Search Results (4,567)

Native cellulose I can be converted into crystalline polymorphs II and III_I, while cellulose II can be further converted into III_II through chemical treatments that induce significant structural, physical, and chemical changes. Accurate identification and differentiation of these polymorphs is essential for predicting fiber reactivity and processing behavior, but current methods are time-consuming. This study demonstrates the potential of near-infrared hyperspectral imaging (HSI-NIR) combined with linear discriminant analysis as a rapid, non-destructive tool for polymorph discrimination. Cellulose I, II, III_I, and III_II were produced from bleached kraft pulps of eucalyptus and pine and from cotton linters using NaOH (20% w/v) and ethylenediamine treatments. HSI-NIR successfully differentiated polymorphs based on spectral signatures in the 1480–1600 nm range, regardless of botanical source. Complementary characterization by XRD confirmed polymorph conversions, showing crystallinity reductions of 10–15% for cellulose I→II and I→III_I conversions, with crystallite size decreasing from 7.2 nm (cotton CI) to 3.2–3.4 nm in all CIII_II samples. XPS analysis revealed increased C-O surface accessibility in cellulose II and III, with complete disappearance of COOH groups in cellulose III samples. These results establish HSI as a promising screening tool for cellulose polymorph identification (>95% classification accuracy) and provide comprehensive baseline data on structural and chemical transformations that govern fiber reactivity in chemical and enzymatic processes. Full article

With the rapid development of electronic devices and wireless communication systems, electromagnetic interference pollution has become a critical concern, driving the urgent demand for high-performance, lightweight, and flexible electromagnetic interference (EMI) shielding materials. To endow fabrics with excellent electromagnetic shielding, a Fe₃O₄/Ag/RGO ternary nanocomposite-coated cotton fabric for electrical conductivity and EMI shielding application was developed. The cotton fabric pretreated with dopamine was coated with graphene oxide (GO), followed by silver nanoparticles (Ag) via a microwave-assisted chemical reduction method, and Ag/reduced graphene oxide (RGO)-coated cotton. Subsequently, nano-ferroferric oxide was deposited on Ag/RGO-coated cotton fabric using a coprecipitation method. The results show that the surface resistance of Fe₃O₄/Ag/RGO-coated cotton fabric arrives at 1.68 Ω/sq, demonstrating excellent electrically conductive performance. Fe₃O₄/Ag/RGO-coated cotton fabric demonstrates outstanding electromagnetic shielding performance, with SE values exceeding 45 dB across the entire 1–18 GHz range. The flexibility and superior electromagnetic shielding performance of Fe₃O₄/Ag/RGO-coated cotton fabric render it a promising candidate for applications in wearable electronics, aerospace, advanced protective systems, and military protective clothing. Full article

Concerns regarding the safety, environmental impacts, and long-term sustainability of pesticide-dependent crop protection have intensified interest in biological control, which suppresses pest populations using natural enemies (predators, parasitoids, and pathogens) within integrated pest management (IPM) programs. This bibliometric study maps the development of biological control research from 2000 to 2025 using records retrieved from the Web of Science Core Collection. The publication trends, collaboration structures, leading countries and institutions, core journals, keyword co-occurrence and clustering, citation bursts, and influential cited references were examined using CiteSpace and VOSviewer. The results show a pronounced increase in publication output after 2011, indicating rapid expansion and consolidation of the field in the last decade. Keyword analyses reveal a thematic shift toward ecosystem-based framing, reflected by the growing prominence of terms such as ecosystem services, habitat management, and ecological intensification, which emphasize landscape- and management-oriented approaches to enhancing pest suppression. Cited-reference patterns highlight the persistent influence of the foundational literature on habitat manipulation, landscape complexity, and conservation biological control. Despite the field’s growth, research gaps remain in integrating biological control with emerging bioengineering tools and explicitly accounting for climate-driven variability across regions and production systems. Full article

Endosymbiotic bacteria in insects are known to influence plant–insect interactions by altering host plant physiology. This study reveals that the endosymbiont Wolbachia significantly impairs photosynthesis in cotton plants. Comparative transcriptomic analysis of cotton leaves infested by Wolbachia-infected spider mites (Tt-I) and uninfected spider mites (Tt-UI) identified 1912 differentially expressed genes (DEGs). Photosynthesis was the most adversely affected biological process, with 17 genes downregulated in the photosynthesis pathway (e.g., key genes psbW and PETF), as supported by GO and KEGG enrichment analyses. Gene co-expression network analysis further highlighted core genes involved in photosynthesis disruption and carbon fixation. Physiological assessments showed that Wolbachia infection led to significantly reduced chlorophyll content and elevated reactive oxygen species (ROS) levels, inducing oxidative stress. These findings demonstrate that Wolbachia disrupts cotton photosynthesis through transcriptional repression and ROS-mediated oxidative stress, providing novel insights into plant–insect-symbiont interactions and a theoretical basis for managing mite pests in cotton. Full article

