Interface Bonding Properties and Mechanism of Steel Fiber and Hot Melt Adhesive via Interface Design Engineering.

Steel fiber textile, which is composed of steel fibers and glass fibers, has a support layer impregnated with hot melt adhesive (HMA). During long-term service, the bonding force between the steel fiber/HMA system interfaces is poor. In order to improve the bond strength and durability of the interface, this paper introduced sandblasting, acid-etching, and phosphating treatments on the surface of the steel fibers. Also, the effects of these three pretreatment methods on the bond strength of the steel fiber/HMA interface were investigated. The results indicate that the interfacial bond strength of composites made from steel fibers is improved via surface treatment. Under a hydrothermal and simulated concrete pore solution environment, the durability of the steel fiber/HMA interface after sandblasting and acid-etching pretreatment is not as good as that after phosphating pretreatment. The mechanical properties of the phosphating/HMA composite were maintained at 4.56 and 2.24 times compared to those of the untreated/HMA composite, respectively. This is because the pinning effect formed by the phosphating film on the surface of steel fibers at the interface of steel fiber/HMA can serve as a physical barrier against corrosion, preventing the invasion of chloride ions and water vapor and improving the durability of the interface.

Optimizing Epoxy Interfacial Bonding Properties and Failure Mechanism between Carbon Textile-Reinforced Mortar Composites and Concrete Substrates.

Textile-reinforced mortar (TRM) composites have been extensively utilized in building reinforcement due to their exceptional mechanical properties. The weakest link in the entire structure is the interface between the TRM composites and the concrete; however, it plays a crucial role in effectively transferring stress. Researchers have taken measures to improve the strength of the interface, but the results are relatively scattered. In this paper, the surface treatment of the substrate, the thickness of the surfactant, and the physical doping of the surfactant on the interfacial bonding strength of the concrete were comparatively studied. The results demonstrate that the sandblasting treatment on the surface of the concrete enhances the bonding area between the mortar and the concrete of the reinforcement layer, leading to a 50% increase in the bending resistance of the structure. When the surfactant thickness increases to 0.5 kg/m2, more surfactants penetrate the mortar and concrete. This significantly inhibits the occurrence of cracks in the structure. The addition of 2.5% Al2O3 nanomaterials to the surfactant diminishes the shrinkage rate of the curing process, enhances the impact toughness, and improves the flexural and compressive properties of the bonding layer. The ultimate load of the structure increases by 65%. Physical doping of the surfactant is the most effective measure with the most apparent improvement result. It significantly enhances the bonding strength of the interface and can be widely used in construction.

Study on the Mechanical Properties and Strengthening Mechanism of Interface-Modified Carbon Fiber Mesh Reinforced Cement-Based Composites with SCA&HMC.

Carbon fiber mesh reinforced cement-based composites (CMCCs) have received extensive attention in the field of engineering repair and structural reinforcement due to their outstanding properties such as two-way force, rust prevention, high specific strength, and low base surface requirements. However, the development of this material has been slowed down to some extent due to the poor interfacial bonding between the carbon fiber mesh and the cement matrix. In this paper, a novel fabrication strategy was proposed in which the carbon fiber mesh was modified with epoxy resin and silane coupling agent (SCA) to increase its surface chemical activity. Meanwhile, the hydroxymethyl cellulose (HMC) was also filled into the concrete matrix to improve the mechanical strength of the matrix as well as the load transfer behaviors between the mortar and carbon fiber (CF) mesh. The potential to employ SCA and HMC was evaluated for the making of CMCCs via the above methods. The results showed that the longitudinal shear strength of composites with SCA and SCA&HMC increased by 26.6% and 56.1% compared to those of CF with epoxy resin (EP) reinforced composites, respectively. The flexural strength of composite with SCA&HMC increases by 147.6% compared to I-(F) without CF. The novel II-HCM&CF/EP-SCA composites with excellent performance are promised to be applied in practical uses.

Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.

PURPOSE: We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients. METHODS: A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis. RESULTS: A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance. CONCLUSION: This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.