Search Results (4,436)

The detection of trace chemicals at low and ultra-low concentrations is critical for applications in environmental monitoring, medical diagnostics, food safety and other fields. Conventional detection techniques often lack the required sensitivity, specificity, or cost-effectiveness, making real-time, in situ analysis challenging. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool, offering improved sensitivity through the enhancement of Raman scattering by plasmonic nanostructures. While noble metals such as Ag and Au are currently the reference choices for SERS substrates, fabrication methods should balance enhancement efficiency, reproducibility and scalability. In this study, we propose a novel approach for SERS substrate fabrication using reactive Aerosol Jet Printing (r-AJP) as an innovative additive manufacturing technique. The r-AJP process enables in-flight Ag seed reduction and nucleation of Ag nanoparticles (NPs) by mixing silver nitrate and ascorbic acid aerosols before deposition, as suggested by computational fluid dynamics (CFD) simulations. The resulting coatings were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses, revealing the formation of nanoporous crystalline Ag agglomerates partially covered by residual matter. The as-prepared SERS substrates exhibited remarkable SERS activity, demonstrating a high enhancement factor (10⁶) for rhodamine (R6G) detection. Our findings highlight the potential of r-AJP as a scalable and cost-effective fabrication strategy for next-generation SERS sensors, paving the way for the development of a new additive manufacturing tool for noble metal material deposition. Full article

In recent years, biofuels and bioenergy derived from algae have gained increasing attention, fueled by the growing demand for renewable energy sources and the urgent need to lower CO₂ emissions. This research examines the generation of bioethanol and biomethane using freshly harvested and sedimented algal biomass. Employing a factorial experimental design, various trials were conducted, with ethanol yield as the primary optimization target. The findings indicated that the sodium hydroxide concentration during pretreatment and the amylase dosage in enzymatic hydrolysis were key parameters influencing the ethanol production efficiency. Under optimized conditions—using 0.3 M NaOH, 25 μL/g starch, and 250 μL/g cellulose—fermentation yielded ethanol concentrations as high as 2.75 ± 0.18 g/L (45.13 ± 2.90%), underscoring the significance of both enzyme loading and alkali treatment. Biomethane potential tests on the residues of fermentation revealed reduced methane yields in comparison with the raw algal feedstock, with a peak value of 198.50 ± 25.57 mL/g volatile solids. The integrated process resulted in a total energy recovery of up to 809.58 kWh per tonne of algal biomass, with biomethane accounting for 87.16% of the total energy output. However, the energy recovered from unprocessed biomass alone was nearly double, indicating a trade-off between sequential valorization steps. A comparison between fresh and dried feedstocks also demonstrated marked differences, largely due to variations in moisture content and biomass composition. Overall, this study highlights the promise of integrated algal biomass utilization as a viable and energy-efficient route for sustainable biofuel production. Full article

Restaurants and open markets generate considerable quantities of organic waste. Converting these residues into poultry feed ingredients offers a sustainable disposal route. This study aimed to evaluate the nutritional and sensory viability of a novel feed complement formulated from Bonito fish meal (Sarda chiliensis chiliensis) and Única potato peel flour (Solanum tuberosum L. cv. Única). This study was conducted in three phases: (i) production and nutritional characterization of the two by-product flours; (ii) formulation of a 48:52 (w/w) blend, incorporated into broiler diets at 15%, 30%, and 45% replacement levels over a 7-week trial divided into starter (3 weeks), grower (3 weeks), and finisher (1 week) phases; and (iii) assessment of growth performance (weight gain, final weight, and feed conversion ratio), followed by a sensory evaluation of the resulting meat using a Check-All-That-Apply (CATA) analysis. The Bonito fish meal exhibited 50.78% protein, while the Única potato peel flour was rich in carbohydrates (74.08%). The final body weights of broiler chickens ranged from 1872.1 to 1886.4 g across treatments, and the average feed conversion ratio across all groups was 0.65. Replacing up to 45% of commercial feed with the formulated complement did not significantly affect growth performance (p > 0.05). Sensory analysis revealed that meat from chickens receiving 15% and 45% substitution levels was preferred in terms of aroma and taste, whereas the control group was rated higher in appearance. These findings suggest that the formulated feed complement may represent a viable poultry-feed alternative with potential sensory and economic benefits, supporting future circular-economy strategies. Full article

