Designing electrolytes and interphases for high-energy lithium batteries.

High-energy and stable lithium-ion batteries are desired for next-generation electric devices and vehicles. To achieve their development, the formation of stable interfaces on high-capacity anodes and high-voltage cathodes is crucial. However, such interphases in certain commercialized Li-ion batteries are not stable. Due to internal stresses during operation, cracks are formed in the interphase and electrodes; the presence of such cracks allows for the formation of Li dendrites and new interphases, resulting in a decay of the energy capacity. In this Review, we highlight electrolyte design strategies to form LiF-rich interphases in different battery systems. In aqueous electrolytes, the hydrophobic LiF can extend the electrochemical stability window of aqueous electrolytes. In organic liquid electrolytes, the highly lithiophobic LiF can suppress Li dendrite formation and growth. Electrolyte design aimed at forming LiF-rich interphases has substantially advanced high-energy aqueous and non-aqueous Li-ion batteries. The electrolyte and interphase design principles discussed here are also applicable to solid-state batteries, as a strategy to achieve long cycle life under low stack pressure, as well as to construct other metal batteries.

Interface design for all-solid-state lithium batteries.

The operation of high-energy all-solid-state lithium-metal batteries at low stack pressure is challenging owing to the Li dendrite growth at the Li anodes and the high interfacial resistance at the cathodes1-4. Here we design a Mg16Bi84 interlayer at the Li/Li6PS5Cl interface to suppress the Li dendrite growth, and a F-rich interlayer on LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes to reduce the interfacial resistance. During Li plating-stripping cycles, Mg migrates from the Mg16Bi84 interlayer to the Li anode converting Mg16Bi84 into a multifunctional LiMgSx-Li3Bi-LiMg structure with the layers functioning as a solid electrolyte interphase, a porous Li3Bi sublayer and a solid binder (welding porous Li3Bi onto the Li anode), respectively. The Li3Bi sublayer with its high ionic/electronic conductivity ratio allows Li to deposit only on the Li anode surface and grow into the porous Li3Bi sublayer, which ameliorates pressure (stress) changes. The NMC811 with the F-rich interlayer converts into F-doped NMC811 cathodes owing to the electrochemical migration of the F anion into the NMC811 at a high potential of 4.3 V stabilizing the cathodes. The anode and cathode interlayer designs enable the NMC811/Li6PS5Cl/Li cell to achieve a capacity of 7.2 mAh cm-2 at 2.55 mA cm-2, and the LiNiO2/Li6PS5Cl/Li cell to achieve a capacity of 11.1 mAh cm-2 with a cell-level energy density of 310 Wh kg-1 at a low stack pressure of 2.5 MPa. The Mg16Bi84 anode interlayer and F-rich cathode interlayer provide a general solution for all-solid-state lithium-metal batteries to achieve high energy and fast charging capability at low stack pressure.

Electrolyte design for Li-ion batteries under extreme operating conditions.

The ideal electrolyte for the widely used LiNi0.8Mn0.1Co0.1O2 (NMC811)||graphite lithium-ion batteries is expected to have the capability of supporting higher voltages (≥4.5 volts), fast charging (≤15 minutes), charging/discharging over a wide temperature range (±60 degrees Celsius) without lithium plating, and non-flammability1-4. No existing electrolyte simultaneously meets all these requirements and electrolyte design is hindered by the absence of an effective guiding principle that addresses the relationships between battery performance, solvation structure and solid-electrolyte-interphase chemistry5. Here we report and validate an electrolyte design strategy based on a group of soft solvents that strikes a balance between weak Li+-solvent interactions, sufficient salt dissociation and desired electrochemistry to fulfil all the aforementioned requirements. Remarkably, the 4.5-volt NMC811||graphite coin cells with areal capacities of more than 2.5 milliampere hours per square centimetre retain 75 per cent (54 per cent) of their room-temperature capacity when these cells are charged and discharged at -50 degrees Celsius (-60 degrees Celsius) at a C rate of 0.1C, and the NMC811||graphite pouch cells with lean electrolyte (2.5 grams per ampere hour) achieve stable cycling with an average Coulombic efficiency of more than 99.9 per cent at -30 degrees Celsius. The comprehensive analysis further reveals an impedance matching between the NMC811 cathode and the graphite anode owing to the formation of similar lithium-fluoride-rich interphases, thus effectively avoiding lithium plating at low temperatures. This electrolyte design principle can be extended to other alkali-metal-ion batteries operating under extreme conditions.

