Search Results (105)

Laccase plays an important role in the detection and degradation of phenolic compounds, but it is limited by its cost and stability. In this study, the laccase-like property of copper peptide (GHK-Cu) has been revealed. In terms of enzymatic reaction kinetics, GHK-Cu has a V_max of 1.735 × 10⁻⁴ mM·s⁻¹ and a K_m of 0.061 mM, demonstrating good substrate affinity and excellent catalytic efficiency. Then, a colorimetry was developed for rapid detection of epinephrine (EP) and 2-aminophenol (2-AP). The linear response range of EP is 20–240 μM, with a limit of detection (LOD) of 9.5 μM. The linear response ranges of 2-AP are 14–100 μM (in ultrapure water) and 2–120 μM (in seawater), with LODs of 2.56 μM and 1.65 μM. In addition, combined with a smartphone platform, a cotton-based sensor has been developed for the detection of 2-AP in seawater. The linear response ranges are 0–0.2 mM and 0.2–1 mM, with LOD of 0.033 mM. The structure of GHK-Cu provides a reference for the development of novel laccase mimetic enzymes. The constructed colorimetry offers an option for the rapid detection of phenolic compounds, and the developed cotton-based sensor enabled rapid and portable detection of 2-AP. Full article

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified. Full article

Magnetooptics (MO) explores light—matter interactions in magnetized media and has advanced rapidly with progress in materials science, spectroscopy, and integrated photonics. This review highlights recent developments in fundamental principles, experimental techniques, and emerging applications. We revisit the canonical MO effects: Faraday, MO Kerr effect (MOKE), Voigt, Cotton—Mouton, Zeeman, and Magnetic Circular Dichroism (MCD), which underpin technologies ranging from optical isolators and high-resolution sensors to advanced spectroscopic and imaging systems. Ultrafast spectroscopy, particularly time-resolved MOKE, enables femtosecond-scale studies of spin dynamics and nonequilibrium processes. Hybrid magnetoplasmonic platforms that couple plasmonic resonances with MO activity offer enhanced sensitivity for environmental and biomedical sensing, while all-dielectric magnetooptical metasurfaces provide low-loss, high-efficiency alternatives. Maxwell-based modeling with permittivity tensor ($\epsilon$) and machine-learning approaches are accelerating materials discovery, inverse design, and performance optimization. Benchmark sensitivities and detection limits for surface plasmon resonance, SPR and MOSPR systems are summarized to provide quantitative context. Finally, we address key challenges in material quality, thermal stability, modeling, and fabrication. Overall, magnetooptics is evolving from fundamental science into diverse and expanding technologies with applications that extend far beyond current domains. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

Fire hazards during cotton picker operations pose a significant safety concern, primarily caused by cotton blockages and friction-induced heat generation between the picking spindle and seed cotton under high-load conditions. Existing fire monitoring systems typically employ a uniform temperature threshold across multiple sensors. However, this approach overlooks the distinct characteristics of different cotton picker mechanisms and the influence of seed cotton moisture content, resulting in frequent false alarms and missed detections. To address these issues, this study pioneers and tests a dynamic, tiered temperature threshold warning strategy. This approach accounts for key cotton picker components and varying seed cotton moisture content (MC), specifically MC 9–12% and MC 12–15%. Additionally, based on the operational characteristics of the cotton conveying tube, this study proposes monitoring the wall surface temperature of the conveying tube and investigates the threshold for this temperature. Results indicate that during seed cotton open burning, the average temperature is 324 °C for MC < 9%, 261.9 °C for MC 9–12%, and 178.4 °C for MC 12–15%. After transitioning to smoldering, the temperatures were 226.6 °C, 191.5 °C, and 163.5 °C, respectively, with 163.5 °C being the lowest threshold for seed cotton open burning in the cotton bin. For smoldering seed cotton, the temperature thresholds were 240 °C for MC < 9% and MC 9–12%, and 280 °C for MC 12–15%. The temperature threshold for the cotton conveyor pipe wall surface was 49 °C. The friction-induced heat generation temperature threshold at the picking head, determined through combined testing and simulation, is set at 289 °C for MC < 9%, 306 °C for MC 9–12%, and 319 °C for MC 12–15%. The aforementioned tiered early warning strategy, developed through multi-source experiments and simulations, can be directly configured into controllers. It enables dynamic threshold alarms based on harvester location, seed cotton moisture content, and temperature zones, providing quantitative support for cotton harvester fire monitoring and risk management. Full article

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Alzheimer’s disease (AD) affects a significant portion of humanity’s elderly population across the globe. Recent studies have identified Amyloid-Beta 42 (Aβ₄₂) as a key biomarker for AD. In this research, we examined the feasibility of using string-shaped electrodes to develop a potentially wearable biosensor for the early detection of AD. Two types of flexible electrochemical electrodes were fabricated using a commercial thread (25% cotton-75% polyester) and an electrospun nanofiber-based string. Decorating the strings with either gold or SiC nanoparticles, several different electrodes were tested to explore their responses to Aβ₄₂. Our results show that the nanofiber-based electrode decorated with gold nanoparticles had the highest sensitivity of 1.71 µA/pg.cm and the best limit of detection (LoD) of 8.36 pg/mL. These findings highlight the importance of the string structure in designing highly sensitive sensors. Full article

Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R² of 0.62, and for LAI using MSR and NDVI an R² of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions. Full article