In this study, a highly cadmium (II)-resistant bacterium strain, C10-4, identified as Bacillus altitudinis, was isolated from a sediment sample collected from Baiyangdian Lake, China. The minimum inhibitory concentration (MIC) of Cd(II) for strain C10-4 was 1600 mg/L. Factors such as the contact time, pH, Cd(II) concentration, and biomass dosage affected the adsorption of Cd(II) by strain C10-4. The adsorption process fit well to the Langmuir adsorption isotherm model and the pseudo-second-order kinetics model, based on the Cd(II) adsorption data obtained from the cells of strain C10-4. This suggests that Cd(II) is adsorbed by strain C10-4 cells via a single-layer homogeneous chemical adsorption process. According to the Langmuir model, the maximum biosorption capacity was 3.31 mg/g for fresh-strain C10-4 biomass. Cd(II) was shown to adhere to the bacterial cell wall through SEM-EDS analysis. FTIR spectroscopy further indicated that the main functional sites for the binding of Cd(II) ions on the cell surface of strain C10-4 were functional groups such as N-H, -OH, -CH-, C=O, C-O, P=O, sulfate, and phosphate. After the inoculation of strain C10-4 into Cd(II)-contaminated soils, there was a significant reduction (p < 0.01) in the exchangeable fraction of Cd and an increase (p < 0.01) in the sum of the reducible, oxidizable, and residual fractions of Cd. The results show that Bacillus altitudinis C10-4 has good potential for use in the remediation of Cd(II)-contaminated soils. Full article

Environmental sustainability and responsible resource utilization are critical global challenges. In this work, we present a sustainable and circular-economy-based approach for synthesizing CuFe₂O₄ nanoparticles by directly utilizing copper oxide minerals sourced from Chilean mining operations. Copper sulfate (CuSO₄) was extracted from these minerals through acid leaching and used as a precursor for nanoparticle synthesis via both chemical co-precipitation and microwave-assisted methods. The influence of different precipitating agents—NaOH, Na₂CO₃, and NaF—was systematically evaluated. XRD and FESEM analyses revealed that NaOH produced the most phase-pure and well-dispersed nanoparticles, while NaF resulted in secondary phase formation. The microwave-assisted method further improved particle uniformity and reduced agglomeration due to rapid and homogeneous heating. Electrochemical characterization was conducted to assess the suitability of the synthesized CuFe₂O₄ for supercapacitor applications. Cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurements confirmed pseudocapacitive behavior, with a specific capacitance of up to 1000 F/g at 2 A/g. These findings highlight the potential of CuFe₂O₄ as a low-cost, high-performance electrode material for energy storage. This study underscores the feasibility of converting primary mined minerals into functional nanomaterials while promoting sustainable mineral valorization. The approach can be extended to other critical metals and mineral residues, including tailings, supporting the broader goals of a circular economy and environmental remediation. Full article

The aim of this study was to isolate Weizmannia spp. that produce lactic acid from xylose and use an experimental design to optimize the production of the metabolite. After isolation, the experiments were conducted in xylose-yeast extract-peptone medium. The identification of isolates was performed using the 16S rDNA PCR technique, followed by sequencing. A central composite rotatable design (CCRD) was used to optimize the concentrations of the carbon source (xylose), nitrogen source (yeast extract and peptone), and sodium acetate. Two strains were considered promising for lactic acid production, with W. coagulans BLMI achieving greater lactic acid production under anaerobic conditions (21.93 ± 0.9 g.L⁻¹) and a yield of 69.18 %, while the strain W. ginsengihumi BMI was able to produce 19.79 ± 0.8 g.L⁻¹, with a yield of 70.46 %. CCRD was used with the W. ginsengihumi strain due to the lack of records in the literature on its use for lactic acid production. The carbon and nitrogen sources influenced the response, but the interactions of the variables were nonsignificant (p < 0.05). The response surface analysis indicated that the optimal concentrations of carbon and nitrogen sources were 32.5 and 3.0 g.L⁻¹, respectively, without the need to add sodium acetate to the culture medium, leading to the production of 20.02 ± 0.19 g.L⁻¹, productivity of 0.55 g/L/h after 36 hours of fermentation, and a residual sugar concentration of 12.59 ± 0.51 g.L⁻¹. These results demonstrate the potential of W. ginsengihumi BMI for the production of lactic acid by xylose fermentation since it is carried out at 50 °C, indicating a path for future studies Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Soil organic carbon (SOC) mineralization plays the critical role of regulating carbon sequestration potential. This process is strongly influenced by agricultural practices, particularly tillage regimes and straw management. However, the complex interactions between tillage methods, straw types, and application rates in terms of SOC dynamics, especially in semi-arid agroecosystems like eastern Inner Mongolia, remain poorly understood. In this study, we assessed the combined effects of no tillage (NT) vs. rotary tillage (RT), three straw types (maize/MS, wheat/WS, and oilseed rape/OS), and three application rates (0.4%/low, 0.8%/medium, and 1.2%/high) on SOC concentration and mineralization using controlled laboratory incubation with soils from long-term plots. The key findings revealed that NT significantly increased the SOC concentration in the topsoil (0–20 cm) by an average of 14.5% compared to that in the RT. Notably, combining NT with medium-rate wheat straw (0.8%) resulted in the achievement of the highest SOC accumulation (28.70 g/kg). SOC mineralization increased with straw inputs, exhibiting significant straw type × rate interactions. Oilseed rape straw showed the highest specific mineralization rate (33.9%) at low input, while maize straw mineralized fastest under high input with RT. Therefore, our results demonstrate that combining NT with either 0.8% wheat straw or 1.2% maize straw represents an optimal application strategy, as the SOC concentration is enhanced by 12–18% for effective carbon sequestration in this water-limited semi-arid region. Therefore, optimizing SOC sequestration requires the integration of appropriate crop residue application rates and tillage methods tailored to different cropping systems. Full article