Apolygus lucorum (Meyer-Dür) is a phytozoophagous crop pest. While the effects of plant-based diets on its development and reproduction have been extensively studied, the combined effects of plant- and prey-based diets on these traits remain poorly understood. This study systematically evaluated the effects of plant-only, prey-only, and mixed plant–prey diets on A. lucorum nymphal survival and development, as well as adult longevity and fecundity, under controlled laboratory conditions. The results demonstrate that diet composition significantly affected nymphal survival and developmental progression. Nymphs fed exclusively on prey (Aphis gossypii Glover or Bemisia tabaci (Gennadius) nymphs) failed to complete juvenile development. Although a diet of Helicoverpa armigera (Hübner) eggs alone enabled some individuals to reach adulthood, survival rates were significantly lower than those in mixed-diet treatments. Mixed feeding markedly improved nymphal survival, with the highest rates observed in groups fed green beans + H. armigera eggs and cotton leaves + B. tabaci nymph combinations (both 64.45%). The developmental duration was also influenced. Mixed diets, particularly green beans + H. armigera eggs, significantly shortened each instar and the total developmental time (11.04 ± 0.17 d), whereas a diet of cotton leaves alone prolonged development (19.45 ± 0.24 d). Adult longevity and reproductive output were likewise diet-dependent. The longest lifespans were recorded in adults fed green beans alone or green beans + H. armigera eggs, while the shortest lifespan was observed for those fed only cotton leaves. Successful oviposition was only achieved following four dietary treatments: green beans alone, green beans + H. armigera eggs, H. armigera eggs alone, and cotton leaves + H. armigera eggs. Among these, the green bean + H. armigera egg diet yielded the best reproductive performance, featuring the shortest pre-oviposition period (5.82 ± 0.60 d), the longest oviposition period (19.41 ± 1.68 d), and the highest mean fecundity per female (238.35 ± 25.51 eggs). This underscores the reproductive advantage of a mixed plant–prey diet. This study clarifies how dietary conditions shape the survival, development, and reproduction of A. lucorum, highlighting its strong reliance on nutritional quality for key life-history traits. These findings offer valuable insights into the ecological adaptations underlying the feeding behavior of this insect. Full article

The textile and clothing industry exerts a significant environmental impact in the EU, contributing heavily to water, land, and resource depletion, with waste generation expected to rise sharply due to fast fashion trends. Accelerating circularity and closed-loop production is critical to reduce the sector’s ecological footprint. This study investigates newer approaches for the removal of indigo prints from cotton (CO) and polyester (PES) textiles using aqueous-based solutions and/or ozone treatment. Aqueous alkaline solutions containing reducing agents and surfactants were evaluated, as well as dry and wet ozone treatments. The efficacy of colour removal was assessed via spectrophotometric analysis [colour strength (K/S) and colour difference (ΔE)] and the fabrics were tested for dimensional stability and tensile strength before and after treatment. Results reveal that surfactant-assisted aqueous treatments enable effective pigment removal and maintain textile properties, supporting subsequent reprinting for textile upcycling. Wet ozone treatment also promoted substantial decolourisation, particularly in cellulosic substrates. Although PES samples exhibited better mechanical resistance, they revealed limited pigment extraction upon ozone treatment. These findings demonstrate the potential of chemical treatments using aqueous-based solutions and surfactants for circular textile applications, facilitating pigment removal without compromising substrate integrity, and boosting the upcycling. Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

