Search Results (2,826)

The invasive fish Atherina boyeri constitutes an ecologically disruptive yet underexploited biomass with strong potential for transformation into value-added biofunctional ingredients. This study investigates the functional, antioxidant, and antimicrobial properties of protein hydrolysates that were produced from fish collected in the Hirfanlı and Yamula reservoirs using three commercial proteases (alcalase, bromelain, and flavourzyme). Bromelain produced the highest degree of hydrolysis, yielding higher proportions of low-molecular-weight peptides and greater radical-scavenging activity. Flavourzyme hydrolysates exhibited the most favorable emulsifying properties, Alcalase hydrolysates produced the highest foaming capacity and stability. All hydrolysates showed high absolute zeta-potential values across pH 3–9, demonstrating strong colloidal stability. Protein solubility remained above 80% across most pH levels, indicating extensive peptide release and improved compatibility with aqueous media. The Oil-binding capacity (2.78–3.75 mL/g) was consistent with reported values for marine hydrolysates. Antioxidant and antimicrobial evaluations revealed clear enzyme-dependent patterns, with Bromelain exhibiting the strongest DPPH activity and Alcalase and Flavourzyme showing the most pronounced inhibition of major foodborne pathogens. Additionally, all hydrolysates exhibited measurable ACE-inhibitory activity, with flavourzyme-derived peptides showing the highest inhibitory activity, underscoring their potential relevance for antihypertensive applications. These findings highlight the strategic valorization of A. boyeri through enzymatic hydrolysis, demonstrating its potential as a sustainable, clean-label functional ingredient source. Full article

Biodiesel, which is a blend of fatty acid methyl esters (FAME), has garnered significant attention as a promising alternative to petroleum-based diesel fuel. Nevertheless, the commercial production of biodiesel faces challenges due to the high costs associated with feedstock and the non-recyclable homogeneous catalyst system. To address these issues, a solid catalyst derived from construction industry waste cement was synthesized and utilized for biodiesel production from waste cooking oil (WCO). The catalyst’s surface and physical characteristics were analyzed through various techniques, including Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Fourier Transform Infrared Spectroscopy (FTIR). The waste-cement catalyst demonstrated remarkable catalytic performance and reusability in the transesterification of WCO with methanol for biodiesel synthesis. A maximum biodiesel yield of 98.1% was obtained under the optimal reaction conditions of reaction temperature 65 °C; methanol/WCO molar ratio 16:1; calcined cement dosage 3 g; and reaction time 8 h. The apparent activation energy (Ea) from the reaction kinetic study is 35.78 KJ·mol⁻¹, suggesting that the transesterification reaction is governed by kinetic control rather than diffusion. The biodiesel produced exhibited high-quality properties and can be utilized in existing diesel engines without any modifications. This research presents a scalable, environmentally benign pathway for WCO transesterification, thereby contributing significantly to the economic viability and long-term sustainability of the global biodiesel industry. Full article

A series of biocomposites were elaborated by incorporating cellulose fibers, obtained from raw alfa plant, into a new polyurethane (PU) matrix synthesized from jojoba oil. The cellulose content was adjusted between 0% and 50%. To examine their properties, several characterization methods were employed. Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) analyses confirmed that the extracted cellulose and the polyurethane matrix have high interfacial adhesion. Thermal stability was assessed using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). They indicate that the composites remained thermally stable in air up to 265 °C and exhibited glass transition temperatures (Tg) in the range of −38 to −7 °C, depending on the fiber percentage inside the polyurethane-based biocomposite. The corresponding mechanical properties increased with the addition of cellulose, reaching optimal improvement at 40% fiber content. Full article

