Search Results (28,312)

The influence of the copper (Cu) content on the corrosion resistance of 3.5%Ni low-carbon weathering steel was investigated using periodic dry–wet cycle accelerated corrosion tests. The mechanical properties of the steels were assessed via tensile and low-temperature impact tests, while corrosion resistance was evaluated based on weight loss measurements. Surface oxide layers were characterized using three-dimensional laser confocal microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and electrochemical methods. Electron probe microanalysis (EPMA) was employed to examine the cross-sectional morphology of the oxide layer after 72 h of accelerated corrosion tests. The results indicate that the solution state of Cu increased the strength of 3.5%Ni steels but significantly damaged the low-temperature toughness. As the Cu content increased from 0.75% to 1.25%, the corrosion rate decreased from 4.65 to 3.74 g/m² h. However, when there was a further increase in the Cu content to 2.15%, there was little decrease in the corrosion rate. With the increase in the Cu content from 0.75% to 2.15%, the surface roughness of 3.5%Ni weathering steel after corrosion decreased from 5.543 to 5.019 μm, and the corrosion behavior was more uniform. Additionally, the α/γ protective factor of the oxide layer of the surface layer increased from 2.58 to 2.84 with an increase in the Cu content from 0.75% to 1.25%, resulting in the oxide layer of the surface layer being more protective. For 1.25%Cu steel, the corrosion current density of rusted samples is lower (ranging from 1.2609 × 10⁻⁴ A/cm² to 3.7376 × 10⁻⁴ A/cm²), and the corrosion potential is higher (ranging from −0.85544 V to −0.40243 V). Therefore, the rusted samples are more corrosion resistant. The Cu in the oxide layer of the surface layer forms CuO and CuFeO₂, which are helpful for increasing corrosion resistance, which inhibits the penetration of Cl⁻. Full article

This study experimentally validates a solar-thermal desalination system equipped with predictive feedwater control guided by real-time solar forecasting. Unlike conventional systems that react to temperature changes, the proposed approach proactively adjusts feedwater flow in anticipation of solar variability. To assess environmental and financial sustainability, the study integrates this control logic with a full Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA). Field testing in a high-temperature, arid region demonstrated strong performance, achieving a Global Warming Potential (GWP) of 1.80 kg CO₂-eq/m³ and a Levelized Cost of Water (LCOW) of $0.88/m³. Environmental impacts were quantified using OpenLCA and ecoinvent datasets, covering climate change, acidification, and eutrophication categories. The TEA confirmed economic feasibility, reporting a positive Net Present Value (NPV) and an Internal Rate of Return (IRR) exceeding 11.5% over a 20-year lifespan. Sensitivity analysis showed that forecast precision and TES design strongly influence both environmental and economic outcomes. The integration of intelligent control with simplified thermal storage offers a scalable, cost-effective solution for off-grid freshwater production in solar-rich regions. Full article

Enhanced Geothermal Systems (EGS) require thermochemically stable proppant materials capable of sustaining fracture conductivity under harsh subsurface conditions. This study systematically investigates the response of commercial proppants to coupled thermo-hydro-chemical (THC) effects, focusing on chemical stability and microstructural evolution. Four proppant types were evaluated: an ultra-low-density ceramic (ULD), a resin-coated sand (RCS), and two quartz-based silica sands. Experiments were conducted under simulated EGS conditions at 130 °C with daily thermal cycling over a 25-day period, using diluted site-specific Utah FORGE geothermal fluids. Static batch reactions were followed by comprehensive multi-modal characterization, including scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and micro-computed tomography (micro-CT). Proppants were tested in both granular and powdered forms to evaluate surface area effects and potential long-term reactivity. Results indicate that ULD proppants experienced notable resin degradation and secondary mineral precipitation within internal pore networks, evidenced by a 30.4% reduction in intragranular porosity (from CT analysis) and diminished amorphous peaks in the XRD spectra. RCS proppants exhibited a significant loss of surface carbon content from 72.98% to 53.05%, consistent with resin breakdown observed via SEM imaging. While the quartz-based sand proppants remained morphologically intact at the macro-scale, SEM-EDS revealed localized surface alteration and mineral precipitation. The brown sand proppant, in particular, showed the most extensive surface precipitation, with a 15.2% increase in newly detected mineral phases. These findings advance understanding of proppant–fluid interactions under low-temperature EGS conditions and underscore the importance of selecting proppants based on thermo-chemical compatibility. The results also highlight the need for continued development of chemically resilient proppant formulations tailored for long-term geothermal applications. Full article