DC fault arcs comprise one of the most serious safety hazards in photovoltaic systems, and their danger far exceeds that of AC arcs. DC arcs lack a natural zero-crossing point, and their burning time can last from several seconds to several minutes, which is sufficient to ignite cable lines and surrounding combustibles, causing fires. To explore the characteristics and mechanism of the ignition of external combustibles by DC fault arcs, this paper, based on the theory of magnetohydrodynamics (MHD), constructed a three-dimensional numerical simulation model of a DC fault arc considering the coupling of electromagnetic, thermal, and flow fields. A DC fault arc experimental platform that can simulate the actual working conditions of photovoltaic systems was built to verify the accuracy of the model. Based on this, by integrating the complex pyrolysis model and the combustion reaction model, and selecting cotton fibers as the typical combustible indicator substances, as stipulated in the UL 1699 standard, a coupled simulation model for the ignition of solid combustibles by direct current fault arcs was established. The numerical simulation of the entire ignition process of the arc was realized, and the coupling mechanism of heat transfer, mass transfer, and chemical reactions during the ignition process was revealed. The research results of this paper fill a research gap in the numerical simulation of arc ignition caused by DC faults in photovoltaic systems, clarify the fire ignition risk patterns of DC fault arcs under different working conditions, and provide important theoretical support and technical references for the formulation of arc fire prevention strategies and the optimized design of fault arc protection devices for photovoltaic systems and other DC power systems. Full article

Mineral powder–based bone grafts exhibit excellent osteoconductivity; however, their clinical efficacy is often compromised by insufficient early-stage tissue ingrowth, leading to particle aggregation and pocket formation within the defect site during the initial healing phase. Here, we report a cotton-type nanofiber-guided mineral graft designed to overcome this early integration failure by creating fibrous pathways for tissue ingress. Cotton-type polycaprolactone (PCL) nanofibers were fabricated via electrospinning using a pin-based collector engineered to induce strong inter-fiber repulsion, resulting in a highly expanded, three-dimensional cottony architecture. Tetracalcium phosphate (TTCP) and α-tricalcium phosphate (α-TCP) mineral particles were subsequently deposited onto the surface of the cottony nanofibers, forming a fibrous–mineral hybrid graft (c-NF@T/α-TCP) in which the nanofibers act as a transient, functionally defined tissue-guiding framework during the early healing phase. The cottony nanofiber network effectively prevented mineral particle aggregation and generated continuous pathways within the graft, facilitating early tissue infiltration and vascular ingress during the first week after implantation. In vivo evaluation in a bone defect model demonstrated that c-NF@T/α-TCP significantly reduced tissue pocket formation at early time points and promoted subsequent bone regeneration compared to mineral powder-only grafts. This study highlights the critical importance of early-stage structural guidance in mineral-based bone grafts and introduces cotton-type nanofiber–guided pathway engineering as a simple yet effective strategy to unlock the regenerative potential of conventional inorganic bone substitutes. Full article

The fading affect bias (FAB) is the faster fading of unpleasant than pleasant affect, and this effect is positively and negatively related to healthy/adaptive and unhealthy/non-adaptive outcomes, respectively. Research has argued that the FAB makes people happy and it prompts them to seek out pleasant experiences. Although Pillersdorf and Scoboria found a negative relation between the FAB and marijuana consumption, they only examined non-marijuana events. This investigation was limited because the relation between the FAB and marijuana consumption may be absent or positive for marijuana events. The current study examined the relation of the FAB to marijuana consumption measures across marijuana and non-marijuana events. The study was conducted both in person (Experiment 1; n = 328) and online (Experiment 2; n = 232). Analyses included ANOVAs to examine fading affect across initial event affect and event type, and Process Model 1 was used to evaluate the fading affect bias across initial event affect in 2-way interactions with continuous variables. Process Model 3 was used to investigate fading affect across initial event affect and event type in 3-way interactions with continuous variables. Both experiments showed a robust FAB that was positively related to adaptive variables and negatively related to non-adaptive variables, and it was positively related to marijuana consumption/reactivity. In addition, the positive relations between FAB and marijuana consumption (hours) and reactivity (highness) measures in Experiment 1 and a marijuana reactivity measure (highness) in Experiment 2 were only found for marijuana events. Implications, limitations, and future research directions are discussed. Full article

This study presents the first cradle-to-gate life cycle assessment (LCA) of T-shirt production using viscose and Lyocell fibers, benchmarked against cotton and polyester under consistent system boundaries. The analysis covers spinning, knitting, wet processing, garment assembly, and regionalized energy supply. Results show that cotton T-shirts exhibit the lowest global warming potential (14.1 kg CO₂eq/kg) but the highest water demand (2.9 m³/kg) in China. Polyester garments, although less water-intensive, contribute significantly to plastic accumulation (1.0 kg/kg shirt) compared to cellulose-based fibers (0.1 kg/kg shirt). Within man-made cellulose fibers, Lyocell generally outperforms viscose in toxicity-related categories—reducing freshwater ecotoxicity by 35% and human non-carcinogenic toxicity by 62%—thanks to its closed-loop solvent recovery. However, Lyocell also shows the highest carbon footprint (21.6 kg CO₂eq/kg) unless produced in regions with cleaner energy mixes. Regional sensitivity analysis indicates that shifting production from China to Brazil could reduce global warming impacts by up to 38%. Overall, these results highlight the trade-offs across fiber types and demonstrate the importance of both material choice and production geography in driving sustainability within textile supply chains. Full article

