This study evaluated the use of HDES for omega-3 recovery from by-products of Octopus maya, an endemic species of the Yucatán Peninsula, Mexico. A 2 × 3 × 2 factorial design was applied to assess the effect of: (1) the hydrogen bond acceptor (HBA, menthol or eucalyptol) of hydrophobic deep eutectic solvents (HDES) with oleic acid as the hydrogen bond donor; (2) the molar ratio (MR) (1:1, 1:2, or 2:1); and (3) ultrasound-assisted extraction time (ET) (30 or 60 min) in omega-3 equivalents (EO3, mg/mL), determined by UV–Vis spectrophotometry and viscosity characterization to the Octopus maya extracted samples (n = 2), reported as x¯ ± SD. The effects of the factors studied were analyzed by a DOE methodology with Minitab® (version 18). Samples with the highest omega-3 were selected and their composition was confirmed by FTIR, Raman spectroscopy and gas chromatography. Eucalyptol at a molar ratio of 1:2 and an extraction time of 30 min yielded the highest OE3 (0.70 mg/mL). The statistical analysis revealed that the extraction of omega-3 determined by UV–Vis spectrophotometry was significantly influenced by the triple interaction of HBA × MR × ET (p < 0.05), indicating that extraction performance depends on the combined effect of solvent composition and processing conditions. All extracts showed Newtonian behavior with viscosities between 0.011 and 0.036 Pa·s, with eucalyptol formulations presenting the lowest values (0.011–0.023 Pa·s). Fatty acid profile allowed to quantify C16:0; C18:0; C18:1 n-9; C18:2 n-6; and C18:3 n-3, palmitic, stearic, oleic, linoleic and linolenic fatty acids, respectively, showing greater affinity for polyunsaturated fatty acids, mainly omega-6 (23.45–27.91%), and lower affinity for saturated fatty acids such as palmitic and stearic acids, indicating HDES as a sustainable alternative for selective extractions.

This study investigated the immobilization of Chlorella sp. in a nylon matrix to analyze its retention behavior and monitor biomass adhesion. Image capture and processing techniques were combined with capacitance measurements over time, using a Python-based data analysis code. The experiment was carried out in a 2 L photobioreactor under controlled conditions (24 °C, continuous aeration at 9.31 L/min, and light intensity of 71 μmol m−2 s−1). The methodology allowed for quantification of biomass distribution on the matrix surface, as well as changes in the capacitance and optical density of the microalgal culture. The results indicated maximum growth around day 15, showing a strong correlation between optical density (absorbance at 686 nm), image analysis of the matrix, and capacitance records. At this point, absorbance reached 3.913, coverage of 24.56% on the nylon matrix, and capacitance of 375.9 μF. Capacitance measurement proved to be a useful indirect tool to estimate biomass adhesion, while image analysis provided spatial distribution. The observed upward trend in process variables highlights the potential of electrical parameters, such as capacitance, for monitoring microalgal immobilization in suspended systems without altering biofilm structure. This approach supports future applications in scaling processes for bioactive compound production or environmental treatment systems.

Hydraulic fracturing of unconventional reservoirs requires accurate fracture monitoring for treatment optimization. Low-frequency distributed acoustic sensing (LF-DAS) in neighbor wells provides dense strain-rate observations, but gauge-length averaging limits spatial resolution and merges closely spaced fracture features. This study formulates gauge-length averaging as an explicit convolution operator and develops a regularized inversion method combining Tikhonov smoothing, a recursive prior, and L-curve parameter selection, supported by a semi-analytical multi-fracture forward model. On a synthetic benchmark, the method advances the effective resolution from the 10 m gauge-length scale to the 1 m sample-spacing scale, recovering fracture count in all hit-window time slices (versus 32% for raw data), achieving Pearson correlation of 0.80 versus 0.29, with peak-position error reduced by 47%. Noise-sensitivity analysis indicates a practical SNR floor near 20 dB, and Wiener-filter comparison confirms 1.5–2.7× correlation and 1.5–2.3× peak-count advantages across tested noise levels. Field application to HFTS-2 B1H stages 22 and 23 reveals previously hidden tensile features consistent with higher local fracture density. With per-stage processing in seconds and no extra sensing hardware, the method is well suited for near-real-time deployment.

