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With the increasing demand for thermal management in electronic devices and industrial systems, nanofluids have emerged as a research hotspot due to their superior thermal conductivity and heat transfer efficiency. Among them, CuO-H2O demonstrates excellent heat transfer performance due to its high thermal conductivity, Fe3O4-H2O offers potential for further optimization by combining thermal and magnetic properties, and Al2O3-H2O exhibits strong chemical stability, making it suitable for a wide range of applications. These three nanofluids are representative in terms of particle dispersibility, thermal conductivity, and physical properties, providing a comprehensive perspective on the impact of nanofluids on microchannel heat exchangers. This study investigates the heat transfer performance and flow characteristics of various types and volume fractions of nanofluids in microchannel heat exchangers. The results reveal that with increasing flow rates, the convective heat transfer coefficient and Nusselt number of nanofluids exhibit an approximately linear growth trend, primarily attributed to the turbulence enhancement effect caused by higher flow rates. Among the tested nanofluids, CuO-H2O demonstrates the best performance, achieving a 4.89% improvement in the heat transfer coefficient and a 1.64% increase in the Nusselt number compared to pure water. Moreover, CuO-H2O nanofluid significantly reduces wall temperatures, showcasing its superior thermal management capabilities. In comparison, the performance of Al2O3-H2O and Fe3O4-H2O nanofluids is slightly inferior. In terms of flow characteristics, the pressure drop and friction factor of nanofluids exhibit nonlinear variations with increasing flow rates. High-concentration CuO-H2O nanofluid shows a substantial pressure drop, with an increase of 7.33% compared to pure water, but its friction factor remains relatively low and stabilizes at higher flow rates. Additionally, increasing the nanoparticle volume fraction enhances the convective heat transfer performance; however, excessively high concentrations may suppress heat transfer efficiency due to increased viscosity, leading to a decrease in the Nusselt number. Overall, CuO-H2O nanofluid exhibits excellent thermal conductivity and flow optimization potential, making it a promising candidate for efficient thermal management in MCHEs. However, its application at high concentrations may face challenges related to increased flow resistance. These findings provide valuable theoretical support and optimization directions for the development of advanced thermal management technologies.

As an emerging environmentally friendly fuel, biodiesel has excellent fuel properties comparable to those of petrochemical diesel. Oleic acid methyl ester, as the main component of biodiesel, has the characteristics of high cetane number and low emission rate of harmful gases. However, the comprehensive chemical conversion pathway of oleic acid methyl ester is not clear. In this paper, the reactive force field molecular dynamics simulation (ReaxFF-MD) method is used to construct a model of oleic acid methyl ester pyrolysis and combustion system. Further, the chemical conversion kinetics process at high temperatures (2500 K–3500 K) was studied, and a chemical reaction network was drawn. The research results show that the density of the system has almost no effect on the decomposition activation energy of oleic acid methyl ester, and the activation energies of its pyrolysis and combustion processes are 190.02 kJ/mol and 144.89 kJ/mol, respectively. Ethylene, water and carbon dioxide are the dominant and most accumulated products. From the specific reaction mechanism, the main pyrolysis path of oleic acid methyl ester is the breakage of the C-C bond to produce small molecule intermediates, and subsequent transformation of the ester group radical into carbon oxides. The combustion path is the evolution of long-chain alkanes into short-carbon-chain gaseous products, and these species are further burned to form stable CO2 and H2O. This study further discusses the microscopic combustion kinetics of biodiesel, providing a reference for the construction of biodiesel combustion models. Based on this theoretical study, the understanding of free radicals, intermediates, and products in the pyrolysis and combustion of biomass can be deepened.

Borosilicate glass is currently the best comprehensive solidified material for deep disposal of high-level radioactive waste. The stability of borosilicate glass under irradiation is an important factor affecting the leakage of radioactive isotopes into the biosphere. In this study, three-component borosilicate glass (NBS) was irradiated by γ-rays at doses of 8 kGy and 800 kGy, respectively. The leaching behaviors of the irradiated samples at different absorbed doses were studied. The surface morphologies of the samples were analyzed using optical microscopy and scanning electron microscopy (SEM). The distributions of elements were analyzed using energy dispersive X-ray spectroscopy (EDS), and the elements in solution were characterized using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The results indicate that the irradiation effects on the leaching behavior of borosilicate glass occurs at certain thresholds. When the corrosion time is prolonged, the corrosion effect gradually covers the irradiation effect of the glass. Compared with 8 kGy irradiation, NBS glass samples exhibit more significant changes in elemental behavior under 800 kGy irradiation. After high-dose irradiation, the effect of corrosion on surface morphology is more significant than at low doses, and the element leaching rate of NBS glass is higher after 7 days of corrosion.