THE REPORTING QUALITY OF NONINFERIORITY TRIALS USING INTRAVITREAL VASCULAR ENDOTHELIAL GROWTH FACTOR INHIBITORS.

PURPOSE: Noninferiority trials (NIFTs) are widely used to study intravitreal vascular endothelial growth factor inhibitors for the treatment of ocular diseases. Thus, this trial design deserves greater attention. We aimed to comprehensively assess the methodological and reporting quality of NIFTs in the field of neovascular ocular diseases. METHODS: We identified NIFTs using antivascular endothelial growth factor agents published before February 2020 from PubMed and Web of Science. Two independent authors extracted and double-checked predefined elements related to the quality of design and reporting. The characteristics and reporting of NIFTs were described with frequencies and percentages. We summarized important factors that were potentially biased the results of NIFTs and provided point-to-point recommendations. RESULTS: In total, 34 studies involving 15,190 subjects and 51 pairs of noninferiority comparisons were identified. Areas of concern that could potentially affect the qualities of NIFTs included the absence of justification for the selection of noninferiority margins (61.8%), the use of unusually wide noninferiority margins (26.5%), the lack of outcome confirmation provided by the intention-to-treat and per-protocol analyses (64.7%), the presence of postrandomization exclusions >10% (52.9%), and not declaring the compensatory benefits (35.3%). Moreover, industry-sponsored NIFTs were more likely to draw positive results (P = 0.036). CONCLUSION: NIFTs of antivascular endothelial growth factor therapies commonly achieved noninferiority of the tested intervention. However, the methodologies and reporting limitations may affect the confidence of the results. Thus, more awareness must be created among investigators for better adherence to guidelines and recommendations while designing, conducting, and reporting on NIFTs.

Sex-related differences in spontaneous intracerebral hemorrhage outcomes: A prognostic study based on 111,112 medical records.

Objective: To identify sex-related differences in the outcome of hospitalized patients with spontaneous intracerebral hemorrhage (SICH), and to identify potential causal pathways between sex and SICH outcome. Methods: A total of 111,112 medical records of in-hospital patients with SICH were collected. Data- and expert-driven techniques were applied, such as a multivariate logistic regression model and causal mediation analysis. These analyses were used to determine the confounders and mediators, estimate the true effect of sex on the SICH outcome, and estimate the average causal mediation effect for each mediator. Results: (1) Failure (disability or death) rates in women with SICH were significantly lower than in men with SICH. On the day of discharge, the odds ratio (OR) of failure between women and men was 0.9137 [95% confidence interval (CI), 0.8879-0.9402], while the odds ratio at 90 days post-discharge was 0.9353 (95% confidence interval, 0.9121-0.9591). (2) The sex-related difference in SICH outcome decreased with increasing age and disappeared after 75 years. (3) Deep coma, brainstem hemorrhage, and an infratentorial hemorrhage volume of >10 ml accounted for 62.76% (p < 0.001), 33.46% (p < 0.001), and 11.56% (p < 0.001) of the overall effect on the day of discharge, and for 52.28% (p < 0.001), 27.65% (p < 0.001), and 10.86% (p < 0.001) of the overall effect at the 90-day post-discharge. Conclusion: Men have a higher failure risk than women, which may be partially mediated by a higher risk for deep coma, brainstem hemorrhage, and an infratentorial hemorrhage volume of >10 ml. Future work should explore the biological mechanisms underlying this difference.