This study demonstrates a sustainable, integrated pathway for valorizing carrot processing by-products through solvent-free lycopene recovery. The approach combines optimized infrared dehydration with ultrasound-assisted extraction using edible oils. Drying kinetics were modeled at multiple temperatures, with the Midilli model providing the best fit (R² > 0.99), enabling accurate prediction of moisture content removal while preserving bioactive compounds. Optimization via Box–Behnken design identified efficient extraction conditions (49.7–60 °C, 10 mL/g, 60 min), achieving lycopene equivalent (LE) yields of 3.07 to 5.00 mg/kg oil. Sunflower and blended oils showed comparable performance under maximum sonication power (240 W), with strong agreement between predicted and experimental yields. The process generated two valuable outputs: a functional lycopene-enriched oil and an exhausted carrot powder co-product, the latter retaining its crude fiber content despite other compositional changes. This research presents a scalable, green methodology that aligns with circular economy principles, transforming agro-industrial waste into functional food ingredients without organic solvents. Thus, the developed approach establishes a transferable model for the sustainable valorization of carotenoid-rich residues, contributing directly to greener food production systems. By providing a practical technological framework to convert waste into wealth, this work supports the fundamental transition toward a circular bioeconomy. Full article

With the advancement of industrial systems toward greater complexity and higher asset value, unexpected equipment failures now risk severe production interruptions, substantial economic costs, and critical safety hazards. Conventional maintenance strategies, which are primarily reactive or schedule-based, have proven inadequate in preventing unplanned downtime, underscoring a pressing demand for more intelligent monitoring solutions. In this context, intelligent spectral detection has arisen as a transformative methodology to bridge this gap. This review explores the integration of spectroscopic techniques with machine learning for equipment defect diagnosis and prognosis, with a particular focus on applications such as hydrogen leak detection and transformer oil aging assessment. Key aging indicators derived from spectral data are systematically evaluated to establish a robust basis for condition monitoring. The paper also identifies prevailing challenges in the field, including spectral data scarcity, limited model interpretability, and poor generalization across different operational scenarios. Future research directions emphasize the construction of large-scale, annotated spectral databases, the development of multimodal data fusion frameworks, and the optimization of lightweight algorithms for practical, real-time deployment. Ultimately, this work aims to provide a clear roadmap for implementing predictive maintenance paradigms, thereby contributing to safer, more reliable, and more efficient industrial operations. Full article

Produced water (PW) generated from oil and gas operations poses a significant environmental challenge due to its high salinity and complex organic–inorganic composition. This study evaluates forward osmosis (FO) as an energy-efficient approach for PW treatment by comparing a commercial cellulose triacetate (CTA) membrane and a fabricated electrospun nanofibrous membrane, both modified with a zwitterionic sulfobetaine methacrylate/polydopamine (SBMA/PDA) coating. Fourier Transform Infrared Spectroscopy (FTIR) spectra verified the successful incorporation of SBMA and PDA through the appearance of characteristic sulfonate, quaternary ammonium, and catechol/amine-related vibrations. Scanning electron microscopy (SEM) imaging revealed the intrinsic dense surface of the CTA membrane and the highly porous nanofibrous architecture of the electrospun membrane, with both materials showing uniform coating coverage after modification. Complementary analyses supported these observations: X-ray Photoelectron Spectroscopy (XPS) confirmed the presence of nitrogen, sulfur, and chlorine containing functionalities associated with the zwitterionic layer; Thermogravimetric Analysis (TGA) demonstrated that surface modification did not compromise the thermal stability of either membrane; and contact-angle measurements showed substantial increases in surface hydrophilicity following modification. Gas chromatography–mass spectrometry (GC–MS) analysis of the Permian Basin PW revealed a chemically complex mixture dominated by light hydrocarbons, alkylated aromatics, and heavy semi-volatile organic compounds. FO experiments using hypersaline PW demonstrated that the fabricated membrane consistently outperformed the commercial membrane under both MgCl₂ and Na₃PO₄ draw conditions, achieving up to ~40% higher initial water flux and total solids rejection as high as ~62% when operated with 2.5 M Na₃PO₄. The improved performance is attributed to the nanofibrous architecture and zwitterionic surface chemistry, which together reduced fouling and reverse solute transport. These findings highlight the potential of engineered zwitterionic nanofibrous membranes as robust alternatives to commercial FO membranes for sustainable produced water treatment. Full article