In this paper, non-equilibrium molecular dynamics simulations are used to study the interfacial heat exchange capacity of one-dimensional carbon nanotube nested structures. When the radius of the CNT substrate is increased from 1.356 to 2.712 nm, the ITC has a great enhancement from 1.340 to 2.949 nw/k. After this, we investigate the effects of overlap length, CNT length, and van der Waals interaction strength on the thermal resistance of the interface between carbon nanotubes. Firstly, we found that the nesting depth can significantly increase the ITC, and the increase in ITC is more obvious at an overlap length of 40 Å than at 30 Å. After this, the effect of length on the interfacial thermal conductivity is investigated, and the interfacial thermal conductivity is enhanced by 33.8% when the length is increased to 30 nm. Finally, the effect of van der Waals interaction strength was investigated, and the ITC increased from 1.60 nW/K to 2.71 nW/K when the scale factor was increased from 1 to 2.

In this work, phytic acid (PA) and 3-mercaptopropyltrimethoxysilane (MPTS) underwent a condensation process to produce a phytic acid–silane (abbreviated PAS) passivation solution. Additionally, it was applied to the surface of cold-rolled steel to create a composite phytic acid–silane film. The functional groups of the passivation solution were analyzed by Fourier transform infrared spectroscopy (FT-IR). The composite film was evaluated using an electrochemical workstation, scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray photoelectron spectroscopy (XPS) and pull-off test. These techniques allowed for the characterization of the film’s micromorphology, oxidation, chemical composition and adhesion strength. The results show that the PAS composite film provides higher protection efficiency compared to cold-rolled steel substrates, low phosphorus passivation films, single phytate passivation films and commercial phosphate films. This composite film also has a higher adhesion strength, which is beneficial for subsequent coating, and a possible corrosion resistance mechanism was proposed as well. The PAS layer successfully prevents the penetration of corrosive media into the cold-rolled steel surface utilizing P–O–Fe bonds, thus improving the corrosion barrier effect of the substrate.

Path planning for the complete coverage of nuclear radiation fields is necessary to ensure the radiation safety of regional operators in radiation environments. Based on a bio-inspired neural network algorithm, a complete coverage path-planning algorithm for the optimal control of the radiation dose is proposed. First, part of the terrain of the Fukushima nuclear power plant and the Monte Carlo particle transport program were used to construct the obstacle distribution and radiation dose field in a simulated nuclear radiation field. Subsequently, the Python programming language was used to conduct algorithm simulation experiments. Each grid of the simulated nuclear radiation field was defined as a neuron, and a bio-inspired neural network was established. The grid dose rate and neuronal activity were combined to achieve optimal control of radiation dose in path planning, and single, four, and eight mobile units were used for simulation experiments. The results showed that the planning path of a single mobile unit can achieve 100% coverage and a 4% coverage repetition rate and can first cover the low-dose area and delay the coverage of the high-dose area to achieve optimal control of the process and cumulative doses. The algorithm is improved via a multiunit collaborative search to increase the time efficiency of complete coverage and decrease the cumulative dose of monomers. The coverage repetition rates of four-unit and eight-unit simulations were 5.72% and 6.29%, respectively. The complete coverage times of the one-unit, four-unit, and eight-unit simulations were 30, 9, and 4 min, respectively, and the time efficiency was doubled. The maximum cumulative doses of the monomers for the one-unit, four-unit, and eight-unit simulations were 4.11×10-3, 1.28×10-3, and 0.85×10-3 mSv, respectively, which also decreased significantly. The proposed algorithm can achieve complete coverage path planning of optimal control of the process dose and cumulative dose. Moreover, the algorithm can coordinate multiunit path planning and significantly decrease the cumulative dose of monomers, which is critical for radiation protection during regional operations in a radiation environment.

