Numerical Simulation of Polyacrylamide Hydrogel Prepared via Thermally Initiated Frontal Polymerization.

Traditional polymer curing techniques present challenges such as a slow processing speed, high energy consumption, and considerable initial investment. Frontal polymerization (FP), a novel approach, transforms monomers into fully cured polymers through a self-sustaining exothermic reaction, which enhances speed, efficiency, and safety. This study focuses on acrylamide hydrogels, synthesized via FP, which hold significant potential for biomedical applications and 3D printing. Heat conduction is critical in FP, particularly due to its influence on the temperature distribution and reaction rate mechanisms, which affect the final properties of polymers. Therefore, a comprehensive analysis of heat conduction and chemical reactions during FP is presented through the establishment of mathematical models and numerical methods. Existing research on FP hydrogel synthesis primarily explores chemical modifications, with limited studies on numerical modeling. By utilizing Differential Scanning Calorimetry (DSC) data on the curing kinetics of polymerizable deep eutectic solvents (DES), this paper employs Malek's model selection method to establish an autocatalytic reaction model for FP synthesis. In addition, the finite element method is used to solve the reaction-diffusion model, examining the temperature evolution and curing degree during synthesis. The results affirm the nth-order autocatalytic model's accuracy in studying acrylamide monomer curing kinetics. Additionally, factors such as trigger temperature and solution initial temperature were found to influence the FP reaction's frontal propagation speed. The model's predictions on acrylamide hydrogel synthesis align with experimental data, filling the gap in numerical modeling for hydrogel FP synthesis and offering insights for future research on numerical models and temperature control in the FP synthesis of high-performance hydrogels.

Mechanical Behavior of Al-Si10-Mg P-TPMS Structure Fabricated by Selective Laser Melting and a Unified Mathematical Model with Geometrical Parameter.

Compared with the traditional lattice structure, the triply periodic minimal surface (TPMS) structure can avoid stress concentration effectively. Here, it is promising in the fields of lightweight and energy absorption. However, the number of structural parameters and mechanical properties of the TPMS structure is plentiful, and the relationship between them is unclassified. In this paper, for the first time, a unified mathematical model was proposed to establish the relationship between TPMS structural design parameters and mechanical properties. Fifteen primitive models were designed by changing the structural parameters (level-set value C and thickness T) and manufacturing by selective laser melting. The geometric defects and surface quality of the structures were explored by optical microscope and scanning electron microscopy (SEM). The mechanical properties were investigated by quasi-static compression test and finite element simulation. The influence of building direction on structural mechanical behavior (failure mode, stress-strain curve) was studied. The real mechanical properties (Young's modulus and plateau stress) of the structure could be predicted according to different C and T combinations. Finally, the energy absorption characteristics were explored. The results showed that when the C value is 0.6 in the range of 0-0.6, the energy absorption performance of the structure is at the maximum level.

Creep modeling of composite materials based on improved gene expression programming.

In this article, a new method for creep modeling and performance prediction of composite materials is presented. Since Findley power-law model is usually suitable for studying one-dimensional time-dependent creep of materials under low stress, an intelligent computing method is utilized to derive three temperature-related sub-functions, the creep model as a function of time and temperature is established. In order to accelerate convergence rate and improve solution accuracy, an improved gene expression programming (IGEP) algorithm is proposed by adopting the probability-based population initialization and semi-elite roulette selection strategy. Based on short-term creep data at seven temperatures, a bivariate creep model with certain physical significance is developed. At fixed temperature, the univariate creep model is acquired. R2, RMSE, MAE, RRSE statistical metrics are used to verify the validity of the developed model by comparison with viscoelastic models. Shift factor is solved by Arrhenius equation. The creep master curve is derived from time-temperature superposition model, and evaluated by Burgers, Findley and HKK models. R-square of IGEP model is above 0.98 that is better than classical models. Moreover, the model is utilized to predict creep values at t = 1000 h. Compared with experimental values, the relative errors are within 5.2%. The results show that the improved algorithm can establish effective models that accurately predict the long-term creep performance of composites.

Ultrafast Macroscopic Assembly of High-Strength Graphene Oxide Membranes by Implanting an Interlaminar Superhydrophilic Aisle.

