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The antibacterial behaviour of suspensions of zinc oxide nanoparticles (ZnO nanofluids) against E. Coli has been investigated. ZnO nanoparticles from two sources are used to formulate nanofluids. The effects of particle size, concentration and the use of dispersants on the antibacterial behaviour are examined. The results show that the ZnO nanofluids have bacteriostatic activity against E. coli. The antibacterial activity increases with increasing nanoparticle concentration and increases with decreasing particle size. Particle concentration is observed to be more important than particle size under the conditions of this work. The results also show that the use of two types of dispersants (Polyethylene Glycol (PEG) and Polyvinylpyrolidone (PVP)) does not affect much the antibacterial activity of ZnO nanofluids but enhances the stability of the suspensions. SEM analyses of the bacteria before and after treatment with ZnO nanofluids show that the presence of ZnO nanoparticles damages the membrane wall of the bacteria. Electrochemical measurements using a model DOPC monolayer suggest some direct interaction between ZnO nanoparticles and the bacteria membrane at high ZnO concentrations.

Developing highly active catalysts for hydrogen evolution reaction is vital for large-scale and efficient production of hydrogen through water splitting. In this study, we reported scale-up TiO2/LaFeO3 composite catalysts with multiple heterojunctions synthesized via facile solid-phase reaction and investigated their hydrogen evolution reaction (HER) performance in both acidic and alkaline solutions. To be excited, under AM1.5 simulated sunlight irradiation, the working electrode decorated by TiO2/LaFeO3 presents a higher current density with 10 mV/cm2 at 0.55 V in a 0.5 M H2SO4 solution, suppressing the corresponding performance with the occasion of pure electrocatalysis. Importantly, the hydrogen evolution efficiency can reach 7.62 mmol h−1 cm−2, overwhelming approximately 5.3 times higher than that of commercial P25 (1.43 mmol h−1 cm−2) with exceptional stability via imposing simulated sunlight and a bias of 500 mV. Therefore, such excellent catalysts could serve as an alternative to reach remarkable HER performance via fossil fuels free technology, potentially making contribution toward the goal of global “carbon neutral” by 2050.

Due to the advantages of high energy density and improved safety properties, Li metal batteries with gel polymer electrolyte (GPE) have drawn much attention in recent years. Herein, a novel gel thermoplastic polyurethane (TPU) electrolyte filled with ultrafine hydrophilic–lipophilic TiO2 nanoparticles was successfully prepared by using a phase separation technology. The gel electrolyte can protect the battery against the leakage of liquid electrolyte by means of its strong interaction with Li+ and solvents. This kind of GPE shows an ionic conductivity of 1.59 mS cm−1 and an electrochemical window of 4.3 V (vs. Li/Li+) at room temperature. Robust TiO2 nanoparticles embedded into TPU matrix can effectively block dendrite puncture and, besides, greatly improve the thermal stability of GPE. The cycling performance, rate capability and mechanical properties guarantee the reliability of the as-prepared GPE in Li-ion batteries.

The safety performance and structural stiffness of a rim, which is the main load-bearing structure of the loader during operation, influence the overall performance, stability, and braking capabilities of the machine. In the industry, researchers are currently pursuing lightweight and high-strength rims as a primary objective. A low weight not only enhances machinery fuel efficiency but also aligns with societal demands for sustainable development, energy conservation, and emission reduction. In this article, multiobjective optimization analysis on rims composed of three different materials is performed, and the relationships between various optimization parameters and target parameters are established using the results of response surface construction. Multiobjective genetic algorithms are utilized to derive various optimization plans, which are subsequently evaluated through static analysis, fatigue analysis, and weight loss analysis. The final optimization plan is determined based on the calculation results while considering production costs. Field tests are conducted on the optimized rims under various working conditions to verify the test results, evaluate the reliability of the finite element analysis results, and confirm the safety of the optimized rim.

In the development trend of intelligent and high-performance construction machinery, the dual-spool electro-hydraulic valve, as a new-generation core control element, directly affects the operation accuracy and energy-efficiency level of construction machinery. The standard linear extended state observer (LESO) produces relatively serious peaks as the system order increases, which leads to the degradation of the observer’s performance and affects the controller’s accuracy. To solve this problem, this paper innovatively proposes an output feedback control strategy for a cascaded structure observer for the dual-spool electro-hydraulic valve. This paper designs an output feedback controller based on the cascaded structure observer. The uniform exponential stability (USE) criterion ensures that the tracking error of the observer for the system state is bounded. The expected load pressure is constructed based on the expected trajectory to replace the actual load pressure, avoiding the influence of the nonlinear coupling between the load pressure and the input signal on the control system. Finally, a stable output feedback controller is obtained based on the backstepping control method and Hurwitz polynomial stability analysis. This study first applies the cascaded structure observer to the field of dual-spool electro-hydraulic valve control, providing a new theoretical framework and technical path for the high-precision control of the hydraulic system of construction machinery. Theoretical analysis shows that compared with the standard LESO, the cascaded structure observer can significantly reduce the online computational burden and effectively suppress the peak phenomenon, providing stronger estimation ability. Finally, a large number of simulation examples verify the effectiveness and superiority of the output feedback controller based on the cascaded structure observer. In all four test scenarios, the average tracking error of C1 (the output feedback controller designed based on the cascaded structure linear extended state observer) is about 5.1%, the average tracking error of C2 (the output feedback controller designed based on the standard structure linear extended state observer) is about 7.8%, and the average tracking error of C3 (the high-gain PID controller) is about 19.2%. The average control accuracy of the designed C1 controller is improved by 2.7% and 14.1% compared with C2 and C3, respectively. In terms of the estimation of external disturbances, the average error of C1 is 14% and the average error of C2 is 29.6%. The estimation accuracy of the former is improved by 15.6% compared with the latter.

