Improvement of dielectric properties and energy storage performance in sandwich-structured P(VDF-CTFE) composites with low content of GO nanosheets.

Polymer-based dielectric capacitors play a notable part in the practical application of energy storage devices. Graphene oxide (GO) nanosheets can improve the dielectric properties of polymer-based composites. However, the breakdown strength will greatly reduce with the increase of GO content. Hence, the construction of sandwich structure can enhance the breakdown strength without reducing the dielectric constant. Herein, single-layered and sandwich-structured poly(vinylidene fluoride-co-chlorotrifluoroethylene) (P(VDF-CTFE)) nanocomposites with low content of GO nanosheets (<1.0 wt%) are prepared via employing a straightforward casting method. Compared with the single-layered composites and pure P(VDF-CTFE), the sandwich-structured composites exhibit comprehensively better performance compared. The sandwich-structured composite with 0.4 wt% GO nanosheets show an excellent dielectric constant of 13.6 (at 1 kHz) and an outstanding discharged energy density of 8.25 J cm-3at 3400 kV cm-1. These results demonstrate that the growth of the dielectric properties is owing to 2D GO nanosheets and the enhancement of breakdown strength due to the sandwich structure. The results from finite element simulation provide theoretical support for the design of high energy density composites.

Polypyrrole Encapsulated Nickel Nanorods for Conductivity Improvement by Magnetic Alignment.

Polypyrrole encapsulated nickel nanorods (PPy@Ni) have been prepared by electro-polymerization using Ni nanorods as template. PPy@Ni were characterized using transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectrometer, Fourier transform infrared (FTIR) spectra and vibrating sample magnetometry (VSM). The maximum saturation magnetization of PPy@Ni was up to 44.6 emu g-1. Further, PPy@Ni was used as the additive for the conductivity improvement in polymer matrix. Experimental results showed that the electric conductivity reached 1.8×10-3 S m-1 when 5% PPy@Ni was added in the poly(urethane acrylate) matrix by applying a magnetic field. Further tests showed that when the additive concentration increased from 0.1% to 5%, the correspondence saturation magnetization increased from 15.1 emug-1 to 31.3 emug-1. This work provides new light on controllable fabrication of conducting polymer by adding magnetic alignment conductive additives.

High quantum yield and well-dispersed quantum dots luminescent composite through sodium carboxymethyl starch.

It is a challenging task to prepare well-dispersed and highly luminescent quantum dots (QDs) powder and a new strategy is reported in this article. Sodium carboxymethyl starch (CMS-Na) was employed in this work to prepare the QDs-starch composite. Ultraviolet (UV) light shows that the blank starches had no fluorescence, while the QDs-starches were highly luminescent. Scanning electron microscopy (SEM) observation shows that the QDs-starch composite has the typical particle morphology with the diameter around 200 nm. Energy dispersive X-ray spectroscopy (EDX) results show that there are intensive tellurium (Te) and cadmium (Cd) element signals. Combined fluorescent lifetime and steady-state spectrometer show that the QDs-starch quantum yields (QYs) increase when the QDs loading increases from 1 × 10-6 mol/g to 2 × 10-6 mol/g, but when the loadings further increase, the QYs decrease slightly. For the red colour (λem  = 660 nm) QDs, the QYs can reach to as high as 28.2%, and for the other colour QDs they can also have the QYs above 22%. Time-resolved photobleaching experiments show that the fluorescent QDs-starch composite has a half-decay time of 40.23 s. These results indicate that the CMS-Na is a promising QDs dispersant to obtain high QY QD composites.

A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index.

Traditional assessments of anxiety and depression face challenges and difficulties when it comes to understanding trends in-group psychological characteristics. As people become more accustomed to expressing their opinions online, location-based online media and cutting-edge algorithms offer new opportunities to identify associations between group sentiment and economic- or healthcare-related variables. Our research provides a novel approach to analyzing emotional well-being trends in a population by focusing on retrieving online information. We used emotionally enriched texts on social media to build the Public Opinion Dictionary (POD). Then, combining POD with the word vector model and search trend, we developed the Composite Anxiety and Depression Index (CADI), which can reflect the mental health level of a region during a specific time period. We utilized the representative external data by CHARLS to validate the effectiveness of CADI, indicating that CADI can serve as a representative indicator of the prevalence of mental disorders. Regression and subgroup analysis are employed to further elucidate the association between public mental health (measured by CADI) with economic development and medical burden. The results of comprehensive regression analysis show that the Import-Export index (-16.272, p < 0.001) and average cost of patients (4.412, p < 0.001) were significantly negatively associated with the CADI, and the sub-models stratificated by GDP showed the same situation. Disposable income (-28.389, p < 0.001) became significant in the subgroup with lower GDP, while the rate of unemployment (2.399, p < 0.001) became significant in the higher subgroup. Our findings suggest that an unfavorable economic development or unbearable medical burden will increase the negative mental health of the public, which was consistent across both the full and subgroup models.

Effects of accelerated biological age on depressive symptoms in a causal reasoning framework.

BACKGROUND: Depression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation. METHODS: We utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly. RESULTS: In the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives. LIMITATIONS: Depressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population. CONCLUSIONS: This study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.