AISI 347H stainless steel is widely used in high-temperature environments due to its excellent creep strength and oxidation resistance; however, its corrosion performance remains highly sensitive to thermal oxidation, and the effects of thermal history on its passive film stability are not yet fully understood. This study addresses this knowledge gap by systematically investigating the influence of solution treatment on the corrosion and oxidation resistance of AISI 347H stainless steel. The specimens were subjected to solution heat treatment at 1050 °C, followed by air cooling, and then evaluated through electrochemical testing, high-temperature oxidation experiments at 550 °C, and multiscale surface characterization techniques. The solution treatment refined the austenitic microstructure by dissolving coarse Nb-rich precipitates, as confirmed by SEM and EBSD, and improved passive film integrity. The stabilizing effect of Nb also played a critical role in suppressing sensitization, thereby enhancing resistance to intergranular attack. Electrochemical measurements and EIS analysis revealed a lower corrosion current density and higher charge transfer resistance in the treated samples, indicating enhanced passivation behavior. ToF-SIMS depth profiling and oxide thickness analysis confirmed a slower parabolic oxide growth rate and reduced oxidation rate constant in the solution-treated condition. At 550 °C, oxidation was suppressed by the formation of compact, Cr-rich scales with dual-distributed Nb oxides, effectively limiting diffusion pathways and stabilizing the protective layer. These findings demonstrate that solution treatment is an effective strategy to improve the long-term corrosion and oxidation performance of AISI 347H stainless steel in harsh service environments. Full article

This study explores the valorization of agro-industrial by-products—riceberry broken rice (RBR) and soybean meal (SBM)—as cost-effective substrates for enhancing exopolysaccharide (EPS) production by Bacillus tequilensis PS21. Eight Bacillus strains were screened, and B. tequilensis PS21 demonstrated the highest EPS yield (2.54 g/100 mL DW). The EPS displayed a strong antioxidant capacity with 65.5% DPPH and 80.5% hydroxyl radical scavenging, and a FRAP value of 6.51 mg Fe²⁺/g DW. Antimicrobial testing showed inhibition zones up to 10.07 mm against Streptococcus agalactiae and 7.83 mm against Staphylococcus aureus. Optimization using central composite design (CCD) and the response surface methodology (RSM) revealed the best production at 5% (w/v) RBR, 3% (w/v) SBM, pH 6.66, and 39.51 °C, yielding 39.82 g/L EPS. This EPS is a moderate-molecular-weight (11,282 Da) homopolysaccharide with glucose monomers. X-ray diffraction (XRD) showed an amorphous pattern, favorable for solubility in biological applications. Thermogravimetric analysis (TGA) demonstrated thermal stability up to ~250 °C, supporting its suitability for high-temperature processing. EPS also exhibited anticancer activity with IC₅₀ values of 226.60 µg/mL (MCF-7) and 224.30 µg/mL (HeLa) at 72 h, reduced colony formation, inhibited cell migration, and demonstrated anti-tyrosinase, anti-collagenase, and anti-elastase effects. This study demonstrates the successful valorization of agro-industrial by-products—RBR and SBM—for the high-yield production of multifunctional EPS with potent antioxidant, antimicrobial, and anticancer properties. The findings highlight the sustainable potential of these low-cost substrates in supporting the development of green and value-added bioproducts, with promising utilizations across the food, pharmaceutical, and cosmetic sectors. Full article

