Ultra-high pressure improved gelation and digestive properties of Tai Lake whitebait myofibrillar protein.

This study investigated the effects of ultra-high pressure (UHP) at different levels on the physicochemical properties, gelling properties, and in vitro digestion characteristics of myofibrillar protein (MP) in Tai Lake whitebait. The α-helix gradually unfolded and transformed into ß-sheet as the pressure increased from 0 to 400 MPa. In addition, the elastic modulus (G') and viscous modulus (G'') of the 400 MPa-treated MP samples increased by 4.8 and 3.8 times, respectively, compared with the control group. The gel properties of the MP also increased significantly after UHP treatment, e.g., the gel strength increased by a 4.8-fold when the pressure reached 400 Mpa, compared with the control group. The results of in vitro simulated digestion showed that the 400 MPa-treated MP gel samples showed a 1.8-fold increase in digestibility and a 69.6 % decrease in digestible particle size compared with the control group.

A novel preoperative image-guided localization for small pulmonary nodule resection using a claw-suture device.

Video-assisted thoracoscopic surgery (VATS) provides better option concerning pathological diagnosis and curative intention of small pulmonary nodules (SPNs) that are sometimes challenging to localize. We assess the safety and feasibility of a new localization technique for SPNs, and report experience accumulated over time. A retrospective review of the new claw-suture localization cases between February 2018 and May 2023 was performed. Nodules were localized by a novel system that has an anchor claw and a tri-colored suture, guided by computed tomography (CT). Localization and operative procedure outcomes were then assessed. A total of 590 SPNs were localized from 568 patients before operation. The median nodule size was 0.70 cm (range, 0.3-2.0 cm). The claw-suture localization was successful without dislodgment or device fracture in 574 of 590 lesions (97.3%). Failures included not meeting target distance between claw and lesion (n = 13 [2.2%]), and device displacement (n = 3 [0.5%]). Complications requiring no further medical intervention included asymptomatic pneumothorax (n = 68 [11.5%]), parenchymal hemorrhage (n = 51 [8.6%]), and hemothorax (n = 1 [0.2%]) with the exception of pleural reaction observed in 2 cases (0.3%). Additionally, the depth of pulmonary nodules was significantly associated with the occurrence of pneumothorax (P = 0.036) and parenchymal hemorrhage (P = 0.000). The median duration of the localization was 12 min (range, 7-25 min). No patient complained of remarkable pain during the entire procedure. Retrieve of device after operation was 100%. The new localization technique is a safe, feasible, and well-tolerated method to localize SPNs for VATS resection.

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.

BACKGROUND AND PURPOSE: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. METHODS: A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models. RESULTS: The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients. CONCLUSION: The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). METHODS: A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA). RESULTS: In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. CONCLUSIONS: The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. CLINICAL RELEVANCE STATEMENT: This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. KEY POINTS: • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

A robust and high-precision edge segmentation and refinement method for high-resolution images.

Limited by GPU memory, high-resolution image segmentation is a highly challenging task. To improve the accuracy of high-resolution segmentation, High-Resolution Refine Net (HRRNet) is proposed. The network is divided into a rough segmentation module and a refinement module. The former improves DeepLabV3+ by adding the shallow features of 1/2 original image size and the corresponding skip connection to obtain better rough segmentation results, the output of which is used as the input of the latter. In the refinement module, first, the global context information of the input image is obtained by a global process. Second, the high-resolution image is divided into patches, and each patch is processed separately to obtain local details in a local process. In both processes, multiple refine units (RU) are cascaded for refinement processing, and two cascaded residual convolutional units (RCU) are added to the different output paths of RU to improve the mIoU and the convergence speed of the network. Finally, according to the context information of the global process, the refined patches are pieced to obtain the refined segmentation result of the whole high-resolution image. In addition, the regional non-maximum suppression is introduced to improve the Sobel edge detection, and the Pascal VOC 2012 dataset is enhanced, which improves the segmentation accuracy and robust performance of the network. Compared with the state-of-the-art semantic segmentation models, the experimental results show that our model achieves the best performance in high-resolution image segmentation.

