To improve the corrosion resistance of traditional pure Zn coatings, we used 30 mm diameter Q195 welded pipes as the substrate and prepared a series of hot-dip galvanized alloy coatings by adding trace amounts of alloy elements such as Al, Ni, and Re to the Zn bath. First, the main factors affecting corrosion resistance were identified through a four-factor and three-level orthogonal experiment. Then, the experiment was further improved for the primary factors, and single-factor experiments were conducted to obtain the optimal parameter combination. Finally, the microstructure characteristics and corrosion resistance of the coating were studied and analyzed using methods such as high and low temperature humidity test, neutral salt spray test, metallographic analysis, and scanning electron microscopy. Results indicate that the introduction of alloying elements suppresses the growth of ζ layer, which makes the coating structure compact, and improves the corrosion resistance of the coating. The coating prepared in this study could remain rustless throughout a 72 h salt spray test and a 120 h humidity test. The process for preparing the alloy coating is same as the existing production process for traditional Zn coatings.

Although regression analysis can predict some drape indicators, they have problems such as low prediction accuracy and the inability to calculate some indicators. To overcome these issues, this study proposes a new method using genetic algorithm to optimize BP neural network (GA-BP neural network) to improve the prediction accuracy of real fabric drape. In this study, we designed a GA-BP neural network model, selected 100 pure cotton woven fabric samples from the fabric database, including 80 training samples, 10 test samples, and 10 validation samples, used the genetic algorithm to optimize the parameters of the neural network, and used correlation analysis to optimize sample input parameters to improve the prediction performance of the model. The results of the drape coefficient prediction for the 10 test samples show that compared with the traditional BP neural network, the average absolute percentage error of the BP neural network optimized by the genetic algorithm decreased from 12.74% to 7.03%. Furthermore, we used an empirical equation to identify error cycles and concluded that the optimal number of hidden layer nodes is 9. This study indicates that the GA-BP neural network can effectively improve the accuracy of fabric drape prediction and has important application value for the virtualization of fabric drape performance.

In order to study the welding methods suitable for metal bipolar plates, 0.1 mm 316L stainless steel was welded by four common bipolar plate welding methods : laser welding, vacuum diffusion welding, brazing and ultrasonic welding.The potentiodynamic polarization curves of four kinds of welded joints were measured by using CHI-604E electrochemical workstation. The electrochemical corrosion properties of four kinds of welded joints were compared, and the electrochemical corrosion behavior was analyzed by combining microstructure and chemical composition.It is found that the corrosion resistance of vacuum diffusion welded joints and laser welded joints is strong, and the corrosion resistance of ultrasonic welded joints and brazed joints is weak.The post-weld deformation of four kinds of welded specimens was measured by laser spectral confocal microscope. Under the action of fixture, the deformation of laser welded specimens was smaller than that of other specimens.The welding time and welding process used in the welding process of the four welding methods were compared.After comprehensive comparison, laser welding is finally determined as the best choice for these four common metal bipolar plate welding methods.

SF₆ is widely used in high-voltage insulated equipment due to its excellent insulating properties. High-voltage insulated equipment faces the problem of insulation aging during its long-term operation, which can reduce the stability and safety of energy power equipment. When partial discharge occurs, SF₆ decomposes under high-voltage and temperature into various species, such as SO₂, SO₂F₂, HF, and H₂S. Based on the first principle, a novel two-dimensional semiconductor pristine GeO₂ monomolecular layer, which has a strong adsorption capacity for SF₆ decomposed gases, is proposed in this study. Results show that this pristine GeO₂ monomolecular layer provides an ideal amount of charge transfer and work function during the adsorption process. The detection of the SF₆ decomposed gases by the novel two-dimensional GeO₂, which is a gas-sensitive semiconductor material, allows us to identify the operational status and degree of insulation of high-voltage insulated equipment, which is crucial for maintaining the reliability and stability of power systems.