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling. METHODS: This study used a joint modeling strategy consisting of: outlier detection and removal, data balancing, model fitting and prediction, performance evaluation. We collected medical record data for all ICH patients with admissions in 2017-2019 from Sichuan Province. Clinical and radiological variables were used to construct models to predict mortality outcomes 90 days after discharge. We used stacking ensemble learning to combine logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) models. Accuracy, sensitivity, specificity, AUC, precision, and F1 score were used to evaluate model performance. Finally, we compared all 84 combinations of the joint modeling strategy, including training set with and without cross-validated committees filter (CVCF), five resampling techniques (random under-sampling (RUS), random over-sampling (ROS), adaptive synthetic sampling (ADASYN), Borderline synthetic minority oversampling technique (Borderline SMOTE), synthetic minority oversampling technique and edited nearest neighbor (SMOTEENN)) and no resampling, seven models (LR, RF, ANN, SVM, KNN, Stacking, AdaBoost). RESULTS: Among 4207 patients with ICH, 2909 (69.15%) survived 90 days after discharge, and 1298 (30.85%) died within 90 days after discharge. The performance of all models improved with removing outliers by CVCF except sensitivity. For data balancing processing, the performance of training set without resampling was better than that of training set with resampling in terms of accuracy, specificity, and precision. And the AUC of ROS was the best. For seven models, the average accuracy, specificity, AUC, and precision of RF were the highest. Stacking performed best in F1 score. Among all 84 combinations of joint modeling strategy, eight combinations performed best in terms of accuracy (0.816). For sensitivity, the best performance was SMOTEENN + Stacking (0.662). For specificity, the best performance was CVCF + KNN (0.987). Stacking and AdaBoost had the best performances in AUC (0.756) and F1 score (0.602), respectively. For precision, the best performance was CVCF + SVM (0.938). CONCLUSION: This study proposed a joint modeling strategy including outlier detection and removal, data balancing, model fitting and prediction, performance evaluation, in order to provide a reference for physicians and researchers who want to build their own models. This study illustrated the importance of outlier detection and removal for machine learning and showed that ensemble learning might be a good modeling strategy. Due to the low imbalanced ratio (IR, the ratio of majority class and minority class) in this study, we did not find any improvement in models with resampling in terms of accuracy, specificity, and precision, while ROS performed best on AUC.

Salt-in-Salt Reinforced Carbonate Electrolyte for Li Metal Batteries.

The instability of carbonate electrolyte with metallic Li greatly limits its application in high-voltage Li metal batteries. Here, a "salt-in-salt" strategy is applied to boost the LiNO3 solubility in the carbonate electrolyte with Mg(TFSI)2 carrier, which enables the inorganic-rich solid electrolyte interphase (SEI) for excellent Li metal anode performance and also maintains the cathode stability. In the designed electrolyte, both NO3 - and PF6 - anions participate in the Li+ -solvent complexes, thus promoting the formation of inorganic-rich SEI. Our designed electrolyte has achieved a superior Li CE of 99.7 %, enabling the high-loading NCM811||Li (4.5 mAh cm-2 ) full cell with N/P ratio of 1.92 to achieve 84.6 % capacity retention after 200 cycles. The enhancement of LiNO3 solubility by divalent salts is universal, which will also inspire the electrolyte design for other metal batteries.

Enhancing Li+ Transport in NMC811||Graphite Lithium-Ion Batteries at Low Temperatures by Using Low-Polarity-Solvent Electrolytes.

LiNix Coy Mnz O2 (x+y+z=1)||graphite lithium-ion battery (LIB) chemistry promises practical applications. However, its low-temperature (≤ -20 °C) performance is poor because the increased resistance encountered by Li+ transport in and across the bulk electrolytes and the electrolyte/electrode interphases induces capacity loss and battery failures. Though tremendous efforts have been made, there is still no effective way to reduce the charge transfer resistance (Rct ) which dominates low-temperature LIBs performance. Herein, we propose a strategy of using low-polarity-solvent electrolytes which have weak interactions between the solvents and the Li+ to reduce Rct , achieving facile Li+ transport at sub-zero temperatures. The exemplary electrolyte enables LiNi0.8 Mn0.1 Co0.1 O2 ||graphite cells to deliver a capacity of ≈113 mAh g-1 (98 % full-cell capacity) at 25 °C and to remain 82 % of their room-temperature capacity at -20 °C without lithium plating at 1/3C. They also retain 84 % of their capacity at -30 °C and 78 % of their capacity at -40 °C and show stable cycling at 50 °C.

[Hematoma Segmentation of Spontaneous Intracerebral Hemorrhage Based on Watershed and Region-Growing Algorithm].