The monosodium iodoacetate (MIA)-induced osteoarthritis (OA) may lead to cartilage degeneration and histopathological lesions. However, the correlation between inflammatory reaction and subchondral bone remodeling in a rodent osteoarthritic model is ambiguous. In this study, intra-articular injection of MIA was performed in 36 four-week-old specific pathogen-free male Wistar rats to induce OA. After 4 weeks of intervention, changes in intrinsic structural properties of the subchondral bones were measured, and the histological evaluation, as well as biochemical analysis, was conducted. We found that intra-articular injection of MIA increased chondrocyte apoptosis and promoted cartilage matrix degradation, such as cartilage surface defects and shallow or disappearing staining. MIA also induced inflammation, improved the expression of IL-1β, TNF-α, and matrix metalloproteinase, and decreased the expression of cartilage-specific proteins with the extension of modeling time. Meanwhile, the MIA also significantly accelerated the subchondral bone remodeling, as shown by the decreased subchondral bone density, thinning of trabeculae, disordered cartilage structure, and morphology. In conclusion, we have shown that MIA-induced rodent osteoarthritic model would cause decreased subchondral bone density, sparse trabecular bone, and other manifestations of osteoporosis accompanied by an inflammatory response, which would worsen with the progression of modeling time. Our results suggest that different phases of MIA-induced OA are associated with the changes in subchondral bone microstructure and the progression of local inflammation.

The microbial characteristics of water treated by tea polyphenols after the ultrafiltration process were studied based on the continuous disinfection performance of tea polyphenols. Metagenomics was carried out to investigate the microbial community distribution, functional characteristics, and variations after disinfection, to reveal the disinfection principle of tea polyphenols, and to provide the theoretical basis for using tea polyphenols as a disinfectant to better control the growth of microorganisms. The analysis of microbial diversity and community structure showed that the microbial diversity in water was significantly reduced after disinfection with tea polyphenols. The results of hierarchical clustering analysis showed that microbial metabolism, environmental information processing, and functional modules in genetic information processing were significantly inhibited after disinfection treatment using tea polyphenols. The tea polyphenols inhibit the growth and reproduction of microorganisms by selection on the microorganisms in the water, thus achieving the effect of bacterial inhibition. HIGHLIGHTS Tea polyphenols as disinfectants inhibit the regrowth of bacteria.; Tea polyphenols disinfection significantly reduces the diversity of microorganisms in water.; The gene function of many microorganisms in water is greatly changed after disinfection by tea polyphenols.; Tea polyphenols disinfection exerts a greater inhibitory effect on the metabolism of various nucleotides and related processes of microorganisms.;

The modern lifestyle contributes to the reduction of motor activity and muscle energy consumption in combination with neuropsychological overload. Based on this, the main purpose of the study was to identify the features of the modern students' internal motivation to engage in physical exercises. To achieve this purpose, the survey methods were used among 75 students of the Yancheng Institute of Technology, as well as the method of analysing the information received. The study identified the reasons for the lack of interest in sports on personal initiative, among which it is possible to highlight the lack of a clear example of parents, health problems, the programme of the training course, etc. It was determined that half of the surveyed students have a passive attitude to physical exercise and sports in general, since they attend physical education classes from time to time. Also, the work identified the indicators of motivation (depending on the year of studies). It was established that for the first-year students, the environment is of great importance (52% of respondents), and for the third-year students – a healthy lifestyle (54 % of respondents). Using the hierarchy analysis method, seven most significant motivational criteria were selected, which allowed determining that communication motives are the most important. This is due to the fact that adaptation in the team is of primary importance for junior students. The results of the experiment indicate that there is an indissoluble connection between internal and external factors of motivation in physical exercise. Only an integrated approach contributes to the inner interest of the younger generation. The results of the paper are of primary importance for university teachers during the preparation of curricula, which will contribute to the interest of students in the lesson

