Search Results (12,938)

The main goal of this study is to address the problem of environmental water pollution caused by organic dyes through waste valorization by synthesizing geopolymer-based adsorbents. In this work, geopolymers were synthesized using fly ash modified with chitosan and polyvinyl alcohol as a starting material. The obtained materials were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and determination of the point of zero charge. We examined the adsorption potential for organic dye (methylene blue, brilliant green, crystal violet) removal through the influence of contact time, initial pH and concentration of adsorbate solution, and temperature on adsorption. The obtained results were analyzed using theoretical kinetics and isotherm models. Interpretation of the obtained results was performed using the Box–Behnken design and chemometric methods of multivariate analysis. The findings showed that modification with chitosan significantly enhanced the adsorption efficiency of the synthesized materials up to 95.9% for methylene blue adsorption. The parameters identified as having the greatest influence on the adsorption process were contact time, pH-value, initial dye concentration, and the type of dye being adsorbed. Full article

To address the issues of zero-valent iron Fe(0) passivation and limited nitrogen and phosphorus removal in constructed wetlands (CWs), this study investigated the enhancement effect of two carbon materials—activated carbon (AC) obtained through high-temperature pyrolysis and biochar (BC) obtained through low-temperature pyrolysis—when coupled with Fe(0). Four systems were set up: control (CW-C), Fe(0) alone (CW-Fe), Fe(0) with AC (CW-FeAC), and Fe(0) with BC (CW-FeBC). Evaluations covered wastewater treatment performance, microbial community structure, and functional gene abundance. Results showed that iron–carbon coupling significantly improved nitrogen and phosphorus removal, with the CW-FeAC system performing best, achieving 58% total nitrogen (TN) and 90% total phosphorus (TP) removal. This enhancement was attributed to AC’s high conductivity, which strengthened iron–carbon micro-electrolysis, accelerated Fe(0) corrosion, and enabled continuous Fe²⁺/Fe³⁺ release, supplying electrons for denitrification and phosphorus precipitation. Microbial analysis indicated that iron–carbon coupling markedly reshaped community structure, enriching key genera such as Thiobacillus (33.8%) and Geobacter (12.5%) in CW-FeAC. Functional gene analysis further confirmed higher abundances of denitrification (napA/narG→nirS→nosZ) and iron metabolism genes (feoA/feoB), suggesting enhanced nitrogen-iron cycling. This study clarifies the mechanisms by which iron–carbon coupling improves nitrogen and phosphorus performance in CWs and highlights the superiority of AC over BC in facilitating electron transfer and functional microorganism enrichment, providing a basis for the design of enhanced CW systems treating low-carbon-nitrogen-ratio wastewater, such as secondary effluent or lightly polluted surface water. Full article

The detection of high-consequence viral pathogens is essential for spillover prevention and reduction in transmission but is limited by the low sensitivity of next-generation sequencing technology. Low-titer field samples from a variety of hosts are primarily composed of non-viral genomic material, reducing the probability of obtaining usable sequence data. Targeted enrichment, such as VirCapSeq-VERT, removes background genomic material to improve virus detection but is mainly used for sequencing clinical samples. We customized the VirCapSeq-VERT probe system to aid in the detection of zoonotic viruses of interest and adapted it for use on the Oxford Nanopore sequencing platform. We validated the method on a variety of samples, including a mock virome consisting of seven RNA viruses, samples from an animal laboratory study, and a set of animal field samples. We also developed Nanite, a lightweight bioinformatics pipeline, to perform bioinformatic analyses. Results indicated that the optimized enrichment protocol improved sequencing by enhancing the detection of viruses, increasing read lengths, and, in some cases, improving genomic coverage. Most importantly, the sequencing of zoonotic viruses was improved in field samples with low titers, suggesting that this protocol is a useful tool for increasing the efficacy of Oxford Nanopore sequencing for field-oriented applications. Full article

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions. Full article

Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the components buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design. Full article

This study presents a dual intelligent framework for predicting the uniaxial compressive strength (UCS) and energy transmission rate (ETR) of rubber-modified concrete, a promising aseismic material. An artificial neural network (ANN) was integrated with three advanced metaheuristic optimization algorithms, dream optimization algorithm (DOA), football optimization algorithm (FbOA), and hiking optimization algorithm (HOA), to enhance predictive accuracy. A database comprising 150 experimental results from UCS and ETR tests was used for model training and validation. Comparative evaluation revealed that the DOA-ANN model achieved the highest accuracy with a coefficient of determination (R²) of 0.9857, root mean square error (RMSE) of 0.9501, mean absolute error (MAE) of 0.5756, and variance accounted for (VAF) of 98.5716% for UCS prediction and R² of 0.9708, RMSE of 1.5334, MAE of 0.9211, and VAF of 97.5063% for ETR prediction, outperforming other optimized ANN, random forest (RF), and conventional machine learning (ML) models. Shapley additive explanations (SHAP) analysis quantified feature importance, highlighting cement and specimen mass as critical predictors, while rubber content exhibited a dual role in strength reduction and energy absorption enhancement. A visual software tool embedding the optimal DOA-ANN model was further developed to enable rapid material design and real-time prediction. This work provides an efficient and interpretable artificial intelligence (AI)-driven approach for advancing the performance evaluation and design of sustainable aseismic concrete. Full article