Objective: To establish a brain hematoma CT image segmentation method based on watershed and region-growing algorithm so as to measure hematoma volume quickly and accurately, to explore the consistency between the results of this segmentation method and those of manual segmentation, the clinical gold standard, and to compare the results of this method with the calculation of the two Tada formulas commonly used in clinical practice. Methods: The preoperative CT images of 152 patients who were treated for spontaneous cerebral hemorrhage at the Department of Neurosurgery, West China Hospital, Sichuan University between January 2018 and June 2019 were retrospectively collected. The CT images were randomly assigned, by using a random number table, to the training set, the test set and the validation set, which contained 100 patients, 22 patients and 30 patients, respectively. The labeling results of the training set and the test set were used in algorithm training and testing. Four methods, namely, manual segmentation, algorithm segmentation, i.e., segmentation calculation based on watershed and regional growth algorithm, Tada formula, i.e., the traditional Tada formula calculation, and accurate Tada formula, i.e., accurate Tada formula calculation based on 3D-Slicer, were applied on the validation set to measure the hematoma volume. The Digital Imaging and Communications in Medicine (DICOM) data of subjects meeting the selection criteria of the study were manually segmented by two experienced neurosurgeons. The hematoma segmentation model was built based on watershed algorithm and regional growth algorithm. Seed point selected by neurosurgeons was taken as the starting point of growth. Regional grayscale difference criterion combined with manual segmentation validation were adopted to determine the regional growth threshold that met the segmentation precision requirements for intracranial hematoma. Using manual segmentation as the gold standard, Bland-Altman consistency analysis was used to verify the consistency of the three other methods for measuring hematoma volume. Results: With manual segmentation as the gold standard, among the three methods of measuring hematoma volume, algorithm segmentation had the smallest percentage error, the narrowest range of difference, the highest intra-group correlation coefficient (0.987), good consistency, and the narrowest 95% limits of agreement ( LoA). The percentage error of its segmentation was not statistically significant for hematomas of different volumes. Conclusion: The segmentation method of spontaneous intracerebral hemorrhage based on watershed and regional growth algorithm shows stable measurement performance and good consistency with the clinical gold standard, which has considerable clinical significance, but it still needs further validation with more clinical samples.

Applying the Spatial Transmission Network to the Forecast of Infectious Diseases Across Multiple Regions.

Objective: Timely and accurate forecast of infectious diseases is essential for achieving precise prevention and control. A good forecasting method of infectious diseases should have the advantages of interpretability, feasibility, and forecasting performance. Since previous research had illustrated that the spatial transmission network (STN) showed good interpretability and feasibility, this study further explored its forecasting performance for infectious diseases across multiple regions. Meanwhile, this study also showed whether the STN could overcome the challenges of model rationality and practical needs. Methods: The construction of the STN framework involved three major steps: the spatial kluster analysis by tree edge removal (SKATER) algorithm, structure learning by dynamic Bayesian network (DBN), and parameter learning by the vector autoregressive moving average (VARMA) model. Then, we evaluated the forecasting performance of STN by comparing its accuracy with that of the mechanism models like susceptible-exposed-infectious-recovered-susceptible (SEIRS) and machine-learning algorithm like long-short-term memory (LSTM). At the same time, we assessed the robustness of forecasting performance of STN in high and low incidence seasons. The influenza-like illness (ILI) data in the Sichuan Province of China from 2010 to 2017 were used as an example for illustration. Results: The STN model revealed that ILI was likely to spread among multiple cities in Sichuan during the study period. During the whole study period, the forecasting accuracy of the STN (mean absolute percentage error [MAPE] = 31.134) was significantly better than that of the LSTM (MAPE = 41.657) and the SEIRS (MAPE = 62.039). In addition, the forecasting performance of STN was also superior to those of the other two methods in either the high incidence season (MAPE = 24.742) or the low incidence season (MAPE = 26.209), and the superiority was more obvious in the high incidence season. Conclusion: This study applied the STN to the forecast of infectious diseases across multiple regions. The results illustrated that the STN not only had good accuracy in forecasting performance but also indicated the spreading directions of infectious diseases among multiple regions to a certain extent. Therefore, the STN is a promising candidate to improve the surveillance work.

Segmentation of Spontaneous Intracerebral Hemorrhage on CT With a Region Growing Method Based on Watershed Preprocessing.

