Search Results (31,759)

In addition to its well-documented biological properties, Aloe barbadensis Miller (Aloe vera) can serve as an effective substrate for fermentation involving lactic acid bacteria. In this study, Aloe vera gel was fermented using Lactobacillus strains (L. plantarum, L. rhamnosus, L. fermentum, L. paracasei). The chemical composition of the gel and its fermented products were analyzed using chromatographic methods, identifying key bioactive compounds, including the predominant aloesin. Antioxidant activity was assessed using chemical methods (DPPH, ABTS, FRAP) and in an in vitro cellular model, analyzing superoxide dismutase activity and the level of reactive oxygen species in skin cells. The L. rhamnosus ferment exhibited the strongest antioxidant properties. Cytoprotective properties were evaluated in HaCaT and HDF cell lines. The tested samples showed no cytotoxicity, and the ferments often outperformed the unfermented gel. Moreover, the developed model cosmetic gel based on fermented Aloe gel stimulated keratinocyte proliferation. Additionally, it was demonstrated that the ferments effectively inhibit the activity of enzymes associated with skin aging processes (collagenase, elastase, hyaluronidase), often more effectively than the unfermented Aloe gel. The results obtained indicate the potential of fermented Aloe vera gel as an ingredient in products that protect the skin. Full article

This study reports that titanium nanoparticles can be used as a surface coating to enhance the corrosion resistance of 316 stainless steel. It specifically examines the influence of the deposition temperature (T_dep) on the coating’s structural and morphological properties, including corrosion behavior. TiO₂ nanoparticles were thoughtfully deposited on steel substrates at temperatures of 300, 400, and 500 °C using a horizontal hot-wall tubular reactor. This equipment was expertly engineered at the CIDETEQ laboratory through the metal–organic chemical vapor deposition (MOCVD) concept. Titanium isopropoxide [Ti(OC₃H₇)₄] was used as the precursor for the coating synthesis. Structural analysis was conducted using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM). Corrosion performance was evaluated under accelerated conditions in 0.5 M H₂SO₄ using potentiodynamic anodic polarization (AP), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The corrosion test indicates that increasing T_dep significantly differentiates the coating morphology and improves corrosion resistance. AP revealed that the pitting potential (E_pit) shifted to more positive values, ranging from +1.4 to +1.5 V. CV voltammograms indicated that coated samples had lower passive current densities (I_p ≈ 10⁴ to 10⁵ A/cm²) than the bare substrate. EIS analysis demonstrated that the coating deposited at 500 °C processed the most favorable electrochemical performance, resisting corrosion for over 28 days. This coating achieved the highest electrical resistance (297 kΩ·cm²) and the lowest capacitance (2.7 μF/cm²), attributed to the formation of a crystalline anatase phase composed of pyramidal-like nanoparticle agglomerates (~40 nm). The dense packing structure effectively blocks charge-transfer pathways, restricting electron and ion transfer. Finally, MOCVD-based chemical surface modification with TiO₂ nanoparticles is considered an innovative method to improve the corrosion resistance of stainless steel, thereby prolonging its durability under accelerated sulfuric acid exposure. Full article

Fully compressible two-phase flow configurations present many challenges for numerical modelling, requiring the development of Real Fluid Models (RFMs) able to simulate flows in subcritical, transcritical and supercritical regimes. Such an RFM has been recently developed at IFPEN based on physical properties lookup tables, mainly for binary and ternary chemical systems. This paper proposes an Artificial Neural Network (ANN) approach to overcome the limitations of lookup tables of thermodynamic properties and to apply RFM to multi-species combustion. A methodology for generating an optimized data set by combining a vapor–liquid equilibrium (VLE) thermodynamic solver and the in situ adaptive tabulation (ISAT) method is developed. It aims to improve the neural network training process for two-phase combustion simulations where many species are present. This ANN methodology has been implemented in the CONVERGE CFD solver and validated using a mixing layer (LOX/GH₂) benchmark from the literature relevant to rocket conditions, and an academic gaseous (H₂/O₂) case relevant to hydrogen combustion. The results show that this ANN approach makes H₂ combustion simulation possible when coupled to the RFM framework and using a 10-species kinetic mechanism. Full article

