Search Results (5,186)

The conversion of CO₂ into CH₄ through the Sabatier reaction is one of the key processes that can reduce CO₂ emissions into the atmosphere. This work aims to develop Ni-Al aerogel-based thermo-photocatalysts with large specific surface areas prepared using a sol–gel method and subsequent supercritical drying in CO₂. Different Al/Ni molar ratios were selected for the development of the catalysts, characterized using ICP-OES, N₂ adsorption–desorption isotherms, XRD, H₂-TPR, TEM, UV-Vis DRS, and XPS techniques. Thermo-photocatalytic activity tests were performed in a photoreactor with two different light sources (λ = 365 nm, λ = 470 nm) at a temperature range from 300 °C to 450 °C and a pressure of 10 bar. The catalyst with the highest Ni loading (AG 1/3) produced the best catalytic results, reaching CO₂ conversion and CH₄ selectivity levels of 82% and 100%, respectively, under visible light at 450 °C. In contrast, the catalysts with the lowest nickel loading produced the lowest results, most likely due to their low amounts of active Ni. These results suggest that supercritical drying is an efficient method for developing active thermo-photocatalysts with high Ni dispersion, suitable for Sabatier reactions under mild reaction conditions. Full article

Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. Full article

The selection of process parameters for an ultra-high-pressure water jet directly affects the adhesion ability of the subsequent coating on the ship’s surface. This study investigates the effect of jet pressure, standoff distance, and nozzle traverse speed on the surface roughness of grade-A marine steel, aiming to optimize the process parameters and improve the quality of surface treatment. Based on single-factor experiments and orthogonal experiments, a three-factor, three-level experimental design was employed, considering jet pressure, standoff distance, and nozzle traverse speed. Scanning electron microscopy (SEM) and a confocal microscope were used to analyze the surface morphology and roughness of grade-A marine steel. The experimental results proved that surface roughness exhibited a nonlinear relationship with jet pressure, initially increasing and then decreasing as pressure rose. Conversely, surface roughness showed negative correlations with both standoff distance and nozzle traverse speed, progressively decreasing with increases in these parameters. Through hierarchical analysis, the effect hierarchy of the three factors on surface roughness was determined as follows: jet pressure > standoff distance > nozzle traverse speed. Parametric optimization revealed that a jet pressure of 150 MPa, a standoff distance of 25 mm, and a nozzle traverse speed of 180 mm/min collectively yielded a peak surface roughness of 62.549 μm. This value aligns with the pre-coating surface preparation standards for grade-A marine steel substrates, ensuring optimal adhesion for subsequent anti-corrosion treatments. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

Hydrothermal liquefaction (HTL) is a thermochemical conversion process that converts wet biomass into biocrude oil, a gas phase, a solid phase, and an aqueous phase (HTL-AP). An obstacle to the development and scaling of HTL is the volume of HTL-AP produced during the process, which has high concentrations of nitrogen and carbon and cannot be disposed of in the environment without treatment. The HTL-AP is enriched with organic compounds, particularly light polar organics and nitrogenous compounds, which are inhibitory to microbial treatment in wastewater treatment plants. For this reason, the valorization of the HTL-AP is significant for the circular economy of HTL. This review synthesizes published findings on different types of treatment of the HTL-AP for the recovery of valuable nutrients and the removal of toxic compounds. This work outlines the trade-offs of the treatments to serve as a guide for future research to address these weaknesses and improve the valorization of the HTL-AP. Furthermore, this work uniquely focuses on HTL-AP treatment for recovering plant-available nitrogen, targeting its potential use as a fertilizer. The literature highlights the importance of increasing nitrogen bioavailability in HTL-AP through two-step treatments and by selecting HTL-AP derived from protein-rich feedstocks, which offer higher initial nitrogen content. According to the current state of research, further work is needed to optimize chemical and biological treatments for nutrient recovery from HTL-AP, particularly regarding treatment scale and duration. Additionally, economic analyses across different treatment types are currently lacking, but are essential to evaluate their feasibility and practicality. Full article

Background and Objectives: Bone regeneration (BR) is a cornerstone technique in reconstructive dental surgery, traditionally using either barrier membranes, titanium meshes, or perforated non-resorbable membranes to facilitate bone regeneration. Recent advancements in 3D technology, including CAD/CAM and additive manufacturing, have enabled the development of customized scaffolds tailored to patient needs, potentially overcoming the limitations of conventional methods. Materials and Methods: A scoping review was conducted according to the PRISMA guidelines. Electronic searches were performed in MEDLINE (PubMed), the Cochrane Library, Scopus, and Web of Science up to January 2025 to identify studies on digital technologies applied to bone augmentation. Eligible studies encompassed randomized controlled trials, cohort studies, case series, and case reports, all published in English. Data regarding digital workflows, software, materials, printing techniques, and sterilization methods were extracted from 23 studies published between 2015 and 2024. Results: The review highlights a diverse range of digital workflows, beginning with CBCT-based DICOM to STL conversion using software such as Mimics and Btk-3D^®. Customized titanium meshes and other meshes like Poly Ether-Ether Ketone (PEEK) meshes were produced via techniques including direct metal laser sintering (DMLS), selective laser melting (SLM), and five-axis milling. Although titanium remained the predominant material, studies reported variations in mesh design, thickness, and sterilization protocols. The findings underscore that digital customization enhances surgical precision and efficiency in BR, with several studies demonstrating improved bone gain and reduced operative time compared to conventional approaches. Conclusions: This scoping review confirms that 3D techniques represent a promising advancement in BR. Customized digital workflows provide superior accuracy and support for BR procedures, yet variability in protocols and limited high-quality trials underscore the need for further clinical research to standardize techniques and validate long-term outcomes. Full article