Intracerebral hemorrhage (ICH) poses a great threat to human life due to its high incidence and poor prognosis. Identification of the bleeding location and quantification of the volume based on CT images are of great significance for assisting the diagnosis and treatment of ICH. In this study, a region-growing algorithm based on watershed preprocessing (RG-WP) was proposed to segment and quantify the hemorrhage. The lowest points yielded by the watershed algorithm were used as seed points for region growing and then hemorrhage was segmented based on the region growing method. At the same time, to integrate the rich experience of clinicians with the algorithm, manual selection of seed points on the basis of watershed segmentation was performed. With the application of segmentation on CT images of 55 patients with ICH, the performance of the RG-WP algorithm was evaluated by comparing it with manual segmentations delineated by professional clinicians as well as the traditional ABC/2 method and the deep learning algorithm U-net. The mean deviation of hemorrhage volume of the RG-WP algorithm from manual segmentation was -0.12 ml (range: -1.05-1.16), while that of the ABC/2 from the manual was 1.05 ml (range: -0.77-9.57). Strong agreement of the algorithm and the manual was confirmed with a high intraclass correlation coefficient (ICC) (0.998, 95% CI: 0.997-0.999), which was superior to that of the ABC/2 and the manual (0.972, 95% CI: 0.953-0.984). The sensitivity (Sen), positive predictive value (PPV), dice similarity index (DSI), and Jaccard index (JI) of the RG-WP algorithm compared to the manual were 0.92 ± 0.04, 0.95 ± 0.04, 0.93 ± 0.02, and 0.88 ± 0.04, respectively, showing high consistency. Besides, the accuracy of the algorithm was also comparable to that of the deep learning method U-net, with Sen, PPV, DSI, and JI being 0.91 ± 0.09, 0.91 ± 0.06, 0.91 ± 0.05, and 0.91 ± 0.06, respectively.

Symptom Clustering Patterns and Population Characteristics of COVID-19 Based on Text Clustering Method.

Background: Descriptions of single clinical symptoms of coronavirus disease 2019 (COVID-19) have been widely reported. However, evidence of symptoms associations was still limited. We sought to explore the potential symptom clustering patterns and high-frequency symptom combinations of COVID-19 to enhance the understanding of people of this disease. Methods: In this retrospective cohort study, a total of 1,067 COVID-19 cases were enrolled. Symptom clustering patterns were first explored by a text clustering method. Then, a multinomial logistic regression was applied to reveal the population characteristics of different symptom groups. In addition, time intervals between symptoms onset and the first visit were analyzed to consider the effect of time interval extension on the progression of symptoms. Results: Based on text clustering, the symptoms were summarized into four groups. Group 1: no-obvious symptoms; Group 2: mainly fever and/or dry cough; Group 3: mainly upper respiratory tract infection symptoms; Group 4: mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms. Apart from Group 1 with no obvious symptoms, the most frequent symptom combinations were fever only (64 cases, 47.8%), followed by dry cough only (42 cases, 31.3%) in Group 2; expectoration only (21 cases, 19.8%), followed by expectoration complicated with fever (10 cases, 9.4%) in Group 3; fatigue complicated with fever (12 cases, 4.2%), followed by headache complicated with fever was also high (11 cases, 3.8%) in Group 4. People aged 45-64 years were more likely to have symptoms of Group 4 than those aged 65 years or older (odds ratio [OR] = 2.66, 95% CI: 1.21-5.85) and at the same time had longer time intervals. Conclusions: Symptoms of COVID-19 could be divided into four clustering groups with different symptom combinations. The Group 4 symptoms (i.e., mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms) happened more frequently in COVID-19 than in influenza. This distinction could help deepen the understanding of this disease. The middle-aged people have a longer time interval for medical visit and was a group that deserve more attention, from the perspective of medical delays.

[Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images].

OBJECTIVE: To examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images. METHODS: A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients' brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network. RESULTS: In the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network. CONCLUSION: The improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.

Interfacial Design for a 4.6 V High-Voltage Single-Crystalline LiCoO2 Cathode.

Single-crystalline cathode materials have attracted intensive interest in offering greater capacity retention than their polycrystalline counterparts by reducing material surfaces and phase boundaries. However, the single-crystalline LiCoO2 suffers severe structural instability and capacity fading when charged to high voltages (4.6 V) due to Co element dissolution and O loss, crack formation, and subsequent electrolyte penetration. Herein, by forming a robust cathode electrolyte interphase (CEI) in an all-fluorinated electrolyte, reversible planar gliding along the (003) plane in a single-crystalline LiCoO2 cathode is protected due to the prevention of element dissolution and electrolyte penetration. The robust CEI effectively controls the performance fading issue of the single-crystalline cathode at a high operating voltage of 4.6 V, providing new insights for improved electrolyte design of high-energy-density battery cathode materials.