Three painted valuable wood sculptures from conventual collections in Apulia (Southern Italy), made between the beginning of the 17th century and the first half of the 18th century, were studied to shed light on the pictorial materials and techniques of the Neapolitan Baroque sculpture in Southern Italy. A multi-analytical approach was implemented using integrated micro-invasive techniques, including polarized light microscopy (PLM) in ultraviolet (UV) and visible (VIS) light, scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS), Fourier-Transform Infrared (FTIR) spectroscopy, and pyrolysis–gas chromatography/high-resolution mass spectrometry (Py-GC/HRMS). The stratigraphic sequences were microscopically identified, and the pictorial layers were discriminated on the basis of optical features, elemental compositions, and mapping. Organic components were detected by FTIR as lipids and proteinaceous compounds for binders, while terpenic resins were detected as varnishes. Accordingly, PY-GC/HRMS identified siccative oils, animal glue, egg, and colophony. The results allowed the identification of the painting techniques used for the pictorial films and the ground preparation layers and supported the distinction between original and repainting layers. The results of this multi-analytical approach provide insights into Baroque wooden sculpture in Southern Italy and offers information to support restorers in conservation works. Full article

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market. Full article

The oily wastewater produced by transformer oil leakage contains pollutants such as mineral oil, metal particles, aged oil and additives, which can disrupt the dissolved oxygen balance in water bodies, pollute soil and endanger human health through the food chain, causing serious environmental pollution. Effective oil–water separation technology is the key to ecological protection and resource recovery. This paper reviews the principles, influencing factors and research progress of traditional (gravity sedimentation, air flotation, adsorption, demulsification) and new (nanocomposite adsorption, metal–organic skeleton materials, superhydrophobic/superlipophilic modified films) transformer oil–water separation technologies. Traditional technologies are mostly applicable to large-particle-free oil and are difficult to adapt to complex matrix wastewater. However, the new technology has significant advantages in separation efficiency (up to over 99.5%), selectivity and cycling stability (with a performance retention rate of over 85% after 20–60 cycles), breaking through the bottlenecks of traditional methods. In the future, it is necessary to develop low-cost and efficient separation technologies, promote the research and development of intelligent responsive materials, upgrade low-carbon preparation processes and their engineering applications, support environmental protection treatment in the power industry and encourage the coupling of material innovation and processes. Full article

Shale oil reservoirs exhibit ultralow permeability and complex pore structures, which result in non-Darcy low-velocity flow and cause permeability to be stress-sensitive. Moreover, two-phase flow of oil and gas frequently occurs during the depletion of shale oil reservoirs. Consequently, investigating the rate-transient behavior of shale oil wells necessitates comprehensive consideration of multiphase flow, threshold pressure gradients, and stress sensitivity. Although numerous analytical models exist for rate-transient analysis of multistage fractured horizontal wells, none of them simultaneously incorporate these critical factors. In this study, we extend the classical five-region model to incorporate multiphase flow, threshold pressure gradients, and stress sensitivity. The proposed model is solved using Pedrosa’s transformation, perturbation theory, the Laplace transform, and the Stehfest numerical inversion method. A systematic analysis of the influence of various parameters on the oil production rate and cumulative oil production is conducted, and a field case study is presented to validate the applicability and effectiveness of the model. It is found that the permeability modulus of the main fracture, the half-length of the main fracture, and the threshold pressure gradient of the unstimulated reservoir have a significant influence on cumulative oil production spanning 20 years. With a 100% relative input error, these parameters result in prediction errors of 23.77%, 16.65%, and 17.78%, respectively. In contrast, the threshold pressure gradient of the main fracture and the threshold pressure gradient of the stimulated reservoir have a negligible impact; under the same level of input error (100%), they cause only 0.36% and 0.48% prediction errors in the 20-year cumulative oil production period, respectively. This research provides an efficient and reliable framework for analyzing production data and forecasting shale oil well performance. Full article