In this paper, screen-printed ion-selective electrodes combined with single-walled carbon nanotubes (SWCNTs) and gold nanoparticles (AuNPs) were used to rapidly and accurately measure serum Ca²⁺ concentration. Due to the susceptibility of cows to hypocalcemia after delivery, this disease can affect the health of cows and reduce milk production. Therefore, the development of an economical and swift detection method holds paramount importance for facilitating early diagnosis and subsequent treatment. In this study, by combining the high electrical conductivity and large surface area of SWCNTs with the strong catalytic activity of AuNPs, a SWCNTs/AuNPs composite with high sensitivity and good stability was prepared, achieving efficient selective recognition and signal conversion of Ca²⁺. The experimental results indicate that the screen-printed electrode modified with SWCNTs/AuNPs exhibited excellent performance in the determination of Ca²⁺ concentration. Its linear response range is 10^−5.5–10⁻¹ M, covering the normal and pathological concentration range of Ca²⁺ in cow blood, and the detection limit is far below the clinical detection requirements. In addition, the electrode also has good anti-interference ability and fast response time (about 15 s), showing good performance in the range of 5–45 °C. In practical applications, the combination of the electrode and portable detection equipment can realize the field rapid determination of cow blood Ca²⁺ concentration. This method is easy to operate, cost-effective, and easy to promote, providing strong technical support for the health management of dairy farms. Full article

This paper explores responses to online coverage of an avian tracking project. Researchers attached novel trackers to a small group of wild magpies (Gymnorhina tibicen). These were subsequently removed by conspecifics, an example of ‘rescue behavior’ that was recounted in several media outlets. Online comments on three articles, from across the political spectrum (the Conversation, UK Guardian, and UK Daily Mail), were selected for thematic analysis. The resulting 680 comments were analyzed qualitatively and quantitatively to uncover predominant themes and the overall balance of positive and negative sentiments expressed about this tagging project or wildlife tagging generally. Topics occurring most frequently were themed into three interrelated areas: (1) sharing personal feelings and experiences, (2) comparing the merits of different species, and (3) sharing knowledge and opinion. Twenty-one percent (21%) of respondents expressed an opinion on the ethics of wildlife tagging. In the Daily Mail and Guardian, this opinion was more likely to be negative towards the use of tags. Opinion was more balanced for readers of the Conversation’s article. Willingness to comment on online news is low, and readers of this story were not asked directly for their opinion. Nevertheless, the data here illustrate some public perceptions of wildlife tagging, and there was a clear negative reaction from many responders. Widening the means through which people can engage with animal science has the potential to advance discussions around research ethics and animal welfare. Reactions to this story expose important questions for scientists seeking to engage with, and convince, the public of the merits of their work. Full article

The production of synthetic natural gas in the context of power-to-gas is a promising technology for the utilization of CO₂. Ni-based catalysts supported on carbon nanotubes (CNTs) were prepared through incipient wetness impregnation and characterized using N₂ adsorption, X-ray diffraction (XRD), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), and temperature-programmed reduction (TPR). The catalysts were tested for CO₂ methanation in the 200–400 °C temperature range and at atmospheric pressure. The results demonstrated that the catalytic activity increased with the addition of the CNTs and Ni loading. The selectivity towards CH₄ was close to 100% for the Ni/ZrO₂/CNT catalysts. Reduction of the calcined catalyst at 500 °C using H₂ modified the surface chemistry of the catalyst, leading to an increase in the Ni particles. The CO₂ conversion was dependent on the Ni loading and the temperature reduction in the NiO species. The 10Ni/ZrO₂/CNT catalyst was highly stable in CO₂ methanation at 350 °C for 24 h. Thus, CNTs combined with Ni and ZrO₂ were considered promising for use as catalysts in CO₂ methanation at low temperatures. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

