3D石墨烯海绵对聚酰亚胺泡沫使用安全性的影响

根据石墨烯及海绵结构的特性,以一步法聚酰亚胺泡沫(PIF)为基体,通过在一步法PIF孔结构中构筑还原氧化石墨烯海绵(rGOs),从而对一步法PIF材料进行阻燃改性,研究rGOs在不同水热还原温度下对rGOs-PIF材料阻燃性能、微观泡孔结构的影响、烟密度的影响。结果表明,rGOs-PIF材料阻燃性能得到明显提升,使用安全性提高。当水热烟密度等方面的影响反应温度为180℃时,rGOs-PIF样品的极限氧指数(LOI)最高达到28.3%,相较于一步法PIF提高了34.38%;当水热反应温度为160℃时,rGOs-PIF样品烟密度等级(SDR)降低了77.98%,最大烟密度(MSD)降低了77.83%。

Temporal Unfolding of Racial Ingroup Bias in Neural Responses to Perceived Dynamic Pain in Others

In this study,we investigated how empathic neural responses unfold over time in different empathy networks when viewing same-race and other-race individuals in dynamic painful conditions.We recorded magnetoencephalography signals from Chinese adults when viewing video clips showing a dynamic painful(or non-painful)stimulation to Asian and White models’faces to trigger painful(or neutral)expressions.We found that perceived dynamic pain in Asian models modulated neural activities in the visual cortex at 100 ms–200 ms,in the orbitofrontal and subgenual anterior cingulate cortices at 150 ms–200 ms,in the anterior cingulate cortex around 250 ms–350 ms,and in the temporoparietal junction and middle temporal gyrus around 600 ms after video onset.Perceived dynamic pain in White models modulated activities in the visual,anterior cingulate,and primary sensory cortices after 500 ms.Our findings unraveled earlier dynamic activities in multiple neural circuits in response to same-race(vs other-race)individuals in dynamic painful situations.

Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy

Solar-induced chlorophyll fluorescence(SIF)has shown remarkable results in estimating vegetation carbon cycles,and combining it with the photochemical reflectance index(PRI)has great potential for estimating gross primary productivity(GPP).However,few studies have used SIF combined with PRI to estimate crop canopy GPP.Large temporal and spatial variability between SIF,PRI,and GPP has also been found in remote sensing observations,and the observed PRI and SIF are influenced by the ratio of different observed information(e.g.,background,direct sunlit,and shaded leaves)and the physiological state of the vegetation.In this study,the PRI and SIF from a multi-angle spectrometer and the GPP from an eddy covariance system were used to assess the ability of the PRI to enhance the SIF-GPP estimation model.A semi-empirical kernel-driven Bidirectional Reflectance Distribution Function(BRDF)model was used to describe the hotspot PRI/SIF(PRIhs/SIFhs),and a modified two-leaf model was used to calculate the total canopy PRI/SIF(PRItot/SIFtot).We compared the accuracies of PRIhs/SIFhs and PRItot/SIFtot in estimating GPP.The results indicated that the PRItot+SIFtot-GPP model performed the best,with a correlation coefficient(R2)of the validation dataset of 0.88,a root mean square error(RMSE)of 3.74,and relative prediction deviation(RPD)of 2.71.The leaf area index(LAI)had a linear effect on the PRI/SIF estimation of GPP,but the temperature and vapor pressure differences had nonlinear effects.Compared with hotspot PRIhs/SIFhs,PRItot/SIFtot exhibited better consistency with GPP across different time series.Our research demonstrates that PRI is effective in enhancing SIF and PRI for estimating GPP on the rice canopy and also suggests that the two-leaf model would contribute to the vegetation index tracking the real-time crop productivity.