The increasing volume of produced water (PW) generated by oil extraction activities has intensified the need for environmentally sustainable strategies that enable its reuse and valorization. Biotechnological approaches, particularly those involving the microbial production of value-added compounds, offer a promising route for transforming PW from an industrial waste into a useful resource. In this context, bacterial exopolysaccharides (EPS) have gained attention due to their diverse functional properties and applicability in bioremediation, bioprocessing and petroleum-related operations. This study evaluated the potential of Lelliottia amnigena to synthesize EPS using oilfield PW as a component of the culture medium in stirred-tank bioreactors. Three conditions were assessed: a control using distilled water (dW), PW diluted to 25% (PW25%) and dialyzed PW (DPW). Batch experiments were conducted for 24 h, during which biomass growth, EPS accumulation and dissolved oxygen dynamics were monitored. Post-cultivation analyses included elemental and monosaccharide composition, scanning electron microscopy and rheological characterization of purified EPS solutions. EPS production varied among treatments, with dW and DPW yielding approximately 9.6 g L⁻¹, while PW25% achieved the highest productivity (17.55 g L⁻¹). The EPS samples contained fucose, glucose and mannose, with compositional differences reflecting the influence of PW-derived minerals. Despite reduced apparent viscosity under PW25% and DPW conditions, the EPS exhibited physicochemical properties suitable for biotechnological applications, including potential use in fucose recovery, drilling fluids and lubrication systems in the petroleum sector. The EPS also demonstrated substantial adsorption capacity, incorporating salts from PW and contributing to contaminant removal. This study demonstrates that PW can serve both as a substrate and as a source of functional inorganic constituents for microbial EPS synthesis, supporting an integrated approach to PW valorization. These findings reinforce the potential of EPS-based bioprocesses as sustainable green technologies that simultaneously promote waste mitigation and the production of high-value industrial bioproducts. Full article

Horizontal wells combined with multi-stage fracturing are key techniques for extracting tight oil formation. However, due to the ultra-low permeability and porosity of reservoirs, energy depletion occurs rapidly, necessitating external supplements to sustain production. During the hydraulic fracturing process, large volumes of fracturing fluid are injected into reservoirs, increasing its pressure to a certain extent. However, due to the oil-wet nature of the formation, the fracturing fluid cannot penetrate the rock, failing to enhance oil recovery during the shut-in period. Surfactant-based nanofluids have been introduced as fracturing fluid additives to reverse rock wettability, thereby boosting imbibition-driven recovery. Although the imbibition has been studied to inspire the tight oil recovery, few studies have demonstrated the imbibition in enhanced fossil hydrogen energy, which further promotes the imbibition recovery. In this paper, complex nanofluid dispersions (CND) have been proved to enhance the tight reservoir pressure. Through contact angle and imbibition experiments, it is shown that CND can transform oil-wet rock to water-wet, reduce the adhesion of oil, and improve the ultimate oil recovery through the imbibition effect. Then, core flow testing experiments were conducted to show CND can decrease the flow resistance and improve the swept area of the injected fluid. In the end, pressure transmission tests were conducted to show CND can enhance the formation energy and production after fracturing. Results demonstrate that CND enables the fracturing fluid to travel further away from the hydraulic fractures, thus decreasing the depletion of tight formation pressure and maintaining a higher oil production rate. Results help optimize the design of the hydraulic fracturing of tight offshore reservoirs. Full article

