Enhancing Dielectric Properties of (CaCu3Ti4O12 NWs-Graphene)/PVDF Ternary Oriented Composites by Hot Stretching.

Using high-dielectric inorganic ceramics as fillers can effectively increase the dielectric constant of polymer-based composites. However, a high percentage of fillers will inevitably lead to a decrease in the mechanical toughness of the composite materials. By introducing high aspect ratio copper calcium titanate (CaCu3Ti4O12) nanowires (CCTO NWs) and graphene as fillers, the ternary poly(vinylidene fluoride) (PVDF)-based composites (CCTO NWs-graphene)/PVDF with a significant one-dimensional orientation structure were prepared by hot stretching. CCTO NWs and graphene are arranged in a directional manner to form a large number of microcapacitor structures, which significantly improves the dielectric constant of the composites. When the ratio of CCTO NWs and graphene is 0.2 and 0.02, the oriented composites have the highest dielectric constant, which is 19.3% higher than the random composites, respectively. Numerical simulations reveal that the introduction of graphene and the construction of the one-dimensional oriented microstructure have a positive effect on improving the dielectric properties of the composites. This study provides a strategy to improve the dielectric properties of composite materials by structural design without changing the filler content, which has broad application prospects in the field of electronic devices.

Preparation of High-k Polymeric Composites Based on Low-k Boron Nitride Nanosheets with High-Connectivity Lamellar Structure.

Typically, the basic method to enhance the dielectric response of polymer-based composites is to fill giant dielectric ceramic fillers, such as BaTiO3 and CaCu3Ti4O12, into the polymer matrix. Here, by using low-k boron nitride (BN) with well-controlled microstructure and surface, we successfully prepared a high-k polymeric composite, where the improvement in the dielectric constant of the composite even exceeds that of composites containing BaTiO3 and CaCu3Ti4O12 particles at the same weight percent. First, a lamellar boron nitride nanosheet (BNNS) aerogel was prepared by bidirectional freezing and freeze drying, respectively, and then the aerogel was calcined at 1000 °C to obtain the lamellar BNNS skeleton with some hydroxyl groups. Finally, the epoxy resin (EP) was vacuum impregnated into the BNNS skeleton and cured inside to prepare the lamellar-structured BNNSs/EP (LBE) composites. Interestingly, the dielectric constants of LBE with a 10 wt % BNNS content reached 8.5 at 103 Hz, which was higher by 2.7 times than that of pure EP. The experimental data and the finite element simulations suggested that the increased dielectric constants of LBE resulted from the combination of two factors, namely, the lamellar microstructure and the hydroxyl groups. The stacking of the BNNS phase into a highly connected lamellar skeleton significantly increased the internal electric field and the polarization intensity, while the introduction of hydroxyl groups on the BNNS surface further improved the polarization of the composite, resulting in a significant increase in the dielectric constant of the LBE. This work provides a new strategy for improving the dielectric constant through the microstructure design of composites.

Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery.

BACKGROUND: Surgical site infections (SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors. AIM: To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively. METHODS: We retrospectively analysed the inpatient records of Shaanxi Provincial People's Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002 (NRS 2002) scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance (NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model. RESULTS: A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus (42.2%), the liver (27.6%), the gastrointestinal tract (19.1%), the appendix (5.9%), the kidney (3.7%), and the groin area (1.4%). SSI occurred in 5% of the patients (n = 150). The risk factors associated with SSI were as follows: Age; gender; marital status; place of residence; history of diabetes; surgical season; surgical site; NRS 2002 score; preoperative white blood cell, procalcitonin (PCT), albumin, and low-density lipoprotein cholesterol (LDL) levels; preoperative antibiotic use; anaesthesia method; incision grade; NNIS score; intraoperative blood loss; intraoperative drainage tube placement; surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio (OR) = 5.698, 95% confidence interval (CI): 3.305-9.825, P = 0.001], antibiotic use (OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3 (OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia (OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2 (OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 µg/L (OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L (OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL (OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season (P < 0.05), surgical site (P < 0.05), and incision grade I or III (P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score (0.662). CONCLUSION: The patient's condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.