VyUSPA3, a universal stress protein from the Chinese wild grape Vitis yeshanensis, confers drought tolerance to transgenic V. vinifera.

KEY MESSAGE: VyUSPA3 from the Chinese wild grape Vitis yeshanensis interacts with ERF105, PUB24 and NF-YB3, and overexpression of the VyUSPA3 gene in V. vinifera cv. 'Thompson Seedless' confers drought tolerance. Drought is a major abiotic stress factor that seriously affects the growth and yield of grapevine. Although many drought-related genes have been identified in Arabidopsis and other plants, the functions of only a few of their counterparts have been revealed in grape. Here, a universal stress protein (USP) A from the Chinese wild grape Vitis yeshanensis, VyUSPA3, was identified and its function was subsequently characterized by overexpressing or silencing the VyUSPA3 gene in V. vinifera cv. 'Thompson Seedless' via Agrobacterium-mediated genetic transformation. After 21 d of the drought treatment, most leaves of the untransformed (UT) 'Thompson Seedless' lines wilted, yet UT lines were less damaged compared to the RNAi-VyUSPA3 lines, nonetheless, the OE-VyUSPA3 lines were mostly unaffected. Meanwhile, OE-VyUSPA3 lines showed smaller stomatal aperture, more developed roots, higher leaf relative water content, proline content, and antioxidant enzyme activities, as well as lower malondialdehyde, H2O2 and O2•- accumulation than UT lines, but this response pattern was reversed in the RNAi-VyUSPA3 lines. Besides, the transcript levels of four drought-related genes (RD22, RD29B, DREB2A, and NCED1) in OE-VyUSPA3 lines were greater than those in the RNAi-VyUSPA3 and UT lines. In addition, a yeast two-hybrid assay and a bimolecular fluorescence complementation assay confirmed that VyUSPA3 interacted with ERF105, PUB24, and NF-YB3, respectively. This study revealed that VyUSPA3 improved drought tolerance in transgenic grapevines possibly through interaction with the hormone signaling, ubiquitination system, ethylene-responsive element binding factor and nuclear factors.

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image.

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.

Microbial succession and its correlation with the dynamics of volatile compounds involved in fermented minced peppers.

Fermented minced peppers are a traditional fermented food that has a unique flavor due to various microbial communities involved in fermentation. Understanding the changes in microbial communities and volatile components of fermented minced peppers is particularly important to unveil the formation of unique flavor of fermented peppers. In this study, the microbial communities and volatile compounds in fermented minced pepper was analyzed by high-throughput sequencing and GC-MS, as well as their underlying correlations were also established. Results indicated that 17 genera were identified as dominant microorganisms in the fermentation of minced pepper, accompanied by the detection of 64 volatile compounds. Further hierarchical clustering analysis (HCA) displayed that dynamic change of volatile metabolites were involved in the fermentation process, where alkane volatile components were mainly generated in the early stage (3-5 days), and alcohols volatile components were in the middle stage (7-17 days), while ester volatile components were mainly produced in both the early stage (3-5 days) and last stage (17-20 days). Bidirectional orthogonal partial least squares (O2PLS) analysis revealed that 11 genera were core functional microorganisms of fermented minced pepper. Cladosporium and Hansenpora were significantly correlated with the formation of 9 and 6 volatiles, respectively. These findings provide new insights into aroma profile variation of fermented minced peppers and underlying mechanism of characteristic aroma formation during fermentation.

Oridonin attenuates lung inflammation and fibrosis in silicosis via covalent targeting iNOS.