Electroconductive textiles (e-Textiles) are vital in developing wearable sensors that preserve the comfort and characteristics of textiles. Among two-dimensional (2D) transition metal dichalcogenides (TMDs), considered a promising option for sensor applications, tungsten di-selenide (WSe₂) homostructures have been used as humidity- and temperature-sensing materials for developing e-textiles, as mentioned in a first-of-its-kind report. Exfoliated chemical vapor deposition (CVD)-grown 2H-WSe₂ nanosheets were dispersed in hydroalcoholic solutions using an amino-functionalized silane to improve dispersion. Acrylic thickener was added to create 2H-WSe₂-based pastes, which were applied onto cotton using the knife-over-roll technique to obtain thin, flexible electroconductive coatings on textiles. Various characterization techniques confirmed the even distribution of 2D-WSe₂-based coatings on fabrics and the maintenance of textile comfort and wearability. The conductivity of coated fabrics was measured at room temperature and ranged between 2.9 × 10⁸ and 1.6 × 10⁹ Ω sq⁻¹. The WSe₂-based textile sensors functioned well as resistance humidity detectors within 30–90% relative humidity (RH), revealing good repeatability and sensitivity after multiple exposure cycles. To a lesser extent, WSe₂-based textile sensors act as temperature detectors within 20–60 °C with limited repeatability. The 2D-based textiles exhibited a quadratic dependence of resistance on temperature and a characteristic thermal hysteresis. This proposed strategy marks a significant milestone in developing scalable and flexible 2D TMD-based detectors with great potential for wearable sensing devices. Full article

The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R² = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R² = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R² and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture. Full article

The growing environmental impact of textile waste, fueled by the rapid rise in global fiber production, underscores the urgent need for sustainable end-of-life solutions. This review explores cutting-edge pathways for textile waste management, spotlighting innovations that reduce reliance on incineration and landfilling while driving material circularity. It highlights advancements in collection, sorting, and pretreatment technologies, as well as both established and emerging recycling methods. Smart collection systems utilizing tags and sensors show great promise in streamlining logistics by automating pick-up routes and transactions. For sorting, automated technologies like near-infrared and hyperspectral imaging lead the way in accurate and scalable fiber separation. Automated disassembly techniques are effective at removing problematic elements, though other pretreatments, such as color and finish removal, still need to be customized for specific waste streams. Mechanical fiber recycling is ideal for textiles with strong mechanical properties but has limitations, particularly with blended fabrics, and cannot be repeated endlessly. Polymer recycling—through melting or dissolving waste polymers—produces higher-quality recycled materials but comes with high energy and solvent demands. Chemical recycling, especially solvolysis and pyrolysis, excels at breaking down synthetic polymers like polyester, with the potential to yield virgin-quality monomers. Meanwhile, biological methods, though still in their infancy, show promise for recycling natural fibers like cotton and wool. When other methods are not viable, gasification can be used to convert waste into synthesis gas. The review concludes that the future of sustainable textile recycling hinges on integrating automated sorting systems and advancing solvent-based and chemical recycling technologies. These innovations, supported by eco-design principles, progressive policies, and industry collaboration, are essential to building a resilient, circular textile economy. Full article

This study investigates the unique interplay between thermal comfort and antimicrobial properties in base fabrics, shaping the foundation for the development of “Smart Socks” as advanced personal protective equipment (PPE). By delving into the inherent qualities of fibers such as cotton, polyester, bamboo, and wool and exploring fabric structures like single jersey, terry, rib, and mesh, the research captures the dynamic relationship between material composition and performance. Terry fabrics emerge as insulators, wrapping the user in warmth ideal for cold climates, while mesh structures breathe effortlessly, enhancing air circulation and moisture wicking for hot environments. Cotton mesh, with its natural affinity for moisture, showcases exceptional moisture management. Antimicrobial testing, focused on fabrics’ interactions with Staphylococcus aureus, highlights the dormant potential of bamboo’s bio-agents while revealing the necessity for advanced antimicrobial treatments. This study unveils a vision for combining innovative fabric structures and fibers to craft smart socks that balance thermal comfort, hygiene, and functionality. Future directions emphasize sensor integration for real-time physiological monitoring, opening pathways to revolutionary wearable PPE. Full article

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article

The development of the field of textronics covers many directions, but the neediest are safety, medicine, and environmental protection. The solutions developed can combine the needs of many people from different social groups and ages. This leads to sustainable socio-economic, scientific and integrated approaches to sustainable development. The authors, seeing the growing need to monitor air pollution in order to increase safety, decided to develop textronic chemical sensors based on carbon-based inks and metal thread embroidery, sensitive to harmful gases and vapors based on textiles. This was to limit the production of subsequent sensors made in plastic housings containing difficult-to-recycle materials and replace them with sensors incorporated into everyday materials such as clothing, which will inform us about emerging threats not only in the place where a large plastic sensor is placed, but in every place at home, at work and outside where we will be. The authors assume that the sensors can be incorporated into clothing, e.g. work clothes, and can also be fastened from one piece of clothing to another. This increases their economic aspect and usability on a larger scale. Three materials of different composition were tested: cotton, polyester and viscose. These materials were selected based on their properties, namely the easier determination of their ability to achieve full circularity of the final product.Functional and mechanical tests of resistance to factors occurring during everyday use were carried out for the use of systems in clothing materials and to produce roller blinds and curtains. To examine the durability of the systems, electrical conductivity was checked before and after the tests. The results showed changes in resistance values after individual tests and during contact with harmful gases. Particularly noticeable are the differences between samples with embroidery and samples with inkjet paste applied. It was shown that the selected materials are suitable for the intended application, and selected modifications together with conductive materials show proper functioning in detecting harmful gases. This project demonstrates the possibility of creating chemical sensors based on printing techniques using carbon printing pastes and embroidery with a metal thread with silver on a textile substrate. Possible applications considering health and environmental aspects are presented. Full article