Phthalonitrile resin is new type of high-temperature resistant thermosetting resin system that has attracted wide attention owing to its excellent thermal and oxidative stability, flame retardancy, and mechanical properties as well as low expansion coefficient, dielectric constant, and dielectric loss. To improve its manufacturability and meet stringent environmental performance requirements, extensive research has been conducted worldwide on the modification of the phthalonitrile system. This paper reviews the research progress of the phthalonitrile system from the aspects of molecular structure design and curing methods and mechanisms along with its applications in electrical components, adhesives, etc. Moreover, the paper discusses the opportunities and challenges faced by phthalonitrile as a new type of special functional resin material, aiming to provide insights for research in relevant fields.

The existing fatigue life prediction of asphalt mixtures is mostly based on traditional fatigue equation fitting; however, due to the multidirectionality of pavement structure and the complexity of materials, the prediction accuracy is often not satisfactory. Therefore, this article establishes an optimized neural network-based model for predicting the strength and fatigue life of asphalt mixtures using indoor indirect tensile tests and verifies the accuracy of the prediction model. The experimental results show that the accuracy of Genetic Algorithm-Back Propagation neural network to predict the fatigue mechanical properties for asphalt mixture is within 4%, which is far superior to traditional fatigue prediction equations and can be used as an effective method to obtain data on the fatigue characteristics of asphalt mixtures.

Radiative-cooling nylon is a filament with a passive radiative cooling function and is made of high-infrared-emitting inorganic particle SiO₂ and infrared-transmitting material polyamide 6 (PA6) using the industrial melt spinning method. In this study, the surface morphology, aggregated structure, chemical composition, mechanical properties, thermal properties, and surface friction properties of three types of radiative-cooling nylon filaments and an ordinary nylon filament were compared. The thermal conductivity, cool feeling at instant contact, and indoor cooling performance of the four knitted fabrics were further tested. The results show that the knitted fabric interwoven with the radiative-cooling PA6/PE sheath-core composite yarn, which is spun with SiO₂-added radiative-cooling PA6 as the skin material and polyethylene (PE) as the core material, and radiative-cooling nylon filament with circular cross-section demonstrate the best thermal conductivity and cooling performance. Its infrared thermal imaging temperature was approximately 1.8 ℃ higher than that of the ordinary nylon knitted fabric, indicating that the knitted fabric exhibits a higher infrared transmittance and better cooling effect. Thus, the radiative-cooling nylon knitted fabric possesses excellent radiative cooling performance and wearability and can be used for the development of radiative-cooling textiles.

Titanium alloys possess excellent properties like low density, high specific strength, and corrosion resistance. So, these alloys are widely used in the aerospace. With the development of aerospace, the usage ratio of such alloys is one of the criteria to measure whether the aerospace equipment is advanced or not. Because these alloys show low hardness and wear resistance, fretting wear becomes an important cause for the failure of titanium alloy parts. To improve the fretting wear resistance, laser cladding was used to produce a coating on the surface of TC4 alloy in this study. The results showed that the hardness and wear resistance of TC4 alloy were improved by laser cladding Ti-Al powder.

The flame retardants aluminum diethylphosphinate (ADP) and aluminum hypophosphite (ALHP) were introduced into natural rubber (NR) and butadiene rubber (BR) to prepare the composites. In this study, we compared the effects of the two flame retardants on the processing characteristics, physical properties, flame retardancy, mechanical properties, and abrasion resistance of the composites. Results showed that both flame retardants delayed the vulcanization of the composites, increased the Mooney viscosity. ADP exhibited a more evident increase in Mooney viscosity than ALHP. When ADP and ALHP was added at 45 phr, the limiting oxygen index (LOI) of the composites increased from 22.1% to 28.7% and 24.5%, respectively. The addition of ADP and ALHP reduced the rebound resilience of the composites, but increased hardness of it. The flame retardants reduced the tensile strength, elongation at break, and tear strength of the composites. Both flame retardants had an adverse effect on the abrasion resistance, with the abrasion loss of the composites increasing by 100% and 85% at 45 phr of ADP and ALHP, respectively. When graphene is used as a carbonization agent for ADP-containing composites, it can improve the flame retardancy without affecting the abrasion resistance. ADP and ALHP have different degrees of influence on the properties of NR/BR composites. Due to the high carbon content of ADP and volatilization by heat, the flame retardancy of NR/BR composites is greatly improved. But the tensile strength and abrasion resistance of the composites are lower than that of the ALHP-containing composites.