Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Feasibility study of large-scale mass customization 3D printing framework system with a case study on Nanjing Happy Valley East Gate

At present, the development and implementation of digital transformation are the keys to promoting high-quality industry development. The new digital fabrication method of robotic 3D printing is a research area being studied by many to tackle the issue of the declining productivity of traditional construction methods. Although many studies have been done, most of the current 3D printing projects are facing limitations in terms of scale. In order to bridge the gap, this article proposed a mass customization 3D printing framework system for large-scale projects. This article discusses how mass customization is made possible through the joint operation of the FUROBOT software and 3D printing hardware. By taking the east gate of Nanjing Happy Valley Plaza as a case study, the article demonstrates and studies the feasibility of the large-scale mass customization 3D printing framework system.     

Graphitic-C3N4/ZnCr-layered double hydroxide 2D/2D nanosheet heterojunction: Mesoporous photocatalyst for advanced oxidation of azo dyes with in situ produced H2O2

In this study the constructional modification of Graphitic carbon nitride nanosheet (GCN-ns) has been made with the aid of ZnCr layered double hydroxide (ZC-LDH) in a unique 2D-2D structure to enhance its visible light absorption. Optical and morphological study presents successful incorporation of ZC-LDH on the surface of GCN-ns. Through adjusting of GCN-ns by ZC-LDH lower recombination rate of e^?/h⁺ pairs, longer lifetimes and an increase in contamination reduction was brought out. The binary nanocomposite was employed to effectively degrade Rhodamine B under UV/vis light irradiation. The improvement in photocatalytic abilities was proven to be related to in situ self-production of H₂O₂ on GCN-ns/ZC-LDH surface by Xe light irradiation which in return accounts for additional hydroxide radical generation. Radical quenching experiments specified the main active species involved while the consequent step-scheme (S-scheme) charge transfer mechanism was proposed.     

Adaptation to Endoplasmic Reticulum Stress Enhances Resistance of Oral Cancer Cells to Cisplatin by Up-Regulating Polymerase η and Increasing DNA Repair Efficiency

Endoplasmic reticulum (ER) stress response is an adaptive program to cope with cellular stress that disturbs the function and homeostasis of ER, which commonly occurs during cancer progression to late stage. Late-stage cancers, mostly requiring chemotherapy, often develop treatment resistance. Chemoresistance has been linked to ER stress response; however, most of the evidence has come from studies that correlate the expression of stress markers with poor prognosis or demonstrate proapoptosis by the knockdown of stress-responsive genes. Since ER stress in cancers usually persists and is essentially not induced by genetic manipulations, we used low doses of ER stress inducers at levels that allowed cell adaptation to occur in order to investigate the effect of stress response on chemoresistance. We found that prolonged tolerable ER stress promotes mesenchymal–epithelial transition, slows cell-cycle progression, and delays the S-phase exit. Consequently, cisplatin-induced apoptosis was significantly decreased in stress-adapted cells, implying their acquisition of cisplatin resistance. Molecularly, we found that proliferating cell nuclear antigen (PCNA) ubiquitination and the expression of polymerase η, the main polymerase responsible for translesion synthesis across cisplatin-DNA damage, were up-regulated in ER stress-adaptive cells, and their enhanced cisplatin resistance was abrogated by the knockout of polymerase η. We also found that a fraction of p53 in stress-adapted cells was translocated to the nucleus, and that these cells exhibited a significant decline in the level of cisplatin-DNA damage. Consistently, we showed that the nuclear p53 coincided with strong positivity of glucose-related protein 78 (GRP78) on immunostaining of clinical biopsies, and the cisplatin-based chemotherapy was less effective for patients with high levels of ER stress. Taken together, this study uncovers that adaptation to ER stress enhances DNA repair and damage tolerance, with which stressed cells gain resistance to chemotherapeutics.     

Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

Phosphorylated chitosan/poly(vinyl alcohol) based proton exchange membranes modified with propylammonium nitrate ionic liquid and silica filler for fuel cell applications

In our previous work, phosphorylated chitosan was modified through polymer blending with poly(vinyl alcohol) (PVA) polymer to produce N-methylene phosphonic chitosan/poly(vinyl alcohol) (NMPC/PVA) composite membranes. The aim of this work is to further investigate the effects of a propylammonium nitrate (PAN) ionic liquid and/or silicon dioxide (SiO₂) filler on the morphology and physical properties of NMPC/PVA composite membranes. The temperature-dependent ionic conductivity of the composite membranes with various ionic liquid and filler compositions was studied by varying the loading of PAN ionic liquid and SiO₂-PAN filler in the range of 5–20 wt%. As the loading of PAN ionic liquid increased in the NMPC/PVA membrane matrix, the ionic conductivity value also increased with the highest value of 0.53 × 10^?3 S cm^?1 at 25 °C and increased to 1.54 × 10^?3 S cm^?1 at 100 °C with 20 wt% PAN. The NMPC/PVA-PAN (20 wt%) composite membrane also exhibited the highest water uptake and ion exchange capacity, with values of 60.5% and 0.60 mequiv g^?1, respectively. In addition, in the single-cell performance test, the NMPC/PVA-PAN (20 wt%) composite membrane displayed a maximum power density, which was increased by approximately 14% compared to the NMPC/PVA composite membrane with 5 wt% SiO₂-PAN. This work demonstrated that modified NMPC/PVA composite membranes with ionic liquid PAN and/or SiO₂ filler showed enhanced performance compared with unmodified NMPC/PVA composite membranes for proton exchange membrane fuel cells.     

Blends of Bio-Based Poly(Limonene Carbonate) with Commodity Polymers

In this study, blends of the bio-based poly(limonene carbonate) (PLimC) with different commodity polymers are investigated in order to explore the potential of PLimC toward generating more sustainable polymer materials by reducing the amount of petro- or food-based polymers. PLimC is employed as minority component in the blends. Next to the morphology and thermal properties of the blends the impact of PLimC on the mechanical properties of the matrix polymers is studied. The interplay of incompatibility and zero-shear melt viscosity contrast determines the blend morphology, leading for all blends to a dispersed droplet morphology for PLimC. Blends with polymers of similar structure to PLimC (i.e., aliphatic/aromatic polyester) show the best performance with respect to mechanical properties, whereas blends with polystyrene or poly(methyl methacrylate) are too brittle and polyamide 12 blends show very low elongations at break. In blends with Ecoflex (poly(butylene adipate-co-terephthalate)) and Arnitel EM400 (copoly(ether ester)) with poly(butylene terephthalate) hard and polytetrahydrofuran soft segments) a threefold increase in E-modulus can be achieved, while keeping the elongation at break at reasonable high values of ≈200%, making these blends highly interesting for applications.