A macroscopic-assembled graphene oxide (GO) membrane with sustainable high strength presents a bright future for its applications in ionic and molecular filtration for water purification or fast force response for sensors. Traditionally, the bottom-up macroscopic assembly of GO sheets is optimized by widening the interlaminar space for expediting water passage, frequently leading to a compromise in strength, assembly time, and ensemble thickness. Herein, we rationalize this strategy by implanting a superhydrophilic bridge of cobalt-based metal-organic framework nanosheets (NMOF-Co) as an additional water "aisle" into the interlaminar space of GO sheets (GO/NMOF-Co), resulting in a high-strength macroscopic membrane ensemble with tunable thickness from the nanometer scale to the centimeter scale. The GO/NMOF-Co membrane assembly time is only 18 s, 30800 times faster than that of pure GO (154 h). More importantly, the obtained membrane attains a strength of 124.4 MPa, which is more than 3 times higher than that of the GO membrane prepared through filtration. The effect of hydrophilicity on membrane assembly is also investigated by introducing different intercalants, suggesting that, except for the interlamellar spacing, the interlayered hydrophilicity plays a more decisive role in the macroscopic assembly of GO membranes. Our results give a fundamental implication for fast macroscopic assembly of high-strength 2D materials.

Starch as a reinforcement agent for poly(ionic liquid) hydrogels from deep eutectic solvent via frontal polymerization.

For the first time, conductive starch/poly(ionic liquid) hydrogels from a polymerizable deep eutectic solvent (DES) by frontal polymerization (FP) were reported. The solubility and dispersibility for starch granules in the polymerizable DES was investigated. The effects of starch content on FP behaviors, mechanical properties and electrical conductivity of composite hydrogels were studied. Results showed that starch could be partially dissolved and dispersed in the DES. Comparing with the pure poly(ionic liquid) hydrogel from DES (the tensile strength was 41 K Pa), the tensile strength of composite hydrogel could increased by 3.07 times and reached 126 K Pa. When the fixed strain was 80 %, its compressive strength could increase by 6 times and reaches 16.8 MPa. The main reason was that there was a strong interfacial interaction between starch and the polymer hydrogel network. The starch/poly(ionic liquid) composite hydrogels also had good electrical conductivity. Absorption of water could increase the conductivity of the composite hydrogel significantly.

High conductive graphene assembled films with porous micro-structure for freestanding and ultra-low power strain sensors.

Graphene emerges as an ideal material for constructing high-performance strain sensors, due to its superior mechanical property and high conductivity. However, in the process of assembling graphene into macroscopic materials, its conductivity decreases significantly. Also, tedious fabrication process hinders the application of graphene-based strain sensors. In this work, we report a freestanding graphene assembled film (GAF) with high conductivity ((2.32 ± 0.08) × 105 S m-1). For the sensitive materials of strain sensors, it is higher than most of reported carbon nanotube and graphene materials. These advantages enable the GAF to be an ultra-low power consumption strain sensor for detecting airflow and vocal vibrations. The resistance of the GAF remains unchanged with increasing temperature (20-100 ℃), exhibiting a good thermal stability. Also, the GAF can be used as a strain sensor directly without any flexible substrates, which greatly simplifies the fabrication process in comparison with most reported strain sensors. Additionally, the GAF used as a pressure sensor with only ~4.7 µW power is investigated. This work provides a new direction for the preparation of advanced sensors with ultra-low power consumption, and the development of flexible and energy-saving electronic devices.

Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods.

The present study aimed to examine the molecular mechanisms of coronary artery disease (CAD). A total of four microarray datasets (training dataset no. GSE12288; validation dataset nos. GSE20680, GSE20681 and GSE42148) were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. Weighted gene co­expression network analysis was applied to identify highly preserved modules across the four datasets. Differentially expressed genes (DEGs) with significant consistency in the four datasets were selected using the MetaDE method. The overlapping genes amongst the DEGs with significant consistency and in the preserved modules were used to construct a protein­protein interaction (PPI) network, followed by functional enrichment analysis. A total of 11 modules were established in the training dataset, and five of them were highly preserved across all four datasets, including 873 genes. There was a total of 836 DEGs with significant consistency in the four datasets. A total of 177 overlapping genes were selected, with which a PPI network was constructed. The top five genes of the PPI network were identified based on their degrees: LCK proto­oncogene, Src family tyrosine kinase (LCK), euchromatic histone lysine methyltransferase 2 (EHMT2), inosine monophosphate dehydrogenase 2 (IMPDH2), protein phosphatase 4 catalytic subunit (PPP4C) and ζ­chain of T­cell receptor associated protein kinase 70 (ZAP70). Genes in the PPI network were significantly involved in a number of Kyoto Encyclopedia Genes and Genomes pathways, including the 'natural killer cell mediated cytotoxicity', 'primary immunodeficiency' and 'Fc gamma R­mediated phagocytosis' pathways. LCK, EHMT2, IMPDH2, PPP4C and ZAP70 are suggested as promising molecular biomarkers for CAD. The 'natural killer cell mediated cytotoxicity', 'primary immunodeficiency' and 'Fc gamma R­mediated phagocytosis' pathways may serve important roles in CAD.