Rapid microwave-assisted bulk production of high-quality reduced graphene oxide for lithium ion batteries

Graphene-based advanced electrodes with improved electrochemical properties have received increasing attention for use in lithium ion batteries (LIBs). The conventional synthesis of graphene via liquid phase exfoliation or chemical reduction of graphene oxide (GO) approaches, however, either involves prolonged processing or leads to the retainment of high-concentration oxygen functional groups (OFGs). Herein, bulk synthesis of high-quality reduced graphene oxide using microwave irradiation (MWrGO) within few seconds is reported. The electromagnetic interaction of GO with microwaves is elucidated at molecular level using reactive molecular dynamic simulations. The simulation suggests that higher power microwave irradiation results in significantly less retainment of OFGs and the formation of structural voids. The synthesized MWrGO samples are thoroughly characterized in terms of structural evolution and physicochemical properties. Specifically, a modified ID/IG-in ratio metric for Raman spectrum, wherein the intensity contribution of D’ peak is deducted from the apparent G peak, is proposed to investigate the structural evolution of synthesized MWrGO, which yields a more reliable evaluation of structural disorder over traditional ID/IG ratio. Li-ion half-cell studies demonstrate that the MWrGO is an excellent candidate for usage as high capacity anode (750.0 mAh g-1 with near-zero capacity loss) and high-performance cathode (high capacity retention of ~70% for LiCoO2 at 10 C) for LIBs

Visualizing the intercity correlation of PM_2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data - Fig 4

<p><b>Matrix views of the intercity correlations and time lags for PM</b>_<b>2.5</b><b>time series between 17 cities in (A) February, (B) May, (C) August, and (D) November of 2014 in the Beijing-Tianjin-Hebei area.</b> The color and size of the circles indicate the strength of the correlation. The color bar on the right provides a scale of the correlation coefficients. Orange, red, and claret colors mean the correlation coefficients are over 0.5, 0.7, and 0.9, respectively, while the green color indicates correlation coefficients <0.5. The label within the circle refers to the time lag. A positive time lag, τ, means the city on the y-axis lags the city on the x-axis by τ hours. Similarly, a negative time lag, τ, means the city on the y-axis leads the city on the x-axis by -τ hours. A hash sign (#) means the correlation for the time lag is significantly larger than the correlation without the time lag at the 10% level. A dollar sign ($) refers to the statistical significance at the 5% level. An asterisk (*) refers to the statistical significance at the 1% level. Note that for clear presentation, all matrix views show only the upper portion of the matrix to avoid duplication. Matrix views for other months are provided in the supplementary information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192614#pone.0192614.s002" target="_blank">S2 Fig</a>). This figure was produced using Python 2.7.5 (<a href="https://www.python.org/" target="_blank">https://www.python.org</a>) and Matplotlib 1.5.0 (<a href="https://matplotlib.org/" target="_blank">https://matplotlib.org</a>/).</p

Landscape metrics that had relationship with PM_2.5 concentration (|<i>r</i>|>0.6).

<p>Landscape metrics that had relationship with PM_2.5 concentration (|<i>r</i>|>0.6).</p

Visualizing the intercity correlation of PM_2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data - Fig 3

<p><b>Maps of intercity correlations and time lags for PM</b>_<b>2.5</b><b>time series between 17 cities in (A) February, (B) May, (C) August, and (D) November of 2014 in the Beijing-Tianjin-Hebei area.</b> The line colors refer to the strength of the correlation of the PM_2.5 time series between the two linked cities. The color bar on the right provides a scale of the correlation coefficients. The numbers on the lines refer to the time lags. Solid lines mean the correlation with the time lag is significantly larger than the correlation without the time lag at the 10% level, while the dashed lines mean the correlation with the time lag is not significantly larger than the correlation without the time lag at the 10% level. The arrows on the lines indicate the temporal order of the correlated PM_2.5 time series, where the PM_2.5 time series in the city at the tail of the arrow leads the PM_2.5 time series in the city at the head of the arrow. Map views for other months are provided in the supplementary information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192614#pone.0192614.s001" target="_blank">S1 Fig</a>). This figure was produced using Python 2.7.5 (<a href="https://www.python.org/" target="_blank">https://www.python.org</a>) and Matplotlib 1.5.0 (<a href="https://matplotlib.org/" target="_blank">https://matplotlib.org</a>/).</p

Cities in the Beijing–Tianjin–Hebei region.

<p>This map was generated using ArcGIS 10.2.2 (<a href="http://www.esri.com/" target="_blank">www.esri.com</a>).</p

List of the selected landscape metrics.

<p>Sources: Fragstats documents 4.2 (2014).</p><p>List of the selected landscape metrics.</p

Classification and distribution of air quality monitoring sites in Beijing.

<p>Classification and distribution of air quality monitoring sites in Beijing.</p

Seasonal pattern of four categories of all sites.

<p>Seasonal pattern of four categories of all sites.</p

Visualizing the intercity correlation of PM_2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data

<div><p>The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM_2.5) problem. To date, considerable progress has been made toward understanding the PM_2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM_2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM_2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM_2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM_2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM_2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM_2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM_2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM_2.5 pollution for impact analysis.</p></div

Classification and description of independent variables.

<p>* xx corresponds to the circular buffer radii (in meters).</p><p>Classification and description of independent variables.</p