Abstract Accurate estimates of aerosol refractive index (RI) are critical for modeling aerosol-radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi-empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen-to-carbon (O:C) and hydrogen-to-carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz-Lorentz equation and trained with n-values at 589 nm () of 160 pure compounds. The predictions can be expanded to predict n-values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n-values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of predictions for all SOA samples are within 5%. The model suggests that -values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.
As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized machine learning or deep learning techniques to implement probabilistic forecasting reconciliation and have made notable progress. However, these methods treat the reconciliation step as a fixed and hard post-processing step, leading to a trade-off between accuracy and coherency. In this paper, we propose a new approach for probabilistic forecast reconciliation. Unlike existing approaches, our proposed approach fuses the prediction step and reconciliation step into a deep learning framework, making the reconciliation step more flexible and soft by introducing the Kullback-Leibler divergence regularization term into the loss function. The approach is evaluated using three hierarchical time series datasets, which shows the advantages of our approach over other probabilistic forecast reconciliation methods.
Numerous functional magnetic resonance imaging (fMRI) studies have examined the neural mechanisms of negative emotional words, but scarce evidence is available for the interactions among related brain regions from the functional brain connectivity perspective. Moreover, few studies have addressed the neural networks for negative word processing in bilinguals. To fill this gap, the current study examined the brain networks for processing negative words in the first language (L1) and the second language (L2) with Chinese-English bilinguals. To identify objective indicators associated with negative word processing, we first conducted a coordinate-based meta-analysis on contrasts between negative and neutral words (including 32 contrasts from 1589 participants) using the activation likelihood estimation method. Results showed that the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus were involved in processing negative words. Next, these six clusters were used as regions of interest in effective connectivity analyses using extended unified structural equation modeling to pinpoint the brain networks for bilingual negative word processing. Brain network results revealed two pathways for negative word processing in L1: a dorsal pathway consisting of the left IFG, the left mPFC, and the left PCC, and a ventral pathway involving the left amygdala, the left ITG, and the left thalamus. We further investigated the similarity and difference between brain networks for negative word processing in L1 and L2. The findings revealed similarities in the dorsal pathway, as well as differences primarily in the ventral pathway, indicating both neural assimilation and accommodation across processing negative emotion in two languages of bilinguals.
Summary Despite the unique switching characteristics of CO2-responsive foaming, its stability remains questionable. In this protocol, we describe steps to synthesize a stable CO2-responsive foam by adding the preferably selected hydrophilic nanoparticle N20 into the surfactant C12A. We detail the selection of the most suitable nanoparticles for the surfactant by measuring the foaming volume and half-life of the dispersion. The protocol can be extended to manufacture with other types of responsive foams (e.g., light responsive foams, magnetic responsive foams). For complete details on the use and execution of this protocol, please refer to Li et al. (2022).1
Ozone reactions on human body surfaces produce volatile organic compounds (VOCs) that influence indoor air quality. However, the dependence of VOC emissions on the ozone concentration has received limited attention. In this study, we conducted 36 sets of single-person chamber experiments with three volunteers exposed to ozone concentrations ranging from 0 to 32 ppb. Emission fluxes from human body surfaces were measured for 11 targeted skin-oil oxidation products. For the majority of these products, the emission fluxes linearly correlated with ozone concentration, indicating a constant surface yield (moles of VOC emitted per mole of ozone deposited). However, for the second-generation oxidation product 4-oxopentanal, a higher surface yield was observed at higher ozone concentrations. Furthermore, many VOCs have substantial emissions in the absence of ozone. Overall, these results suggest that the complex surface reactions and mass transfer processes involved in ozone-dependent VOC emissions from the human body can be represented using a simplified parametrization based on surface yield and baseline emission flux. Values of these two parameters were quantified for targeted products and estimated for other semiquantified VOC signals, facilitating the inclusion of ozone/skin oil chemistry in indoor air quality models and providing new insights on skin oil chemistry.
Adaptive radiative cooling offers smart thermal regulation that saves energy for conditioning regardless of the variation in environment or requirements. In a recent study by Banerjee and colleagues, an electrochromic device based on the redox process of PEDOT was developed, enabling tunable surface temperature by applied electrical bias at ambient conditions.
Emerging organic pollutants (EOPs) in water are of great concern due to their high environmental risk, so urgent technologies are needed for effective removal of those pollutants. Herein, a heterogeneous advanced oxidation process (AOP) of peroxymonosulfate (PMS) activation by functional material was developed for degradation of a typical antibiotic, gatifloxacin (GAT). The reactive species including sulfate radical (SO4•−) and singlet oxygen (1O2) in this AOP were regulated by interlayered ions (Na+/H+) of titanate nanotubes that supported on Co(OH)2 hollow microsphere. Both the Na-type (NaTi-CoHS) and H-type (HTi-CoHS) materials achieved efficient PMS activation for GAT degradation, and HTi-CoHS even exhibited a relatively high degradation efficiency of 96.6% within 5 min. Co(OH)2 was considered the key component for generation of SO4•− after PMS activation, while hydrogen titanate nanotubes (H-TNTs) promoted the transformation of peroxysulfate radical (SO5•−) to 1O2 by hydrogen bond interaction. Therefore, when the interlayer ion of TNTs transformed from Na+ to H+, more 1O2 was produced for organic pollutant degradation. H-TNTs with lower symmetry preferred to adsorb PMS molecules to achieve interlayer electron transport through hydrogen bonding, rather than electrostatic interaction of Na+ for Na-TNTs. In addition, the degradation pathway of GAT mainly proceeded by the cleavage of C–N bond at the 8 N site of the piperazine ring, which was confirmed by condensed Fukui index and mass spectrographic analysis. This work gives new sights into the regulation of reactive species in AOPs by the composition of material and promotes the understanding of pollutant degradation mechanisms in water treatment process.