The efficient simultaneous removal of NO_x and CO from sintering flue gas under low-temperature conditions (110–180 °C) in iron and steel enterprises remains a significant challenge in the field of environmental catalysis. In this study, we present an innovative strategy to enhance the performance of CuSmTi catalysts through silver modification, yielding a bifunctional system capable of oxygen structure regulation and demonstrating superior activity for the combined NH₃-SCR and CO oxidation reactions under low-temperature, oxygen-rich conditions. The modified AgCuSmTi catalyst achieves complete NO conversion at 150 °C, representing a 50 °C reduction compared to the unmodified CuSmTi catalyst (T_100% = 200 °C). Moreover, the catalyst exhibits over 90% N₂ selectivity across a broad temperature range of 150–300 °C, while achieving full CO oxidation at 175 °C. A series of characterization techniques, including XRD, Raman spectroscopy, N₂ adsorption, XPS, and O₂-TPD, were employed to elucidate the Ag-Cu interaction. These modifications effectively optimize the surface physical structure, modulate the distribution of acid sites, increase the proportion of Lewis acid sites, and enhance the activity of lattice oxygen species. As a result, they effectively promote the adsorption and activation of reactants, as well as electron transfer between active species, thereby significantly enhancing the low-temperature performance of the catalyst. Furthermore, in situ DRIFTS investigations reveal the reaction mechanisms involved in NH₃-SCR and CO oxidation over the Ag-modified CuSmTi catalyst. The NH₃-SCR process predominantly follows the L-H mechanism, with partial contribution from the E-R mechanism, whereas CO oxidation proceeds via the MvK mechanism. This work demonstrates that Ag modification is an effective approach for enhancing the low-temperature performance of CuSmTi-based catalysts, offering a promising technical solution for the simultaneous control of NO_x and CO emissions in industrial flue gases. Full article

Methods for the improvement of regional lignocellulosic resources (wood and agriculture waste) were studied and analyzed using blends with optimized compositions and a selective pretreatment of the blends using microwaves to enhance their thermochemical conversion and energy production efficiency. A batch-size pilot device was used to provide the thermochemical conversion of biomass blends of different compositions, analyzing the synergy of the effects of thermal and chemical interaction between the components on the yield and thermochemical conversion of volatiles, responsible for producing heat energy at various stages of flame formation. To control the thermal decomposition of the biomass, improving the flame characteristics and the produced heat, a selective pretreatment of blends using microwaves (2.45 GHz) was achieved by varying the temperature of microwave pretreatment. Assessing correlations between changes in the main characteristics of pretreated blends (elemental composition and heating value) on the produced heat and composition of products suggests that selective MW pretreatment of biomass blends activates synergistic effects of thermal and chemical interaction, enhancing the yield and combustion of volatiles with a correlating increase in produced heat energy, thus promoting the wider use of renewable biomass resources for sustainable energy production by limiting the use of fossil fuels for heat-energy production and the formation of GHG emissions. Full article

Electro-oxidation of ammonia has emerged as a promising route for sustainable energy conversion and pollutant mitigation. In this study, we report the facile fabrication of dendritic Cu@Pt core–shell nanostructures electrodeposited on pencil graphite, forming an efficient electrocatalyst for the ammonia oxidation reaction (AOR). The designed electrocatalyst exhibited high catalytic activity towards AOR, achieving high current density at very low potentials (−0.3 V vs. Ag/AgCl), with a lower Tafel slope of 16.4 mV/dec. The catalyst also demonstrated high electrochemical stability over 1000 potential cycles with a regeneration efficiency of 78%. In addition to catalysis, Cu@Pt/PGE facilitated very sensitive and selective electrochemical detection of ammonia nitrogen by differential pulse voltammetry, providing an extensive linear range (1 μM to 1 mM) and a low detection limit of 0.78 μM. The dual functionality of Cu@Pt highlights its potential in enhancing ammonia-based fuel cells and monitoring ammonia pollution in aquatic environments, thereby contributing to the development of sustainable energy and environmental technologies. Full article

Based on engine bench testing, this study investigated the effect of diesel oxidation catalytic converter (DOC) formulations on the gaseous emissions performance of diesel engines equipped with a DOC+ catalyzed diesel particulate filter (CDPF)+selective catalytic reduction (SCR) system after the treatment system. The experimental results indicate that changes in DOC formulations have no significant effect on engine fuel economy. As the precious metal loading increases and the Pt/Pd ratio decreases, the T₅₀ for CO and HC decreases, and the low-temperature conversion rates (<300 °C) for CO and HC increase. However, as the temperature continues to rise, the beneficial effect of increased precious metal loading or Pd on CO and HC conversion rates gradually weakens. The average conversion rates in the high-temperature range (≥300 °C) show little difference. The NO conversion rate increases with increasing precious metal loading. The NO conversion rate is more sensitive to Pt content, with higher Pt content formulations promoting NO oxidation, contrary to the trends observed for CO and HC conversion rates. When the SCR inlet temperature is low, high NO₂ concentrations are beneficial for improving the SCR’s NOx conversion efficiency. When the SCR inlet temperature is high, the SCR’s NOx conversion efficiency exceeds 90% with no significant differences. No significant impact of DOC formulation changes on CDPF pressure drop under external conditions was observed. Full article