Coal preparation plants pursue maximum economic benefit, yet product structure optimization under fluctuating coal quality and changing market demand is a coupled decision-making problem involving the organization of primary products such as lump clean coal, clean coal, raw fine coal, coal slime, and gangue, together with commercial coal blending and process-scheme selection. Conventional optimization methods that focus on a single stage are often insufficient to address such complex coordinated decisions. To this end, a GPSOM–WOA nested optimization model was developed to achieve the coordinated optimization of primary product separation, commercial coal blending, and process-scheme selection under the objective of economic benefit maximization. In the outer layer, where process-scheme selection and primary product structure adjustment involve both discrete decisions and continuous variables, a simplified Group-based Particle Swarm Optimization with Multiple Strategies (GPSOM) was employed to search the primary product structure parameters and generate engineering-feasible primary product balance tables. In the inner layer, where the commercial coal blending problem is subject to multiple constraints, including ash content, moisture, calorific value, and supply demand, the Whale Optimization Algorithm (WOA) was adopted to optimize blending ratios within a restricted feasible region. A piecewise penalty function was introduced for quality-limit violations to support profit-oriented constrained optimization. Subject to commercial coal quality constraints on ash content, moisture, and calorific value, a case study of a coal preparation plant in Inner Mongolia was conducted to compare product structures and economic benefits under different process conditions. The results show that the proposed model can realize the joint optimization of primary product structure and commercial coal blending, and can provide a quantitative basis for product structure optimization and process selection in coal preparation plants.

This work investigates the behavior of carbon dioxide (CO2) near the surface of different single-atom alloys to evaluate their potential as catalysts for decarbonization processes. Specifically, 26 transition metals from the first three transition series, alloyed with three low Miller index copper supports, were considered. Adsorption energies and distances of linear CO2, trigonal CO2, and CO* + O* on the surfaces were calculated using the semiempirical computational method MOPAC-PM7. Additionally, activation energies were determined from previously published research. The proposed methodology is less computationally demanding than DFT studies, and results show good agreement with both experimental and simulated data. This approach provides a computationally efficient methodology for screening promising materials that convert CO2 into valuable products, such as methane and methanol.

Images of wind turbine blades captured by drones often feature complex backgrounds, and small targets such as minor defects or images have low resolution, leading to reduced recognition rates. To address environments with complex feature backgrounds, this paper proposes the PPS-MSDeim model. Based on the lightweight end-to-end detection framework DEIM-N, it introduces three core innovations to tackle the challenge of detecting small, irregular defects on wind turbine blades against complex backgrounds. First, we design an inverted multi-scale deep separable convolutional module (MDSC). After compressing channels via a bottleneck layer, it concurrently processes 3 × 3, 5 × 5, and 7 × 7 inverted deep separable convolutions. By first fusing channel information and then extracting multi-receiver-field spatial features, this approach enhances the ability to characterize morphologically variable defects while reducing computational overhead. The MDSC is then embedded into the backbone network HGNetv2. Second, we construct a Multi-Scale Feature Aggregation and Diffusion Pyramid Network (MFADPN). Through a Multi-Scale Feature Aggregation Module (MSFAM), it directly fuses features from layers P2 to P5, achieving deep integration of high-level semantics and low-level details. Combining dilated convolutions with expansion ratios of 1, 3, and 5 captures multi-level context, and a Sobel edge branch is introduced to enhance defect contours; subsequently, a feature diffusion operation is performed to distribute the enhanced features back to each level, shortening information paths and preventing signal decay; simultaneously, a high-resolution detection head is added to P2 and the P5 head is removed to improve sensitivity for small object detection. Finally, we propose the PPSformer module to replace the original Transformer encoding layer. It uses patch embedding to convert images into sequences and introduces a multi-head probabilistic sparse self-attention mechanism that focuses only on key-value pairs during attention computation. This design efficiently captures irregularly varying feature information and globally detects data anomalies induced by external defects. This study uses real engineering data sets, and the results show that PPS-MSDeim, based on DEIM, increased mAP@0.5 by 6.7%, reaching 95.1%. mAP@0.5–0.95 increased by 12.0%, reaching 70.1%. This indicates that the proposed method has a significant advantage in detecting defects in wind turbine blades.

Tritium self-sufficiency is a fundamental design requirement of a fusion fuel cycle, necessitated by the limited global availability of tritium relative to the fuelling demands of a fusion reactor. Minimising tritium losses within a fuel cycle is therefore essential. The Hydrogen Isotope Separation System (HISS) employs cryogenic distillation technology to remove excess protium and deuterium while rebalancing the deuterium–tritium (DT) mixture required for reactor operation. However, the HISS design involves a trade-off between reduced tritium emissions and increasing internal tritium inventory, both contributing to the overall tritium losses. In this work, a multi-objective Bayesian Optimisation (BO) framework based on an ε-constraint formulation is developed to construct Pareto-optimal solutions to compare alternative HISS architectures. Gaussian Process surrogate models derived from physics-based Aspen Plus simulations are used to resolve the non-linear relationships between design variables and performance metrics, including tritium inventory, tritium emission losses, and bottom-product purity. Application of the framework to representative case studies demonstrates that tritium emission losses significantly exceed tritium decay losses associated with internal inventory hold-ups across the investigated operating conditions. The proposed framework enables quantitative comparison of equilibrator integration strategies to compare HISS architectures and assess their impact on tritium losses within the fusion fuel cycle.