Silicosis, the most common type of pneumoconiosis, exhibits a high incidence in workers who are chronically exposed to crystalline silica (CS). No specific remedy for cure as yet. The terpenoid oridonin exerts multiple modulatory functions in neoplasms and inflammations as a natural compound. In this study, we explored the effect of oridonin on silicosis and revealed the underlying molecular mechanism. An experimental silicosis mouse model was established to evaluate the effects of oridonin on pneumonia and pulmonary fibrosis. In addition, the impact of oridonin on alveolar macrophages (AMs) was examined in the MH-S cell line. Its molecular target, inducible nitric oxide synthase (iNOS), was identified by chemobiological means, and virus-mediated gene overexpression systems confirmed that oridonin directly restrained iNOS protein levels. Oridonin alleviated pneumonia and pulmonary fibrosis in silicosis mice with no obvious systemic toxicity. These effects were partially related to oridonin inhibition of CS-induced AMs injury and inflammation. Furthermore, oridonin suppressed iNOS enzymatic expression and activity by covalently binding to the Thr109 residue of the iNOS target. Thus, our results indicate oridonin as a potential iNOS enzymatic suppressor in experimental silicosis that attenuates pneumonia and pulmonary fibrosis progression, which provides a therapeutic avenue for silicosis prevention and treatment.

Facilitating charge transfer through an atomic coherent interface of a novel direct Z-scheme BiVO4@Cu3SnS4 heterojunction to boost photocatalytic performance.

Direct Z-scheme photocatalytic systems are very promising composite photocatalysts, and their photocatalytic performance is highly associated with the quality of the interface within them. Herein, a novel direct Z-scheme heterojunction with a coherent interface has been presented for the first time. Specifically, the heterojunction was constructed by dispersing pre-prepared BiVO4 crystals into the reaction system to synthesize Cu3SnS4, followed by a hydrothermal reaction. It is shown that Cu3SnS4 was deposited on the surface of each pre-prepared BiVO4 crystal as a thin layer via heterogeneous nucleation to acquire a core-shell heterojunction. The BiVO4@Cu3SnS4 heterojunction was found to possess an atomic coherent interface, which is formed through the bonding between the (121) plane of BiVO4 and the (112) plane of Cu3SnS4, originating from the matching in the crystalline lattice between the two planes. The coherent interface facilitated the charge transfer from Cu3SnS4 to BiVO4 owing to the difference in their Fermi levels, thereby forming a built-in electric field pointing from Cu3SnS4 to BiVO4. Reduced fluorescence emission and a shortened carrier lifetime reveal an obvious reduction in the inter-band charge recombination for the optimal BVO@CTS-0.19 sample. Consequently, BVO@CTS-0.19 shows remarkably enhanced photocatalytic performance in MO degradation, Cr6+ reduction and oxygen evolution. The Z-scheme charge transfer mechanism for BVO@CTS-0.19 was verified by a suite of techniques. This work provides a universal strategy for building a coherent interface to develop high-performance direct Z-scheme heterojunctions.

From pure water to hydrogen peroxide on a novel 2,5,8-triamino-tri-s-triazine (melem)-derived photocatalyst with a high apparent quantum efficiency.

Photocatalytic hydrogen peroxide (H2O2) production is a green process but remains a great challenge. Herein, a novel photocatalyst with high activity for H2O2 production, is developed based on 2,5,8-triamino-tri-s-triazine (melem) by linking it with 2, 3-naphthalene dicarboxylic anhydride (NDA). The obtained melem/NDA hybrid not only exhibited narrowed band gap and obviously enhanced visible light absorption, but also showed reduced charge recombination originated from its spatial distribution in HOMO and LUMO induced by the introduction of NDA as verified by DFT calculations. More significantly, the sufficient LUMO and HOMO positions for the optimal sample, melem/NDA0.5, ensured efficient H2O2 production from pure water via both the oxygen reduction reactions mainly through the two-step one-electron path and the water oxidation reaction through the one-step two-electron path. Consequently, melem/NDA0.5 achieves an apparent quantum efficiency of as high as 6.9 % at 420 nm. This work sheds light on developing high-performance organic photocatalysts for boosting photocatalytic H2O2 production.