BackgroundRecently, global concerns regarding the illicit transportation and trafficking of nuclear materials and other radioactive sources have increased, leading to increased demands for efficient and rapid security and non-proliferation technologies. The International Atomic Energy Agency's Incident and Trafficking Database has reported 3 235 confirmed incidents involving nuclear and other radioactive materials out of regulatory control from 1993 to 2017. Of these incidents, 278 are associated with trafficking or malicious use of materials such as highly enriched uranium, plutonium, and plutonium-beryllium neutron sources. Therefore, developing depth-of-interaction detector for neutrons and gamma rays is important for effective control of nuclear and radiation materials at national and international cross points such as borders, ports, and airports.PurposeThis study aims to design a depth-of-interaction detector for neutrons and gamma rays and characterize its performance.MethodsHereby, an EJ276 plastic scintillator (Φ3 cm× 15 cm) coupled with two silicon photomultipliers (SiPMs) in both sides was designed as a depth-of-interaction detector for neutrons and gamma rays. The short gate time was optimized to achieve better neutron/gamma-ray discrimination, and the reaction position was determined based on the amplitude ratio and time of flight (TOF) difference between signals from two sides. Finally, Am-Be neutron source and 137Cs γ source were applied to detector parameter optimization and resolution calibration for performance characterization.ResultsExperimental results demonstrate that good consistency in the detection efficiency of the detector at different incident positions, where the resolution of the one-dimensional reaction position is approximately 4.4 cm.ConclusionsThe designed depth-of-interaction detector can be used toreplace detector arrays in neutron scatter cameras and coded-aperture imagers to reduce costs and system complexity.

There exist complex chemical reactions and phase transition processes—melting and solidification—in the iron ore sintering process. This will result in bed shrinkage and has an important impact on the sintering process and sinter properties. Numerical simulation was carried out in this paper, and it is based on the porous medium and double-energy equation model, coupled with the porosity shrinkage model. The influence of bed shrinkage on the sintering process was explored and revealed by comparing the calculation results, considering and neglecting bed shrinkage. The results indicate that bed shrinkage has less influence on the highest temperature in the area above 0.15 m of the sintered bed but it severely decreases by about 60 K below 0.15 m. Bed shrinkage makes the thickness of the combustion zone reduce by about 1.367, 1.398, and 4.367 cm at 500, 1000, and 1500 s, respectively. Bed shrinkage reduces the vertical sintering velocity, and the highest temperature of flue gas decreased by 77 K approximately; in addition, it also has a delay of 50 s to reach the highest temperature. The volume concentration of SO2 in the flue gas remains unchanged considering bed shrinkage, but it has a delay of about 50 s to reach the maximum value.

The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to find optimal mixing scheme of Zhaotong lignite, Fuyuan bituminous coal, and Xiaolongtan lignite in terms of combustion performance. Comparative evaluation of different mixing schemes is also conducted, where the flammability index, comprehensive combustion characteristic index, burnout temperature, and economic costs can be used to measure the advantages and disadvantages of different mixing schemes with different parameters. Through analysis and optimization, it is found that when the lignite of Xiaolongtan, lignite of Zhaotong, and bituminous coal of Fuyuan are mixed with a ratio 2 : 1 : 2, the mixed coal has the best performance; when the lignite of Xiaolongtan, lignite of Zhaotong, and bituminous coal of Fuyuan are mixed with a ratio 0 : 2 : 1, the mixed coal has the worst performance.

An optimal prediction model for flow boiling heat transfer of refrigerant mixture R245fa inside horizontal smooth tubes is proposed based on the GRNN neural network. The main factors strongly affecting flow boiling such as mass flux rate (G), heat flux (q), quality of vapor-liquid mixture (x), evaporation temperature (Tev), and tube inner diameter (D) are used as the inputs of the model and the flow boiling heat transfer coefficient (h) as the output. Neural network model is used to optimize the prediction of flow boiling heat transfer coefficient of R245fa in horizontal light pipe through training and learning. The prediction results are in good agreement with the experimental results. For the network model of heat transfer, the average deviation is 7.59%, the absolute average deviation is 4.89%, and the root mean square deviation is 10.51%. The optimized prediction accuracy of flow boiling heat transfer coefficient is significantly improved compared with four frequently used conventional correlations. The simulation results reveal that the modeling method based on R245fa neural network is feasible to calculate the flow boiling heat transfer coefficient, and it may provide some guidelines for the optimization design of tube evaporators for R245fa.

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.