Asphalt pavement cracking is an important factor affecting its service life. Under certain conditions, the self-healing behavior of asphalt itself can repair pavement cracks. However, the self-healing ability of asphalt itself is limited. In order to strengthen the self-healing ability of asphalt, the microcapsule wrapped with a repair agent is pre-mixed into the asphalt mixture. When the crack occurs and spreads to the surface of the microcapsule, the microcapsule ruptures and the healing agent flows out to realize the self-healing of the crack. Current microcapsules are mostly prepared with healing agents and bio-oil as core materials, and their high-temperature resistance to rutting is poor. While the epoxy resin contains a three-membered cyclic ether, it can undergo ring-opening polymerization to bond and repair the asphalt matrix. In addition, research on microcapsules mainly focuses on the self-healing properties of microcapsule-modified asphalt. In fact, before adding microcapsules to asphalt to improve its self-healing performance, it is necessary to ensure that the asphalt has a good road performance. On this basis, the self-healing performance of asphalt is improved, thereby extending the service life of asphalt pavement. Therefore, two-component epoxy self-healing microcapsules (E-mic and G-mic) were first prepared in this paper. Then, a temperature scanning test, rheological test of bending beams, and linear amplitude scanning test were, respectively, conducted for the microcapsule/asphalt to evaluate its road performance, including the high-temperature performance, low-temperature crack resistance, and fatigue performance. Finally, the self-healing performance of microcapsules/asphalt was tested. The results showed that the self-developed epoxy self-healing microcapsules were well encapsulated and presented as spherical micron-sized particles. The average particle size of the E-mic was approximately 23.582 μm, while the average particle size of the G-mic was approximately 22.440 μm, exhibiting a good normal distribution. In addition, they can remain intact and unbroken under high-temperature conditions. The results of road performance tests indicated that the microcapsule/asphalt mixture exhibits an excellent high-temperature resistance to permanent deformation, low-temperature crack resistance, and fatigue resistance. The self-healing test demonstrated that the microcapsule/asphalt exhibited an excellent self-healing performance. When the microcapsule content was 4%, the self-healing rate reached its optimal level of 67.8%, which was 149.2% higher than that of the base asphalt. Full article

Interfacial solar evaporation has emerged as a promising strategy for freshwater production, where 3D evaporators offer distinct advantages in heat management and salt rejection. Freeze–thaw cycling is a widely adopted fabrication method for 3D hydrogel evaporators, yet the impact of preparation conditions (e.g., freezing temperature) on their evaporation performance remains poorly understood, hindering rational optimization of fabrication protocols. Herein, we report the fabrication of chitosan-based hydrogel evaporators via freeze–thaw cycles at different freezing temperatures (−20 °C, −40 °C, and −80 °C), leveraging its low cost and environmental friendliness. Characterizations of crosslinking density and microstructure reveal a direct correlation between freezing temperature and network porosity, which significantly influences evaporation rate, photothermal conversion efficiency, and anti-salt performance. It is noteworthy that the chitosan hydrogel prepared at −80 °C demonstrates an excellent evaporation rate in high-salinity environments and exhibits superior salt resistance during continuous evaporation testing. Long-term cyclic experiments indicate that there was an average evaporation rate of 3.76 kg m⁻² h⁻¹ over 10 cycles, with only a 2.5% decrease observed in the 10th cycle. This work not only elucidates the structure–property relationship of freeze–thaw fabricated hydrogels but also provides a strategic guideline for tailoring evaporator architectures to different salinity conditions, bridging the gap between material design and practical seawater desalination. Full article

Efficient lubrication lowers friction, wear, and energy losses in automotive drivetrain components. Advanced lubricants are key to sustainable transportation performance, durability, and efficiency. This study analyzes the tribological performance of Group III base oil with CuO and ZnO nanoadditive mixtures. These additives enhance the performance of Group III base oils, making them highly relevant for automotive lubricant applications. An Optimol SRV5 tribometer performed ball-on-disk sliding contact tests with 100Cr6 steel specimens subjected to a 50 N force and a temperature of 100 °C. The test settings are designed to mimic the boundary and mixed lubrication regimes commonly seen in the automobile industry. During the tests, the effect of nanoparticles on friction was measured. Microscopic wear analysis was performed on the worn specimens. The results demonstrate that adding 0.3 wt% CuO nanoparticles to Group III base oil achieves a 19% reduction in dynamic friction and a 47% decrease in disk wear volume compared to additive-free oil. Notably, a 2:1 CuO-to-ZnO mixture produced synergy, delivering up to a 27% friction reduction and a 54% decrease in disk wear. The results show the synergistic effect of CuO and ZnO in reducing friction and wear on specimens. This study highlights the potential of nanoparticles for lubricant development and automotive applications. Full article