The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study.

PURPOSE: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.

A novel bacteriocin isolated from Lactobacillus plantarum W3-2 and its biological characteristics.

In this study, screening bacteriocin-producing strains from 2,000 plant-derived strains by agar well diffusion method was conducted. The corresponding produced bacteriocin was purified and identified by Sephadex gel chromatography, reversed-phase high-performance liquid chromatography (RP-HPLC), and liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Meanwhile, the biological characteristics of bacteriocin were investigated. The targeted strain W3-2 was obtained and identified as Lactobacillus plantarum by morphological observation and 16S rRNA gene sequence analysis. Correspondingly, a novel bacteriocin (named plantaricin W3-2) produced by L. plantarum W3-2 with a molecular weight of 618.26 Da, and an amino acid sequence of AVEEE was separated, purified by Sephadex gel chromatography and RP-HPLC, and identified by LC-MS/MS. Further characteristics analysis displayed that plantaricin W3-2 had good thermal, pH stability, and broad-spectrum antimicrobial ability. In conclusion, plantaricin W3-2 can be used as a new food preservative.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

Expression of the pyrroline-5-carboxylate reductase (P5CR) gene from the wild grapevine Vitis yeshanensis promotes drought resistance in transgenic Arabidopsis.

Proline accumulation is one of the most common reactions in plants under drought stress. Pyrroline-5-carboxylate reductase (P5CR) is the final enzyme and plays an important role in proline biosynthesis. The Chinese wild grapevine Vitis yeshanensis J.X. Chen accession 'Yanshan-1' is highly resistant to drought, but the genetic and molecular mechanisms associated with this resistance have not been elucidated. Here, we cloned a VyP5CR gene (Genbank ID: MZ226960) from 'Yanshan-1', and evaluated its transcriptional response to drought, NaCl, cold, as well as exogenous ABA, MeJA and SA. Tissue specific analysis showed that VyP5CR could be expressed in various organs and was highly expressed in roots. To gain insight into the roles of VyP5CR, we overexpressed VyP5CR in Arabidopsis thaliana (Arabidopsis). Transgenic Arabidopsis plants expressing VyP5CR showed enhanced survival rate, smaller stomata in response to severe drought, as well as stronger root growth on a medium containing mannitol. Under drought stress, VyP5CR-OE plants showed reduced levels of MDA, H2O2 and O2-, and higher proline content, SOD and POD activity. In addition, VyP5CR-OE plants showed increased induction of the drought-related genes COR15A, COR47, DREB2A, KIN1, NCED3 and RD29A. Taken together, these experiments reveal that VyP5CR can promote the drought tolerance of transgenic Arabidopsis. Besides, an interacting protein with VyP5CR, VyCSN5B (COP9 signalosome complex subunit 5b), was screened out by yeast two-hybrid and verified by bimolecular fluorescence complementation assay.

Genome-wide analysis of the Universal stress protein A gene family in Vitis and expression in response to abiotic stress.

Universal Stress Protein A (USPA) plays critical roles in the regulation of growth, development and response to abiotic stress in plants. To date, most research related to the role of USPA in plants has been carried out in herbaceous models such as Arabidopsis, rice and soybean. Here, we used bioinformatics approaches to identify 21 USPA genes in the genome of Vitis vinifera L. Phylogenetic analysis revealed that VvUSPAs could be divided into eight clades. Based on predicted chromosomal locations, we identified 16 pairs of syntenic, orthologous genes between A. thaliana and V. vinifera. Further promoter cis-elements analysis, together with identification of potential microRNA (miRNA) binding sites, suggested that at least some of the VvUSPAs participate in response to phytohormones and abiotic stress. To add support for this, we analyzed the developmental and stress-responsive expression patterns of the homologous USPA genes in the drought-resistant wild Vitis yeshanensis accession 'Yanshan-1' and the drought-sensitive Vitis riparia accession 'He'an'. Most of the USPA genes were upregulated in different degrees in the two genotypes after drought stress and exposure to ethephon (ETH), abscisic acid (ABA) and methyl jasmonate (MeJA). Individual USPA genes showed various tissue-specific expression patterns. Heterologous expression of five selected genes (VvUSPA2, VvUSPA3, VvUSPA11, VvUSPA13 and VvUSPA16) in Escherichia coli (E. coli) enhanced resistance to drought stress. Our study provides a model for mapping gene function in response to abiotic stress and identified three candidate genes, VvUSPA3, VvUSPA11 and VvUSPA16, as regulators of drought response in V. vinifera.