With the gradual depletion of conventional hydrocarbon resources, low- and ultra-low-permeability reservoirs have become important targets for oil development. Nanoemulsions exhibit great potential for enhanced oil recovery because of their favorable interfacial activity, small droplet size, and excellent transport capability. However, the interfacial dynamics and capillary mechanisms involved in microscale two-phase displacement processes remain poorly understood. In this study, a self-developed micro-capillary bundle apparatus was used to investigate nanoemulsion displacement behavior in micrometer-scale capillaries. The interfacial behavior was quantitatively analyzed based on the relationship between interface velocity and pressure difference (v-ΔP). The results show that the displacement process follows the classical Washburn equation, with a linear relationship between v and ΔP. During oil displacement, the capillary force remains negative and acts as a resistance, indicating a pressure-driven forced displacement mechanism. Environmental factors such as temperature, electrolyte concentration, and wettability have limited effects, whereas pore size plays a dominant role. The addition of an appropriate amount of microspheres can reduce capillary resistance and lower the required driving pressure. The present findings mainly reveal the interfacial motion characteristics and capillary mechanisms of nanoemulsions in microscale pore throats, providing a fundamental basis for understanding fluid transport behavior in low-permeability reservoirs.

Mango leaves (Mangifera indica), an underutilized residue, represent a promising source of functional proteins with potential applications in emulsion-based delivery systems. Leaf protein concentrate (LPC) was extracted and modified by high-intensity ultrasound (HIU) to enhance its techno-functional properties. The modified protein was subsequently used as a natural emulsifier to develop oil-in-water (O/W) emulsions enriched with Curatella americana leaf extract, a phenolic-rich source of antioxidant bioactive compounds. Ultrasound-assisted emulsification (UAEm) conditions were optimized using a Box–Behnken experimental design, evaluating the effects of protein concentration (0.5, 1, and 1.5%), oil-to-water ratio (1:4, 1:4.5, and 1:5, mL:mL), and sonication time (2.5, 5, and 7.5 min) on droplet size (D[4,3], µm). The optimized formulation consisted of 1.5% protein, an O/W ratio of 1:4 mL, and a time of 7.5 min, producing an emulsion with a droplet diameter of 7.23 µm. The emulsions exhibited high resistance to storage, pH variation (2–10), ionic strength (100–500 mM NaCl), and thermal treatments up to 50 °C. Additionally, incorporating C. americana extract enhanced thermal stability, photostability, and antioxidant retention under UV exposure, suggesting the formation of reinforcing protein–polyphenol interactions. These findings demonstrate the potential of mango leaf protein as a sustainable emulsifier and protective carrier for sensitive bioactive compounds, supporting its application in functional food and nutraceutical formulations.

Biodiesel fuel produced through transesterification is mainly used in blends with conventional diesel fuel (D100). The analysis of the combustion process parameters for each specific biodiesel fuel represents the basis for a rational approach to the utilization of available motor fuel quantities. In this study, the differential and cumulative heat release laws during the combustion of D100 and blends of biodiesel fuel made from waste grape seed oil and D100 were analyzed. In addition, the engine efficiency and economy for the cases of using the aforementioned fuels were analyzed. The tests were conducted on a single-cylinder, air-cooled diesel engine with direct fuel injection. The engine testing was conducted for two engine loads; that for which the brake was a mean effective pressure of 4.2 bar, and for the full load, that for the brake was a mean effective pressure of 5.6 bar at engine speeds of 1635 rpm, 1937 rpm, and 2239 rpm. All experimental work was conducted for conventional diesel fuel D100 and for biodiesel diesel blends B7 and B14. The combustion rates of D100, a blend containing 7% of biodiesel by volume (B7), and a blend containing 14% of biodiesel by volume (B14) were examined. However, the higher combustion rate of the B14 blend, particularly during the combustion of the first 50% of the fuel mass per cycle, could have a positive impact on the fuel economy of the working cycle and the brake thermal efficiency (BTE). The maximum heat release rates for D100, B7, and B14 at full load and an engine speed of 2239 rpm are 115.65 J/deg, 148.01 J/deg, and 152.99 J/deg, respectively. At full load and engine speeds of 1635 rpm and 2239 rpm, the brake thermal efficiencies (BTEs) for D100, B7, and B14 were 0.301, 0.285, and 0.296 and 0.281, 0.273, and 0.277, respectively. Under other tests, the highest BTE was observed for the B14 blend. Therefore, from the perspective of brake thermal efficiency (BTE), the most favorable blend for application is B14.