In recent years, Aksu apple has become popular with consumers because of its unique texture and taste. At present, maintaining quality during storage is the key problem with the apples in the Aksu region. 1-Methylcyclopropene (1-MCP) can delay fruit senescence, so is widely used in fruit preservation. However, many factors affect the preservation effect of 1-MCP. The effects of 1-MCP concentration (0 µL·L−1, 1 µL·L−1, 3 µL·L−1, 5 µL·L−1, and 8 µL·L−1) and postharvest application time (0, 1 and 2 d after harvest) on the quality of stored apple were studied. It was found that 1 µL·L−1 1-MCP was more beneficial in improving the quality of stored apples, reduced the respiration intensity and decay rate, increased the fruit firmness and total soluble solid content, and reduced the relative content of ester volatile aroma components. In addition, 1-MCP treatment applied at different postharvest times also affected the sensory quality and flavor of apples. The effect of 1-MCP treatment immediately after harvest was better.

Polyimide@graphene oxide (PI@GO) composite fabricated from electrospinning method is evaluated as the separator for Li-ion batteries operated at high temperature. The thermal stability, mechanical properties, and ionic conductivity of PI electrospun separators are improved by using graphene oxide as an enhancer. The operating temperature of the as-prepared separators can be propelled up to 280 °C without evident performance degradation. The LiFePO 4 /C cell with PI@GO separator can sustain the open circuit voltage up to 60 min at 160 °C, indicating the composite separators are reliable separators for LIBs operated at high temperature. The thermal stability, mechanical properties, and electrochemical performance of PI separators were improved by a modification of GO. Highlights PI@GO separators have been fabricated from electrospun technology; The composite separator exhibits high thermal stability up to 280 °C; The high ionic conductivity of 5.63 mS·cm −1 can be obtained; The mechanical properties of PI separator can be improved by the addition of GO; The PI@GO separator exhibits anodic stability up to 5.14 V.

Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part layout can improve work efficiency, save time, and reduce the labor intensity and production costs for the company. In this study, we studied a layout optimization algorithm, focusing on rectangular parts in the material preparation process. A mathematical model for the rectangular layout problem was constructed, and a hybrid genetic whale optimization algorithm is proposed that is a combination of the whale optimization algorithm and the genetic algorithm. Based on the “one size fits all” layout strategy, the materials are divided into strips, which are further divided into stacks, serving as the positioning strategy to determine the positional relationships of the parts. Test cases and actual engineering data were used to compare the layouts generated using different algorithms. The results show that the genetic whale algorithm proposed in this paper results in a high utilization rate and is highly effective.

Fire retardancy for textiles is important to prevent the rapid spread of fire and minimize damage to property and harm to human life. To infer fire‐resistance on textile materials such as cotton or nylon, chemical coatings are often used. These chemicals are usually toxic, and economically and environmentally unsustainable, however, some naturally produced protein‐based fire retardants could be an alternative. A biofilm protein from Bacillus subtilis (BslA) was identified and recombinantly expressed in Escherichia coli with a double cellulose binding domain. It was then applied to a range of natural and synthetic fabric materials. A flame retardancy test found that use of BslA reduced fire damage by up to 51% and would pass fire retardancy testing according to British standards. It is therefore a viable and sustainable alternative to current industrial fire‐retardant coatings.

Accurately predicting the drilling efficiency of rotary drilling is the key to achieving intelligent construction. The current types of principle analysis (based on traditional interactive experimental methods) and efficiency prediction (based on simulation models) cannot meet the requirements needed for the efficient, real-time, and accurate drilling efficiency predictions of rotary drilling rigs. Therefore, we adopted a method based on machine learning to predict drilling efficiency. The extremely complex rock fragmentation process in drilling conditions also brings challenges to predicting drilling efficiency. Therefore, this article went through a combination of mechanism and data analysis to conduct correlation analysis and to clarify the drilling characteristic parameters that are highly correlated with drilling efficiency, and it then used them as inputs for machine learning models. We propose a rotary drilling rig drilling efficiency prediction model based on the GA-BP neural network to construct an accurate and efficient drilling efficiency prediction model. Compared with traditional BP neural networks, it utilizes the global optimization ability of a genetic algorithm to obtain the initial weights and thresholds of a BP neural network in order to avoid the defect of ordinary BP neural networks, i.e., that they easily fall into local optimal solutions during the training process. The average prediction accuracy of the GA-BP neural network is 93.6%, which is 3.1% higher than the traditional BP neural network.