Understanding LiI-LiBr Catalyst Activity for Solid State Li2S/S Reactions in an All-Solid-State Lithium Battery.

Li||MoS2 solid-state batteries have higher volumetric energy density and power density than Li||Li2S batteries. However, they suffer from energy and power decay due to the formation of lithium sulfide that has low ionic/electronic conductivity and a strong Li-S bond. Herein, we overcome these challenges by incorporating the catalytic LiI-LiBr compound and carbon black into MoS2. The comprehensive simulations, characterizations, and electrochemical evaluations demonstrated that LiI-LiBr significantly reduces Li+/S2- interaction and increases the ionic conductivity of Li2S, thus enhancing the reaction kinetics and Li2S/S redox reversibility. MoS2@LiI-LiBr@C||Li cells with an areal capacity of 0.87 mAh cm-2 provide a reversible capacity of 816.2 mAh g-1 at 200 mA g-1 and maintain 604.8 mAh g-1 (based on the mass of MoS2) for 100 cycles. At a high areal capacity of 2 mAh cm-2, the battery still delivers reversible capacity of 498 mAh g-1. LiI-LiBr-carbon additive can be broadly applied for all transition-metal sulfide cathodes to enhance the cyclic and rate performance.

An Inorganic-Rich Solid Electrolyte Interphase for Advanced Lithium-Metal Batteries in Carbonate Electrolytes.

In carbonate electrolytes, the organic-inorganic solid electrolyte interphase (SEI) formed on the Li-metal anode surface is strongly bonded to Li and experiences the same volume change as Li, thus it undergoes continuous cracking/reformation during plating/stripping cycles. Here, an inorganic-rich SEI is designed on a Li-metal surface to reduce its bonding energy with Li metal by dissolving 4m concentrated LiNO3 in dimethyl sulfoxide (DMSO) as an additive for a fluoroethylene-carbonate (FEC)-based electrolyte. Due to the aggregate structure of NO3 - ions and their participation in the primary Li+ solvation sheath, abundant Li2 O, Li3 N, and LiNx Oy grains are formed in the resulting SEI, in addition to the uniform LiF distribution from the reduction of PF6 - ions. The weak bonding of the SEI (high interface energy) to Li can effectively promote Li diffusion along the SEI/Li interface and prevent Li dendrite penetration into the SEI. As a result, our designed carbonate electrolyte enables a Li anode to achieve a high Li plating/stripping Coulombic efficiency of 99.55 % (1 mA cm-2 , 1.0 mAh cm-2 ) and the electrolyte also enables a Li||LiNi0.8 Co0.1 Mn0.1 O2 (NMC811) full cell (2.5 mAh cm-2 ) to retain 75 % of its initial capacity after 200 cycles with an outstanding CE of 99.83 %.

Self-Formed Electronic/Ionic Conductive Fe3 S4  @ S @ 0.9Na3 SbS4 ⋠0.1NaI Composite for High-Performance Room-Temperature All-Solid-State Sodium-Sulfur Battery.

Fe3 S4  @ S @ 0.9Na3 SbS4 ⋅0.1NaI composite cathode is prepared through one-step wet-mechanochemical milling procedure. During milling process, ionic conduction pathway is self-formed in the composite due to the formation of 0.9Na3 SbS4 ⋅0.1NaI electrolyte without further annealing treatment. Meanwhile, the introduction of Fe3 S4 can increase the electronic conductivity of the composite cathode by one order of magnitude and nearly double enhance the ionic conductivities. Besides, the aggregation of sulfur is effectively suppressed in the obtained Fe3 S4  @ S @ 0.9Na3 SbS4 ⋅0.1NaI composite, which will enhance the contact between sulfur and 0.9Na3 SbS4 ⋅0.1NaI electrolyte, leading to a decreased interfacial resistance and improving the electrochemical kinetics of sulfur. Therefore, the resultant all-solid-state sodium-sulfur battery employing Fe3 S4  @ S @ 0.9Na3 SbS4 ⋅0.1NaI composite cathode shows discharge capacity of 808.7 mAh g-1 based on Fe3 S4 @S and a normalized discharge capacity of 1040.5 mAh g-1 for element S at 100 mA g-1 for 30 cycles at room temperature. Moreover, the battery also exhibits excellent cycling stability with a reversible capacity of 410 mAh g-1 at 500 mA g-1 for 50 cycles, and superior rate capability with capacities of 952.4, 796.7, 513.7, and 445.6 mAh g-1 at 50, 100, 200, and 500 mA g-1 , respectively. This facile strategy for sulfur-based composite cathode is attractive for achieving room-temperature sodium-sulfur batteries with superior electrochemical performance.