Accurate prediction of effluent water quality is essential for the intelligent and sustainable operation of wastewater treatment plants (WWTPs). However, this task remains challenging due to the strong nonlinearity, long-term temporal dependencies, and severe fluctuations inherent in influent characteristics. In this study, a novel data-driven framework termed the Multi-Scale Temporal Representation-Enhanced Informer (MTRE-Informer), is proposed to predict key effluent quality indicators, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). To ensure data quality and computational efficiency, a generative recurrent learning framework is first employed for anomaly detection and correction, followed by variance inflation factor (VIF)-based feature selection to mitigate multicollinearity. Furthermore, feature contribution analysis is conducted to improve model interpretability. Subsequently, the core MTRE-Informer architecture utilizes hierarchical multi-scale temporal representation learning to simultaneously capture local patterns and long-term dependencies within the complex dynamics of the wastewater treatment process. Experimental results demonstrate that the MTRE-Informer achieves robust and stable predictive performance across diverse operational datasets. For TN prediction, the proposed framework attains a coefficient of determination () of 0.9637 and a mean absolute percentage error (MAPE) of 3.39%. Compared with baseline approaches, the improvement ranges from 3.8% to 14.2%, validating its superior capability. To further enhance model robustness, an anomaly detection and correction strategy based on a generative recurrent learning framework is employed. In addition, feature contribution analysis and VIF-based feature selection are conducted to improve interpretability, mitigate multicollinearity, and enhance computational efficiency. Overall, this framework provides a reliable and practical solution for real-time effluent quality prediction, facilitating the intelligent management of WWTPs. Full article

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

This study investigates the development of high-strength biofiber cement boards with enhanced thermal insulation properties by utilizing recycled biofibers derived from cement packaging bags, combined with a water-based adhesive to enhance the curing efficiency of Portland cement through a cementation–curing process. This approach reduces waste from cement packaging and other biofiber residues through recycling, thereby promoting environmental sustainability. Moreover, it does not require the use of additional chemicals for the disposal or treatment of fiber waste, nor does it require the incineration of biofiber waste. Recycled biofiber from cement bags, composed primarily of cellulose (60 wt%), lignin (15 wt%), and hemicellulose (10 wt%), serves as a reinforcing phase, while the cement and adhesive mixture functions as a strong binding matrix. The fabrication of composite materials using undamaged cement bag fibers preserves fiber integrity and enables a well-ordered one-dimensional (1D) fiber alignment, which promotes more effective reinforcement than two-dimensional (2D) or three-dimensional (3D) orientations, in accordance with the rule of mixtures. In addition, the incorporation of a water-based PVAc adhesive accelerates the curing rate of the cement phase, promoting the formation of a strong interconnected network structure, and facilitates a more complete curing process. The physical, mechanical, chemical, and thermal properties of the biofiber cement boards were evaluated in accordance with relevant industrial standards, including TISI 878:2023, BS 874, ASTM C1185, ASTM D570, ASTM C518, ISO 8301, and JIS A1412. The results indicate that an optimal cement mortar to water-based adhesive ratio of 1:2, combined with an increased number of biofiber sheet layers, significantly enhances material performance, particularly in Formulas (7)–(9). Among these, Formula (9) exhibits the lowest water absorption (0.0835 ± 0.0102%), the highest tensile strength (19.489 ± 0.670 MPa), the highest flexural strength (20.867 ± 2.505 MPa), the highest Young’s modulus (5735.068 ± 387.032 MPa), and low thermal conductivity (0.152 W/m.K). The resulting boards demonstrate strong bonding ability, enhanced resistance to fire, moisture, and weathering, and a longer service life compared to lower cement-to-adhesive ratios (1:1 and 1:0). These findings demonstrate the potential of recycled biofiber composites, combined with water-based adhesives, as sustainable alternative materials for thermal insulation and structural applications, including ceilings and walls in building construction. Full article

Sapphire, with excellent optical properties and high hardness, has become a key hard and brittle material component in extreme environments like aviation equipment and infrared detection systems. Its processing quality directly determines the performance of various equipment systems. To address processing defects, technologies such as multi-wire cutting, magnetorheological polishing, chemical mechanical polishing, femtosecond laser processing, and ion beam etching have been developed and studied to improve the surface quality of sapphire components. This paper focuses on key technologies, including sapphire’s nano-scale surface morphology control, intrinsic nano-surface atomic-level defect control, and combined process systems for precision and shape control. These technologies lay the foundation for sapphire components’ process chain manufacturing to achieve high-precision shape and surface quality control. Full article