Background/Objective: The criteria for selecting and managing oral moisturizers have yet to be clearly defined. The purpose of this study was to examine the effects of storage temperature and storage time on the pH and oxidation–reduction potential (ORP) of oral moisturizers. Methods: The pH and ORP of 20 commercially available oral moisturizers stored at 37 °C, 25 °C, and 4 °C were measured immediately after opening (0M) and at 1 month (1M), 3 months (3M), and 6 months (6M) post-opening. The data were analyzed using Fisher’s exact test based on the critical pH of enamel and dentin, two-way repeated-measures analysis of variance (ANOVA), and Pearson’s correlation analysis. Results: At 0M, 25% of the products had pH values below the critical threshold for enamel, and 75% fell below that for dentin. The distribution of products significantly differed based on critical pH thresholds (p < 0.05). The two-way repeated-measures ANOVA showed that the pH was significantly affected by the storage time (p < 0.05), but not by the storage temperature or its interaction with time. By contrast, ORP was significantly affected by the storage temperature, storage time, and their interaction (p < 0.05). The Pearson’s correlation analysis revealed a significant negative correlation between pH and ORP at 4 °C (6M), 25 °C (1M, 3M, 6M), and 37 °C (all time points) (p < 0.05). Conclusions: Many oral moisturizers exhibit acidic pH values, indicating that products with a pH of 6.7 or higher should be selected. Additionally, to minimize degradation, oral moisturizers should be stored at 4 °C and used within 3 months of opening. Full article

This study provides a novel experimental setup and methodology for the quantitative investigation of evaporation-induced foaming behaviors in a polymer/small-molecule solution system (PSMS). In traditional dynamic test methods, it is difficult to precisely describe the evaporation-induced foaming process of a multicomponent solution because the concentration of light components in solution continuously decreases during ebullition, causing undesired changes in foaming behavior. In this study, a precisely controlled condensation reflux module was introduced into the setup to maintain pressure, temperature, and concentration of the PSMS at constant levels during the entire ebullition process, allowing dynamic test methods to quantify the evaporation-induced foamability. With this newly proposed device, experimental data of typical PSMS, polyolefin elastomer (POE)/n-hexane solution system, were obtained and modeled to illustrate the foam growth profile, thereby characterizing the dynamic foaming process based on a logistic growth function. The corresponding dimensionless number Σevap was calculated to evaluate evaporation-induced foam stability by analyzing the foam growth profile under varying pressure, concentration, and energy input levels. Furthermore, given that the PSMS represents a highly non-ideal system, the bubble nucleation rate J was modified in this work by introducing a correction coefficient δ to account for the non-ideal effects of macromolecules present in solutions. Additionally, another correction coefficient λ was incorporated into the Gibbs free energy term to adjust for supersaturation of liquid during nucleation. The experiment’s data align well with the modified bubble nucleation rate mechanism proposed herein. Full article

The development in coastal engineering and maritime transport demands accurate wave height prediction. In this study, hybrid deep learning models, including CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, are employed to develop regional multivariate wave prediction models that incorporate multiple features, such as wave height, wind stress, water depth, pressure, and sea surface temperature (SST), for the entire Bay of Bengal area. Sensitivity analysis is performed to evaluate the accuracy using statistical metrics, such as the correlation coefficient, RMSE, and MAE. The findings demonstrate that regional multivariate models offer satisfactory results for the entire Bay of Bengal region. The multivariate model performs better compared to the univariate model as the forecast horizon increases. Performance assessment of each environmental factor, employing the integrated gradient method, reveals that sea surface temperature has the most significant influence, while wind stress is the least dominant factor in the wave prediction model. Among the tested models, the CNN-BiGRU has superior performance with a correlation of 0.9872, an RMSE of 0.1547, and an MAE of 0.1005 for the 3 h prediction and is proposed as the optimal model. This study contributes to assessing the contribution of each environmental feature and improving the accuracy of regional wave prediction. Full article

E36 steel, widely used in shipbuilding and offshore structures, offers moderate strength and excellent low-temperature toughness. However, its welded joints are highly susceptible to fatigue failure. Cracks typically initiate at weld toes or within the heat-affected zone (HAZ), severely limiting the fatigue life of fabricated components. Traditional life prediction methods are complex, inefficient, and lack accuracy. This study proposes a machine learning (ML) framework for efficient fatigue life prediction of E36 welded joints. Welded specimens using SQJ501 filler wire on prepared E36 steel established a dataset from 23 original fatigue test data points. The dataset was expanded via Z-parameter model fitting, with data scarcity addressed using SMOTE. Pearson correlation analysis validated data relationships. After grid-optimized training on the augmented data, models were evaluated on the original dataset. Results demonstrate that the machine learning models significantly outperformed the Z-parameter formula (R² = 0.643, MAPE = 16.15%). The artificial neural network (R² = 0.972, MAPE = 4.45%) delivered the best overall performance, while the random forest model exhibited high consistency between validation (R² = 0.888, MAPE = 6.34%) and testing sets (R² = 0.897), with its error being significantly lower than that of support vector regression. Full article