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches. Full article

The necrotrophic pathogen Sclerotinia sclerotiorum compromises the physiological and anatomical integrity of cotton, leading to substantial economic losses due to rapid tissue necrosis, stem blight, boll rot, and leaf wilting. In this context, the use of endophytic microorganisms emerges as a promising strategy for the biocontrol of white mold. This study tested the hypothesis that endophytic fungal strains isolated from the roots of Butia purpurascens, a palm tree endemic to the Cerrado biome, could mitigate disease symptoms in Gossypium hirsutum L. To evaluate this, cotton plants were subjected to biotic stress imposed by S. sclerotiorum to assess the effectiveness of seven fungal strains in attenuating disease. The impact of the pathogen was monitored through growth variables, gas exchange, leaf temperature, chlorophyll a fluorescence, antioxidant enzyme activity, proline and malondialdehyde (MDA) levels, and the incidence of rot in petioles, leaves, and flower buds. Overall, inoculation with endophytic fungi significantly alleviated the effects of the phytopathogen, promoting vegetative growth and optimizing physiological performance. Treated plants exhibited alleviated stress in primary photochemistry, reduced non-photochemical energy dissipation, and stable carbon fixation. Additionally, efficient modulation of the antioxidant system and preservation of anatomical structures were observed, minimizing the severe symptoms of white mold. Notably, the non-pathogenic strains BP10EF (Gibberella moniliformis), BP16EF (Penicillium purpurogenum), and BP33EF (Hamigera insecticola) acted as potent physiological modulators, yielding responses similar to those of healthy plants. These results highlight the biotechnological potential of these endophytic strains, which can be explored as both growth promoters and resistance inducers in cotton against white mold. Full article

The growing environmental impacts associated with conventional plastics and textiles have intensified interest in bio-based and circular material alternatives. This study presents a qualitative and structured literature review of the valorization of fruit and nut agricultural residues as sustainable feedstocks for biomaterials and biotextiles, with a strategic focus on Greece. Drawing on international literature, regional agricultural production data, and validated processing technologies, the review synthesizes existing evidence on residue availability, conversion routes, environmental performance, and market trends. The reviewed literature indicates that residues such as grape pomace, olive by-products, citrus peels, and nut shells have been widely reported as suitable sources of cellulose, lignin, and pectin for the development of fibers, films, and composite materials. Findings from published life cycle assessment (LCA) studies suggest potential reductions in water use, greenhouse gas emissions, and land-use intensity compared with conventional cotton and synthetic textiles, although results vary depending on system boundaries and processing conditions. The review further highlights enabling factors, technical limitations, and policy considerations relevant to the Greek context. This study provides a qualitative integrative perspective on the opportunities and constraints associated with agricultural residue valorization, identifying key research gaps and strategic directions for future development within Greece and similar Mediterranean regions. Full article

This study presents a highly efficient and sustainable biocatalytic platform for bisphenol A (BPA) bioremediation through the covalent immobilization of laccase onto hierarchically functionalized cotton fibers. The immobilization strategy involved selective periodate oxidation of cellulose, grafting a hexamethylenediamine (HMDA) spacer arm, and glutaraldehyde activation, ensuring stable covalent attachment. Characterization via FTIR, SEM, and BET confirmed successful surface modification and high enzyme loading, achieving an immobilization yield of 90.5%. The immobilized laccase (CT-DA-HMD-Lac) exhibited significantly enhanced performance compared to the free enzyme, with a two-fold increase in maximum reaction velocity (V_max) and a 75% improvement in catalytic efficiency of action (V_max/K_m). Furthermore, the biocatalyst demonstrated superior robustness, maintaining high activity across broader pH and temperature ranges, and retaining 75% of its initial activity after 15 consecutive reusability cycles. Storage stability was also markedly improved, with 83% activity retention after 60 days. Practical application in BPA degradation showed 85% removal efficiency within 300 min, a 2.4-fold increase in the degradation rate constant over the free enzyme. These results highlight functionalized cotton as a promising, cost-effective, and scalable support for advanced enzymatic wastewater treatment and the remediation of persistent endocrine-disrupting chemicals. Full article