A submillimeter bundled microtubular flow battery cell with ultrahigh volumetric power density.

Flow batteries are a promising energy storage solution. However, the footprint and capital cost need further reduction for flow batteries to be commercially viable. The flow cell, where electron exchange takes place, is a central component of flow batteries. Improving the volumetric power density of the flow cell (W/Lcell) can reduce the size and cost of flow batteries. While significant progress has been made on flow battery redox, electrode, and membrane materials to improve energy density and durability, conventional flow batteries based on the planar cell configuration exhibit a large cell size with multiple bulky accessories such as flow distributors, resulting in low volumetric power density. Here, we introduce a submillimeter bundled microtubular (SBMT) flow battery cell configuration that significantly improves volumetric power density by reducing the membrane-to-membrane distance by almost 100 times and eliminating the bulky flow distributors completely. Using zinc-iodide chemistry as a demonstration, our SBMT cell shows peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow battery cells. The battery cycled for more than 220 h corresponding to >2,500 cycles at off-peak conditions. Furthermore, the SBMT cell has been demonstrated to be compatible with zinc-bromide, quinone-bromide, and all-vanadium chemistries. The SBMT flow cell represents a device-level innovation to enhance the volumetric power of flow batteries and potentially reduce the size and cost of the cells and the entire flow battery.

[Analyses on the characteristics and the trends of pneumoconiosis notified between 2001 and 2012 in Hebei Province].

OBJECTIVE: To describe the incidence, development and death of pneumoconiosis reported in Hebei from 2001 to 2012 and investigate the epidemiological trends and characteristics of pneumoconiosis to provide basic data for formulating the guidelines and policies for control of pneumoconiosis. METHODS: The Hebei database of new cases of pneumoconiosis reposed from 2001 to 2012 were subjected to systematic arrangement. Clean data and descriptive analysis using SPSS 17.0. The statistical indices included number of new and death pneumoconiosis cases in each year. RESULTS: From 2001 to 2012 a total of 4558 new cases of pneumoconiosis were reported. The situation was same to coal-workers' pneumoconiosis and silicosis. (2) The pneumoconiosis cases were distributed mainly in the city of Tang Shan, Cheng De, Zhang Jia Kou and Han Dan (88.24%). (3) Most cases were centerred in coal industry, metallurgical industry, nonferrous metals industry, architectural material industry and light industry. (4) The mean age of onset in new cases was shorted each year for silicosis, coal-workers' pneumoconiosis, potter pneumoconiosis and electric welder pneumoconiosis, especially for 2010 to 2012 (9 years). (5) The work types of these cases mainly included drilling (26.72%), mining as the main work (6.67%), hybrid coal mine work (6.95%), molding worker (5.24%) and berterring worker (4.82%). (6) The new cases of pneumoconiosis reposed from 2001 to 2012 were diagnosissed I (3415, 74.92%), II (782, 17.16%), III (361, 7.92%). (7) The death cases of pneumoconiosis reposed from 2001 to 2012 were 1182, most of them were distributed mainly in the city of Tang Shan, Cheng De, Zhang Jia Kou and Han Dan (88.24%). CONCLUSION: The incidence of pneumoconiosis is still high: the new cases of pneumoconiosis is still rising. The mean age of onset in new cases was shorted each year. The new cases of pneumoconiosis reposed from 2001 to 2012 were diagnosed II was above 25%. The prevention and control of pneumoconiosis should be enhanced in key industries and for people engaging in key regions, industries, types of work according to the epidemiological characteristics of pneumoconiosis. Most cases were centerred in coal-workers' pneumoconiosis and silicosis.

[Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model].

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Silicon Nanoparticles Embedded in Chemical-Expanded Graphite through Electrostatic Attraction for High-Performance Lithium-Ion Batteries.