Strategic Structure Tuning of Yolk-Shell Microcages for Efficient Nitrogen Fixation.

The electrocatalytic nitrogen reduction reaction (ENRR) under ambient conditions is considered as a promising process to produce ammonia. Towards highly efficient catalysts, here an optimized one-step pyrolysis strategy was tailored to design yolk-shell microcages (YS Co@C/BLCNTs), consisting of Co nanocrystals encapsulated in N-doped carbon framework and bridged by bamboo-like carbon nanotubes (BLCNTs). The cavity created between yolk and shell not only served as a "micro-bag" to store the reactant N2 and enhance its dissolution, but also induced a "cage effect" to confine the diffusion of reaction intermediate, hence making the reaction proceed in the direction of producing NH3 . This catalyst displayed excellent catalytic activities for ENRR: a high NH3 yield of 12.87 µg mgcat -1 h-1 at a high faradaic efficiency of 20.7 % at -0.45 V (vs. reversible hydrogen electrode, RHE). After 5 cycles of consecutive ENRR process, the NH3 yield rate was 11.29 µg mgcat -1 h-1 , indicating the excellent electrocatalytic stability. These results provide a structural engineering for ENRR catalyst with doped N, cooperating with non-precious metal to activate the inert triple bond of N2 and achieve NH3 fixation.

Robust Strategy of Quasi-Solid-State Electrolytes to Boost the Stability and Compatibility of Mg Ion Batteries.

Magnesium ion batteries (MIBs) have attracted a lot of attention because of the natural abundance of magnesium, high volumetric energy density, and superior safety. Nevertheless, MIBs are still in their infancy because of the significant challenge in developing a suitable electrolyte with low flammability, high ionic conductivity, and compatibility with the Mg anode. Herein, we construct rechargeable quasi-solid-state MIBs based on tailored polymer electrolytes. The quasi-solid state electrolyte of poly(vinylidene fluoride-co-hexafluoropropylene)-nanosized SiO2-Mg(TFSI)2 combines the outstanding dynamic property of a liquid electrolyte and the good stability of a solid-state electrolyte. It exhibits a highly reversible Mg2+ deposition-dissolution capability, high ion conductivity (0.83 mS cm-1), and superior compatibility with the Mg metal and cathode. The quasi-solid-state MIBs with a layered titanic oxide cathode show a high reversible capacity of 129 mA h g-1 at 50 mA g-1 (150 W h kg-1) without any decay after 100 cycles.

Layered H0.68Ti1.83O4/reduced graphene oxide nanosheets as a novel cathode for rechargeable magnesium batteries.

Rechargeable magnesium batteries (RMBs) are promising devices for energy storage owing to their high volumetric energy density and high safety, but they still suffer from the lack of suitable cathodes. Herein, we report the development of ultrathin hydrated H0.68Ti1.83O4/reduced graphene oxide nanosheet composite (HTO/rGO) as a novel cathode for RMBs. The HTO/rGO, strongly combining ultrathin HTO nanosheets (5 nm in thickness with large interlayer spacing of 0.93 nm) and high electronic conductivity of rGO performs quick Mg2+ kinetics with high reversibility and considerable capacity of 182 mA h g-1, undergo an intercalation type reaction.