Sustainable oil and gas development demands eco-friendly and cost-effective drilling fluids. Water-based drilling fluids (WBDFs) are preferred over oil-based alternatives for their lower environmental impact, but they often suffer from excessive fluid loss in permeable formations, leading to thick filter cakes, reduced mud weight, and operational delays. Conventional chemical additives mitigate this issue but pose environmental and health risks due to their toxicity and non-biodegradability. This study explores the use of biodegradable additives extracted from avocado seed (AS), rambutan shell (RS), tamarind shell (TS) and banana trunk (BT) biomass in four particle sizes of 300, 150, 75 and 32 μm to improve filtration control in WBDFs. All four materials were crushed by ball milling and characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray (EDX). In accordance with API Spec 13A recommendations, several water-based drilling fluids (WBDFs), including reference fluid and modified fluids formulated with biodegradable additives at a fixed percentage of 3 wt% and varied particle sizes, were prepared. The rheological and filtration properties of the formulated drilling fluids were investigated by conducting industry-standard rheology and filtration tests under LPLT conditions (100 psi, 25 °C) and HPHT conditions (1500 psi, 75 °C). The results show that 32 μm tamarind shell powder delivered the strongest performance, reducing fluid loss by 82.4% under HPHT conditions and producing the thinnest mud cake (0.33 mm); it also reduced fluid loss by 72.8% under LPLT conditions, outperforming the other biodegradable materials. Full article

As a tectonically stable and extensively superimposed basin situated in the North China Craton, the Ordos Basin hosts abundant reserves of oil, natural gas, and coal within its Paleozoic strata, rendering it a focal area in energy-related geological research. The basin’s evolutionary history provides a comprehensive record of key geological transitions—from an Early Paleozoic carbonate platform to Late Paleozoic marine–continental transitional deposits and ultimately to continental clastic sedimentation—largely governed by the regional tectonic dynamics associated with the North China Plate. This study presents a systematic review of the sedimentary and tectonic evolution of the Paleozoic sequence in the basin. Findings indicate that during the Early Paleozoic, the basin developed under a passive continental margin setting, characterized by widespread epicontinental marine carbonate deposition. By the Late Ordovician, subduction of the Qinqi Ocean triggered the Caledonian orogeny, resulting in regional uplift across the basin, widespread erosion, and a significant hiatus in Middle to Late Ordovician sedimentation, which facilitated the formation of paleo-weathered crust karst reservoirs. In the Late Paleozoic, the basin evolved into an intracratonic depression. From the Late Carboniferous to the Early Permian, the Hercynian tectonic event influenced the transformation from isolated rift basins to a broad epicontinental sea, leading to the deposition of critical coal-bearing strata within marine–continental transitional facies. Starting in the Middle Permian, the closure of surrounding oceanic domains induced widespread tectonic uplift, shifting the depositional environment to a terrestrial fluvial-lacustrine system and marking the termination of marine sedimentation in the region. Based on the comprehensive research findings, this study underscores that the superposition, inheritance, and interaction of multiple tectonic events are the primary controls on the paleogeographic architecture and sedimentary. Full article

This paper investigates the effectiveness of lightweight Convolutional Neural Networks (CNNs) compared with Vision Transformers (ViTs) for industrial defect detection, with a focus on wind turbine blades. While ViTs have recently attracted significant attention in computer vision research, their advantages over traditional CNNs remain unclear in highly specialized industrial applications. To address this gap, a rigorous comparative study was conducted using a labeled dataset of wind turbine blade surface defects, including corrosion, craze, hide_craze, surface_attach, surface_corrosion, surface_injure, surface_oil, thunderstrike. Experimental results demonstrate that lightweight CNNs outperform ViTs in both accuracy and efficiency. Specifically, CNN-based models achieved a maximum accuracy of 98.2%, while the best-performing ViT reached only 50.6%. Beyond accuracy, CNNs also showed superior data efficiency and robustness when trained on relatively small datasets, underscoring their suitability for industrial defect detection tasks where large-scale annotated data are often unavailable. These findings highlight the continuing relevance of lightweight CNNs in industrial settings and provide practical guidance for selecting models in safety-critical applications such as wind turbine blade inspection. This paper contributes by clarifying the limitations of ViTs under industrial conditions and reinforcing the value of lightweight CNNs as a reliable and computationally efficient solution for defect detection. Full article