Fe(III), Hg(II) and Pb(II), respectively. The time for 94% adsorption of target metal ions was less than 2 min. The detection limits of the method was found to be 0.28, 0.22, 0.09 and 0.17 ng mL(-1) for Cr(III), Fe(III), Hg(II) and Pb(II), respectively. The precision (R.S.D.) of the method was lower 4.0% (n=8). The prepared sorbent as solid-phase extractant was successfully applied for the preconcentration of trace Cr(III), Fe(III), Hg(II) and Pb(II) in natural and certified samples with satisfactory results.