Silicon (Si) is a promising next-generation anode for high-energy-density lithium-ion batteries. The application of silicon/carbon (Si/C) composites with high Si content is hindered by the huge volume change and insecure electrochemical interface of the Si anode. Herein, chemical-expanded graphite (CEG) is used as a carbon matrix to form Si@CEG/C composites with an embedded structure. CEG with an abundant pore structure and electropositivity can well disperse and accommodate a mass of Si nanoparticles (Si NPs). With the flexibility and porosity of CEG, the embedded structure of Si NPs fixed in an expanded graphite layer can adopt the volume change of Si NPs and offer the abundant path of diffusion of lithium-ion, which leads to a moderate cycle and rate performance. Si@CEG/C exhibits a high reversible capacity of 1232.4 mA h g-1 at a current density of 0.5 A g-1 and with a capacity retention rate of 87% after 200 cycles. This embedded structure of Si/C composites built by CEG is meaningful for the structure design of the Si-based anode with higher specific capacity, active material utilization, and satisfactory cycle stability.

Understanding and Controlling the Nucleation and Growth of Zn Electrodeposits for Aqueous Zinc-Ion Batteries.

Aqueous Zn-ion batteries (AZBs) have been proposed as one of the most promising electrical energy-storage systems due to their low cost, high safety, environmental friendliness, and high energy density. However, their application is impeded by the Zn dendrite growth, which may puncture the separator, causing an internal short circuit. Although numerous efforts have been devoted to alleviating dendrite issues by structural design, surface modification, or electrolyte optimization, there are few works focusing on the fundamental research to understand the formation of Zn dendrites, which is critical to address the dendrites issue. In this work, we have systematically investigated the nucleation and growth behaviors of Zn on a stainless steel substrate. We reveal the dependence of Zn growth morphology on cycling conditions (current density and areal capacity) and further elucidate the intricate correlation with cycle life. It is observed that higher current density corresponds to higher nuclei density with a smaller size of zinc deposits and lower areal capacity render smaller zinc flakes, which contributes to the long cycle life of Zn-ion batteries. Based on these findings, a seeding protocol is then proposed to improve the uniformity and compaction of the Zn electrode. The methodology and findings here can potentially be applied to study the nucleation and growth of other metals.

Rational design of walnut-like ZnO/Co3O4 porous nanospheres with substantially enhanced lithium storage performance.

Rational fabrication and smart design of multi-component anode materials to achieve desirable reversible capacities and exceptional cyclability are significant for lithium-ion batteries (LIBs). Herein, walnut-like ZnO/Co3O4 porous nanospheres were prepared by a facile solvothermal method, which were then applied as a mechanically stable anode material for LIBs. The rationally designed hybridized electrode brings favorable structural features, particularly ZnO/Co3O4 porous nanospheres with abundant vacant space and enhanced surface area, enhancing lithium/electron transport and relieving volumetric stresses during the cycling process. Moreover, several in situ hybridized anode materials with electrochemical cooperation further overcome the challenge of capacity decay and conductivity deficiency. The as-obtained ZnO/Co3O4 delivered a much better lithium storage performance compared with ZnO, Co3O4, and their physical mix. We believe that the novel design criteria will bring opportunities in exploration and promote the practical application of transition metal oxides.

Self-Assembly of Silicon@Oxidized Mesocarbon Microbeads Encapsulated in Carbon as Anode Material for Lithium-Ion Batteries.

The utilization of silicon/carbon composites as anode materials to replace the commercial graphite is hampered by their tendency to huge volumetric expansion, costly raw materials, and complex synthesis processes in lithium-ion batteries. Herein, self-assembly method is successfully applied to prepare hierarchical silicon nanoparticles@oxidized mesocarbon microbeads/carbon (Si@O-MCMB/C) composites for the first time, in which O-MCMB core and low-cost sucrose-derived carbon shell not only effectively enhance the electrical conductivity of the anode, but also mediate the dramatic volume change of silicon during cycles. At the same time, the carbon can act as "adhesive", which is crucial in enhancing the adhesive force between Si and O-MCMB in the composites. The as-obtained Si@O-MCMB/C delivers an initial reversible capacity of 560 mAh g-1 at 0.1 A g-1, an outstanding cyclic retention of 92.8% after 200 cycles, and respectable rate capability. Furthermore, the synthetic route presented here is efficient, less expensive, simple, and easy to scale up for high-performance composites.