In Situ Coating of Li7P3S11 Electrolyte on CuCo2S4/Graphene Nanocomposite as a High-Performance Cathode for All-Solid-State Lithium Batteries.

A cathode material, CuCo2S4/graphene@10%Li7P3S11, is reported for all-solid-state lithium batteries with high performance. The electrical conductivity of CuCo2S4 is improved by compounding with graphene. Meanwhile, Li7P3S11 electrolyte is coated on the surface of CuCo2S4/graphene nanosheets to build an intimate contact interface between the solid electrolyte and the electrode effectively, facilitating lithium-ion conduction. Benefitting from the balanced and efficient electronic and ionic conductions, all-solid-state lithium batteries using CuCo2S4/graphene@10%Li7P3S11 composite as cathode materials demonstrate superior cycling stability and rate capabilities, exhibiting an initial discharge specific capacity of 1102.25 mAh g-1 at 50 mA g-1 and reversible capacity of 556.41 mAh g-1 at a high current density of 500 mA g-1 after 100 cycles. These results demonstrate that the CuCo2S4/graphene@10%Li7P3S11 nanocomposite is a promising active material for all-solid-state lithium batteries with superior performances.

Passivation of the Cathode-Electrolyte Interface for 5 V-Class All-Solid-State Batteries.

An all-solid-state battery is a potentially superior alternative to a state-of-the-art lithium-ion battery owing to its merits in abuse tolerance, packaging, energy density, and operable temperature ranges. In this work, a 5 V-class spinel LiNi0.5Mn1.5O4 (LNMO) cathode is targeted to combine with a high-ionic-conductivity Li6PS5Cl (LPSCl) solid electrolyte for developing high-performance all-solid-state batteries. Aiming to passivate and stabilize the LNMO-LPSCl interface and suppress the unfavorable side reactions such as the continuous chemical/electrochemical decomposition of the solid electrolyte, oxide materials including LiNbO3, Li3PO4, and Li4Ti5O12 are rationally applied to decorate the surface of pristine LNMO particles with various amounts through a wet-chemistry approach. Electrochemical characterization demonstrates that the composite cathode consisting of 8 wt % LiNbO3-coated LNMO and LPSCl in a weight ratio of 70:30 delivers the best electrochemical performance with an initial discharge capacity of 115 mA h g-1 and a reversible discharge capacity of 80 mA h g-1 at the 20th cycle, suggesting that interfacial passivation is an effective strategy to ensure the operation of 5 V-class all-solid-state batteries.

Sulfur-Embedded FeS2 as a High-Performance Cathode for Room Temperature All-Solid-State Lithium-Sulfur Batteries.

All-solid-state lithium-sulfur batteries employing sulfur electrodes and a solid electrolyte at room temperature are still a great challenge owing to the low conductivities of sulfur cathodes. In this work, we report room temperature all-solid-state lithium-sulfur batteries using thin sulfur layer-embedded FeS2 (FeS2@S) microsphere composites as active materials in the FeS2@S-Li10GeP2S12-Super P cathode electrode. Setting the cut-off voltage between 1.5 and 2.8 V, only lithiation-delithiation reactions between L2FeS2 and FeSy and direct reaction between Li2S and S will occur, which avoids large volume change of FeS2 caused by the conversion reaction, leading to the structure integrity of FeS2@S. The resultant batteries exhibit excellent rate and cyclic performances, delivering specific capacities of 1120.9, 937.2, 639.7, 517.2, 361.5, and 307.0 mA h g-1 for the FeS2@S composite cathode, corresponding to the normalized capacities of 1645.5, 1252.9, 782.5, 700.2, 478.4, and 363.6 mA h g-1 for sulfur at 30, 50, 100, 500, 1000, and 5000 mA g-1, respectively. Besides, they can retain the normalized capacity of 430.7 mA h g-1 for sulfur at 1000 mA g-1 after 200 cycles at room temperature.