Objectives: The aim of this study was to determine the differential temperature produced on ceramic implants using laser irradiation on a pulsed setting of intrabony defects in vitro. Methods: A ceramic (Zr) dental implant (Zeramex, 4.8 × 12 mm) was placed into a bovine bone block. A three-wall intrabony defect (6 × 4 × 3 mm) was created to mimic an osseous peri-implant defect. Thermocouples were placed on the apical and coronal areas to measure temperature changes (∆T) during 60 s of laser irradiation. The bovine block was heated to 37 °C, and the defect walls were irradiated with the CO₂ and Er,Cr:YSGG laser. The settings used were pulsed mode for both lasers, with 30 Hz and 1.5 W for the Er,Cr:YSGG laser and 70 Hz and 2 W for the CO₂ laser. The same laser settings were repeated at room temperature (RT, 23 °C). Twenty trials were performed for each experimental group at room and body temperature for assessment of ∆T. Paired t-test were used to compare the measurements between 37 °C and 23 °C for the Er,Cr:YSGG, and CO₂ laser, respectively. Results: The CO₂ laser resulted in the highest ∆T (°C) at the coronal (15.22 ± 0.28/8.82 ± 0.21) and apical (5.84 ± 0.14/2.30 ± 0.28) level when this laser was used in both room temperature and body temperature, respectively. The highest ∆T (°C) for the Er,Cr:YSGG laser at body temperature at the coronal thermocouple was 7.64 ± 0.55, while for the CO₂ laser, at body temperature was 8.82 ± 0.21. Conclusion: Within the limitations of our study, the use of CO₂ laser and Er,Cr:YSGG laser on peri-implant defects generally appears to be safe in treating peri-implant defects around zirconia implants in vitro. Full article

With increasing popularity of food delivery services, the microbial safety of transported meals should be ensured. An effect of the type of a meal (cooked rice; mashed potatoes; mushroom sauce), inner primary packaging (sugarcane bagasse [SB] tray; polypropylene [PP] tray), secondary container (polyester/polyethylene foam/aluminum foil [PPA] bag; PP box) on the time interval of the internal hot ready-to-eat (RTE) meal temperature decrease to the value critical for Bacillus cereus growth (40 °C) was tested during a simulated delivery; in aliquot samples of the same meals, B. cereus growth was quantified presuming a natural contamination of the meals. Type of a meal had no effect on the tested time interval (p > 0.05). Packaging a meal in the PP tray as compared to the SB tray and inserting primary trays into the PP box instead of PPA bag delayed (p < 0.05) the internal meal temperature decrease by 50 and 15 min, respectively. Average B. cereus counts in the naturally contaminated meals after the four-hour culturing at 40 °C was 2.99 log CFU·g⁻¹. It was concluded that a hot RTE meal delivered up to four hours under the tested conditions is not likely to facilitate B. cereus growth above unacceptable levels. Full article

With the continuous expansion of data center scales worldwide, the problem of energy consumption has become increasingly prominent. To address the multi-parameter control challenge in environmental temperature regulation for large data center computer rooms, achieve precise control of hot-aisle temperatures in data centers, and reduce energy waste, this paper designs a multi-parameter model-free adaptive control (MMFAC) algorithm suitable for computer room environmental temperatures. The algorithm integrates the model-free adaptive control (MFAC) algorithm with a weight matrix to perform scaling transformations. Considering the large parameter space of the MFAC controller and the dynamic complexity of data center temperature control systems, compact-form dynamic linearization (CFDL) technology and optimization mathematical methods are used to simplify the parameter identification of the pseudo-Jacobian matrices and the calculation of control quantities for the regulation devices. Simulation experiments based on measured data from a data center show that the proposed algorithm can calculate control quantities for equipment such as air conditioners according to real-time environmental parameter measurements and drive each device based on these control quantities. Meanwhile, the algorithm can reduce errors in key parameters by adjusting the weight matrix. Comparative tests with other control algorithms show that the algorithm has faster response in temperature control and smaller control errors, verifying the effectiveness and application prospects of the algorithm in data center temperature control. Full article