Background: The transition from fossil-derived polymer additives to renewable alternatives is essential to mitigate environmental persistence and ensure chemical safety within the plastics industry. This review provides a comprehensive overview of recent developments in bio-based functional additives and their integration into circular economy frameworks. Methods: Following PRISMA guidelines, a systematic literature search was conducted using the Scopus database for studies published between 2023 and 2026. Search terms targeted bio-based plasticizers, flame retardants, antioxidants, and compatibilizers. Studies were screened against predefined inclusion criteria, specifically focusing on experimental validation in polymer matrices, while data mining was employed to map emerging research fronts. Results: From an initial 996 records, 54 studies were selected after removing duplicates and ineligible articles. The findings highlight a paradigm shift from passive physical fillers toward active, multifunctional macromolecular agents. Recent literature demonstrates that targeted molecular interventions, such as phosphorylated lignin and biomimetic structures, can resolve trade-offs between ductility and thermal stability at low loadings (<5 wt%). Synthesis routes, performance outcomes, and end-of-life trajectories for each additive class are summarized. Conclusions: Bio-based additives have evolved from simple substitutes into strategic tools for the molecular programming of sustainable polymers. Although challenges regarding scalability and high-temperature processing persist, their integration into circular economy strategies establishes a clear roadmap for next-generation bioplastics. Full article

Epoxy-glass-mica composite materials are widely used as electrical insulating materials in high-voltage rotating machinery due to their layered structure and excellent dielectric properties. Taking the F-class epoxy glass with a small amount of rubber powder mica tape commonly used as the main insulation of wind turbine stator coils as the research object, 7-day, 14-day, 21-day, and 28-day low-temperature treatment tests were conducted at −50 °C. The surface morphology and chemical structure changes of the materials were characterized by SEM and FTIR, and the influence laws of low-temperature treatment on the electrical properties of the mica tape insulation materials were systematically studied. The experimental results show that the low-temperature environment will induce microcracks and interface delamination and other structural damages, but no obvious change in the chemical structure of the mica tape was observed. With the extension of the low-temperature treatment time, the electrical properties of the mica tape show a deteriorating trend, and after 28 days of low-temperature treatment, the breakdown field strength of the F-class mica tape decreased by approximately 18.5%, and the volume conductivity overall increased by about two orders of magnitude. This indicates that the microcrack defects induced by low-temperature will lead to an enhanced electrical-thermal coupling effect in the insulation structure, thereby accelerating the degradation process of the insulation material. This reveals the degradation mechanism of wind turbine stator main insulation from “structural damage” to “performance degradation” and then to “insulation aging” under low-temperature conditions, providing a theoretical basis for the design and reliability assessment of insulation systems in wind turbine generators in cold regions. Full article

This research evaluates the feasibility of using a lignocellulosic biosorbent prepared from mature leaves of Asplenium scolopendrium (produced through simple mechanical processing of the leaves, without applying any chemical modification or heat treatment) for the removal of methylene blue from water. Before and after adsorption the material was characterized using SEM technique and color analysis. Subsequently, the adsorption behavior was analyzed by examining equilibrium, kinetic, and thermodynamic aspects of the process. The equilibrium data were best represented by the Sips isotherm model, while the adsorption rate followed the Avrami model. Thermodynamic evaluation indicated that the retention of the dye occurs predominantly through a physical adsorption mechanism, while a minor contribution from chemisorption may be present, slightly enhancing the overall dye uptake. Process optimization was performed using the Taguchi experimental design, which also allowed the identification of the most significant operational variable. In addition, analysis of variance (ANOVA) was applied to quantify the contribution of each factor affecting dye removal efficiency. Among the investigated variables, time showed the strongest influence (72.65%), whereas temperature had a negligible effect (1.33%). The maximum adsorption capacity reached 174.1 mg/g, surpassing the performance of several comparable biosorbents reported in the literature. Overall, the findings demonstrate that Asplenium scolopendrium (hart’s-tongue fern) leaves represent an inexpensive, sustainable, and efficient material for eliminating methylene blue from aqueous solutions. Full article

The combination of Fourier-transform infrared (FTIR) spectroscopy and machine learning gives a promising result in wheat variety classification. This study aimed to evaluate the contributions of distinct spectral regions and their combinations to classification performance. Out of the full raw spectra of four bread wheat varieties, namely Altindane, Cavus, Flamura-85, and Nevzatbey, 15 spectral datasets were prepared. Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) models were trained and analyzed. The highest classification performance was obtained using spectral regions associated with protein and lipid bands. The highest average accuracy of 0.9895 was shown by the SVM model, while the ANN produced comparable results with lower variability. Additionally, Variable Importance in Projection (VIP) analysis identified the most influential spectral bands in the protein (Amide II, ~1542 cm⁻¹) and carbonyl (1744–1715 cm⁻¹) regions. These findings indicate that classification is driven by chemically meaningful features rather than purely statistical patterns. The approach followed in this study provides an insight that, in FTIR-based classification, when rigorously evaluated using nested cross-validation, spectral region selection can outweigh model complexity. This approach demonstrates strong potential for rapid and non-destructive assessment, especially for real-time applications in grain processing and automated sorting systems. Full article