The olive oil industry generates by-products like olive leaves and pomace, which are rich in bioactive compounds, especially polyphenols. This study applied a circular economy approach to valorize these residues using green ultrasound-assisted extraction (UAE) with GRAS solvents. Key parameters (solvent composition, ultrasound amplitude, and specific energy) were optimized via Response Surface Methodology (RSM) to enhance polyphenol recovery and yield. Ethanol concentration proved to be the most influential factor. Optimal conditions for olive pomace were 100% ethanol, 46 μm amplitude, and 25 J∙mL⁻¹ specific energy, while olive leaves required 72% ethanol with similar ultrasound settings. Under these conditions, extracts were prepared and analyzed using HPLC-ESI-QTOF-MS and DPPH assays. The optimized UAE process achieved yields of 15–20% in less than 5 min and under mild conditions. Optimal extracts showed high oleuropein content (6 mg/g in leaves, 5 mg/g in pomace), lower hydroxytyrosol levels, and minimal oxidized derivatives, suggesting reduced degradation compared to conventional methods. These findings demonstrate UAE’s effectiveness in recovering valuable phenolics from olive by-products, supporting sustainable and efficient resource use. Full article

Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. DSCNN optimizes feature extraction and network architecture to enhance both performance and efficiency: To improve feature extraction, the core innovation is a feature extraction and enhancement module with dynamic snake convolution that dynamically adjusts the convolution kernel’s shape and position to better fit the image’s geometric structures, significantly improving feature extraction. To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. Additionally, the network incorporates a SwishReLU-based activation function and a multi-scale convolutional concatenation structure. This multi-scale design effectively captures both local details and global image structure, enhancing SR reconstruction. In summary, the proposed DSCNN outperforms existing methods in both objective metrics and visual perception (e.g., our method achieved optimal PSNR and SSIM results on the Set5 $\times 4$ dataset). Full article

Sin Nombre virus (SNV) is the main causative agent of hantavirus cardiopulmonary syndrome (HCPS) in North America. SNV is transmitted via environmental biological aerosols (bioaerosols) produced by infected deer mice (Peromyscus maniculatus). It is similar to other viruses that have environmental transmission routes rather than a person-to-person transmission route, such as avian influenza (e.g., H5N1) and Lassa fever. Despite the lack of person-to-person transmission, these viruses cause a significant public health and economic burden. However, due to the lack of targeted pharmaceutical preventatives and therapeutics, the recommended approach to prevent SNV infections is to avoid locations that have a combination of low foot traffic, receive minimal natural sunlight, and where P. maniculatus may be found nesting. Consequently, gaining insight into the SNV bioaerosol decay profile is fundamental to the prevention of SNV infections. The Biological Aerosol Reaction Chamber (Bio-ARC) is a flow-through system designed to rapidly expose bioaerosols to environmental conditions (ozone, simulated solar radiation (SSR), humidity, and other gas phase species at stable temperatures) and determine the sensitivity of those particles to simulated ambient conditions. Using this system, we examined the bioaerosol stability of SNV. The virus was found to be susceptible to both simulated solar radiation and ozone under the tested conditions. Comparisons of decay between the virus aerosolized in residual media and in a mouse bedding matrix showed similar results. This study indicates that SNV aerosol particles are susceptible to inactivation by solar radiation and ozone, both of which could be implemented as effective control measures to prevent disease in locations where SNV is endemic. Full article