The coexistence of emulsified oil, dissolved organics, and heavy metal ions in industrial oily wastewater makes one-step treatment highly challenging. Herein, an organic-inorganic co-modified PVDF composite membrane (MTSP) was fabricated via nonsolvent-induced phase separation, with tea polyphenols, SiO₂, and fibrous MXene synergistically incorporated. The resulting membrane exhibited a superhydrophilic/underwater oleophobic surface, with a water contact angle of 1° and an underwater oil contact angle of ~136°, owing to the optimized surface chemistry and hierarchical pore structure. As a result, the MTSP membrane effectively suppressed oil fouling while enabling rapid water transport. At 0.1 bar, the optimized membrane delivered an oil/water separation efficiency of ~99.5% and a high flux of 2420–2670 L·m⁻²·h⁻¹, while maintaining >99% separation efficiency for various emulsified oils, including kerosene, edible oil, n-hexane, and 1,2-dichloroethane. It also showed excellent recyclability and chemical stability, retaining >98–99% efficiency after five cycles and after 24 h exposure to pH 1 and pH 12 conditions. Notably, for complex simulated wastewater containing emulsified kerosene, phenol, and Fe³⁺, Cu²⁺, Zn²⁺, and Cd²⁺, the membrane maintained ~99% oil/water separation efficiency and simultaneously removed ~79% of phenol and 70–86% of heavy metal ions in a single filtration process. The superior performance is attributed to the synergistic effects of the superhydrophilic/underwater-oleophobic membrane surface, hierarchical transport channels enabling rapid water permeation, and multifunctional sites that adsorb/coordinate dissolved pollutants. This work provides a simple, scalable design strategy for PVDF-based membranes that integrate high-flux separation, antifouling performance, and multi-pollutant remediation for the treatment of complex oily wastewater. Full article

Wheat processing generates large volumes of co-products, particularly wheat bran (WB) and wheat germ (WG), which remain underutilized despite their high content of dietary fiber, phenolic compounds, bioactive peptides, and lipophilic antioxidants. Although their composition and processing have been widely investigated, an integrated and application-oriented evaluation of these fractions remains limited. This review provides a structured and critical analysis of WB, raw and defatted WG, and wheat germ oil (WGO), linking composition, processing strategies, and functional performance within a unified framework. Conventional and emerging technologies, including enzymatic hydrolysis, fermentation, thermomechanical treatments, and supercritical CO₂ extraction, are discussed in terms of selectivity, impact on techno-functional properties, and scalability. An evidence-grading approach is introduced to distinguish bioactivities supported by chemical assays, cell-based models, animal studies, or human data, enabling a more rigorous interpretation of health-related effects. Across applications, these co-products have been incorporated into food systems and related sectors, primarily showing improvements in nutritional composition, oxidative stability, and product performance under experimental conditions. However, translation to an industrial scale remains constrained by techno-economic limitations, regulatory requirements, and stability challenges. This work highlights the need for integrated processing strategies aligned with industrial feasibility to support the development of sustainable cereal biorefineries. Full article

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

This study aims to evaluate the blanching process of wasted Undaria pinnatifida as a ruminant feed source by assessing chemical compositions, in vitro nutrient digestibility, rumen fermentation characteristics, greenhouse gas emissions, and rumen microbes. The blanching process was conducted at different temperatures (15 vs. 80 vs. 90 °C) and times (2 vs. 4 min) to assess the chemical and mineral contents. Supplementation levels of U. pinnatifida (0 vs. 0.5 vs. 1 vs. 2%) were observed with the blanching process (non-blanching (NBL) vs. blanching (LOS)). Increasing blanching temperature and time decreased (p < 0.05) dry matter, crude ash, and the mineral contents, including sodium, phosphorus, and arsenic. Moreover, LOS treatment increased (p < 0.01) in vitro dry matter and neutral detergent fiber digestibility, ruminal pH, and the acetate-to-propionate ratio, but reduced (p < 0.01) CH₄ (mL/g NDFD). Additionally, 2% of LOS treatment reduced the abundance of protozoa, fungi, fibrolytic microbes, methanogenic archaea, Methanobrevibacter ruminantium, Methanosarcina barkeri, and Methanosphaera stadtmanae (p < 0.01). Therefore, blanching at 80 °C for 2 min improved the nutritional profile by reducing antinutritional minerals. Subsequent in vitro fermentation suggested that supplementing the diet with 0.5–1% of LOS improved digestibility and altered fermentation, potentially reducing methane yield (per NDFD). Full article