This study aimed to evaluate the production of xylose reductase (XR), an enzyme responsible for converting xylose into xylitol, by Candida tropicalis ATCC 750 using hemicellulosic hydrolysate from cashew apple bagasse (CABHM) as a low-cost carbon source. The effects of temperature, aeration, and fluid dynamics on XR biosynthesis were also investigated. The highest XR production (1.53 U mL⁻¹) was achieved at 30 °C, with 8.3 g·L⁻¹ of xylitol produced by the yeast under microaerobic conditions, demonstrating that aeration and fluid dynamics are important factors in this process. Cellular metabolism and enzyme production decreased at temperatures above 35 °C. The maximum enzymatic activity was observed at pH 7.0 and 50 °C. XR is a heterodimeric protein with a molecular mass of approximately 30 kDa. These results indicate that CABHM is a promising substrate for XR production by C. tropicalis, contributing to the development of enzymatic bioprocesses for xylitol production from lignocellulosic biomass. This study also demonstrates the potential of agro-industrial residues as sustainable feedstocks in biorefineries, aligning with the principles of a circular bioeconomy. Full article

The Shenhu sea area is rich in unconsolidated hydrate reserves, but the formation mineral particles are small, the rock cementation is weak, and the coupling mechanism of hydrate phase change, fluid seepage, and formation deformation is complex, resulting in unclear productivity change law under depressurization exploitation. Therefore, a thermal–fluid–solid–chemical coupling model for natural gas hydrate depressurization exploitation in the Shenhu sea area was constructed to analyze the variation law of reservoir parameters and productivity. The results show that within 0–30 days, rapid near-well pressure drop (13.83→9.8 MPa, 36.37%) drives peak gas production (25,000 m³/d) via hydrate dissociation, with porosity (0.41→0.52) and permeability (75→100 mD) increasing. Within 30–60 days, slower pressure decline (9.8→8.6 MPa, 12.24%) and fines migration cause permeability fluctuations (120→90 mD), reducing gas production to 20,000 m³/d. Within 60–120 days, pressure stabilizes (~7.6 MPa) with residual hydrate saturation < 0.1, leading to stable low permeability (60 mD) and gas production (15,000 m³/d), with cumulative production reaching 2.2 × 10⁶ m³. This study clarifies that productivity is governed by coupled “pressure-driven dissociation–heat limitation–fines migration” mechanisms, providing key insights for optimizing depressurization strategies (e.g., timed heat supplementation, anti-clogging measures) to enhance commercial viability of unconsolidated hydrate reservoirs. Full article

Modulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. This study introduces an innovative algorithm for automatic modulation classification (AMC) built on a deep residual network (DRN) architecture. The approach is tailored to accurately identify advanced 5G waveform types such as Orthogonal Frequency-Division Multiplexing (OFDM), Filtered OFDM (FOFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Weighted Overlap and Add OFDM (WOLA), using both 16-QAM and 64-QAM modulation schemes. To our knowledge, this is the first application of deep learning in the classification of such a diverse set of complex 5G waveforms. The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. A detailed performance evaluation is conducted using metrics like classification recall, precision, accuracy, and F-measure. When compared with traditional machine learning approaches reported in recent studies, our DRN-based method shows significantly improved classification accuracy and robustness. These results highlight the effectiveness of deep residual networks in improving adaptive signal processing and enabling automatic modulation recognition in future wireless communication technologies. Full article

The rapid growth of plant-based biodegradable tableware, driven by plastic restrictions, necessitates rigorous safety assessments of potential chemical contaminants like per- and polyfluoroalkyl substances (PFASs). This study comprehensively evaluated PFAS contamination risks in commercial sugarcane pulp tableware, focusing on the residues of five target PFASs (PFOA, PFOS, PFNA, PFHxA, PFPeA) and their migration behavior under simulated use and takeout conditions. An analysis of 22 samples revealed elevated levels of total fluorine (TF: 33.7–163.6 mg/kg) exceeding the EU limit (50 mg/kg) in 31% of products. While sporadic PFOA residues surpassed the EU single compound limit (0.025 mg/kg) in 9% of samples (16.1–25.5 μg/kg), the levels of extractable organic fluorine (EOF: 4.9–17.4 mg/kg) and the low EOF/TF ratio (3.19–10.4%) indicated inorganic fluorides as the primary TF source. Critically, the migration of all target PFASs into food simulants (water, 4% acetic acid, 50% ethanol, 95% ethanol) under standardized use conditions was minimal (PFOA: 0.52–0.70 μg/kg; PFPeA: 0.54–0.63 μg/kg; others < LOQ). Even under aggressive simulated takeout scenarios (50 °C oscillation for 12 h + 12 h storage at 25 °C), PFOA migration reached only 0.99 ± 0.01 μg/kg in 95% ethanol. All migrated levels were substantially (>15-fold) below typical safety thresholds (e.g., 0.01 mg/kg). These findings demonstrate that, despite concerning residue levels in some products pointing to manufacturing contamination sources, migration during typical and even extended use scenarios poses negligible immediate consumer risk. This study underscores the need for stricter quality control targeting PFOA and inorganic fluoride inputs in sugarcane pulp tableware production. Full article