Hydrogel microneedles (HMNs) act as non-invasive devices that can effortlessly merge with the human body for drug delivery and diagnostic purposes. Nonetheless, their improvement is limited by intricate and repetitive issues related to material composition, structural geometry, manufacturing accuracy, and performance enhancement. At present, there are only a limited number of studies accessible since artificial intelligence and machine learning (AI/ML) for HMN are just starting to emerge and are in the initial phase. Data is distributed across separate research efforts, spanning different fields. This review aims to tackle the disjointed and narrowly concentrated aspects of current research on AI/ML applications in HMN technologies by offering a cohesive, comprehensive synthesis of interdisciplinary insights, categorized into five thematic areas: (1) material and microneedle design, (2) diagnostics and therapy, (3) drug delivery, (4) drug development, and (5) health and agricultural sensing. For each domain, we detail typical AI methods, integration approaches, proven advantages, and ongoing difficulties. We suggest a systematic five-stage developmental pathway covering material discovery, structural design, manufacturing, biomedical performance, and advanced AI integration, intended to expedite the transition of HMNs from research ideas to clinically and commercially practical systems. The findings of this review indicate that AI/ML can significantly enhance HMN development by addressing design and fabrication constraints via predictive modeling, adaptive control, and process optimization. By synchronizing these abilities with clinical and commercial translation requirements, AI/ML can act as key facilitators in converting HMNs from research ideas into scalable, practical biomedical solutions. Full article

Laser cladding offers distinct advantages over traditional manufacturing methods, including low heat input, minimal dilution ratio, dense clad layers, and robust bonding. It is widely employed for surface strengthening of metals to enhance performance and repair failed components, thereby reducing material waste. This study investigates laser cladding repair of EA4T steel, focusing on examining the effects of laser power, scanning speed, and powder feed rate on melt pool dilution ratio and shape factor during cladding of IN718 material onto EA4T steel substrate. Orthogonal experiments were conducted to investigate the combined effects of different process parameters on dilution rate and shape factor. Optimal process parameters were determined by comprehensively evaluating melt pool cross-sectional morphology and internal defects. Based on this, theoretical lap calculations were performed, and the optimal theoretical lap ratio was obtained through experiments. Experiments indicated that the influence of process parameter variations on molten pool morphology parameters is not linear; the combined effects of all factors must be comprehensively considered. Full article

Background and Objectives: The selection of the optimal distal level of fusion in long-level fusion surgery remains controversial. Fusion ending at L5 preserves motion but increases the risk of L5–S1 disc degeneration. The L5 laminar hook has been introduced to enhance distal fixation, but its indications and clinical effects remain unclear. This study aimed to investigate the indication of the L5 laminar hook and its effect on adjacent segment degeneration when performing long-level fusion terminating at L5 in patients with degenerative sagittal imbalance (DSI). Materials and Methods: 112 patients with DSI who had undergone corrective surgery and ended at L5 were analyzed retrospectively. Finally, 64 were treated with an L5 laminar hook (Group I), while 48 were not (Group II). We compared radiographic spinopelvic parameters between the two groups and followed up disc degeneration at the L5–S1 level. Additional analysis was conducted within Group I to determine the indications for L5 laminar hook. Results: Preoperative C7 sagittal vertical axis (C7SVA), lumbar lordosis (LL), and pelvic incidence (PI)-LL mismatch were significantly greater in Group I than in Group II (p < 0.05). At 2 years of follow-up, advanced L5–S1 disc degeneration had developed in 14 patients (21.9%) in Group I and 36 patients (75%) in Group II. Patients in Group B (exacerbated L5–S1) had a significantly higher body mass index (BMI), larger preoperative C7SVA, and PI-LL mismatch than patients in Group A (preserved L5–S1). The C7SVA and PI-LL mismatch cut-off values for Group A were 15.8 cm and 40.8°, respectively. Conclusions: L5 laminar hook helps to reduce disc degeneration at the L5–S1 level and can be used to maintain the deformity correction ending at L5 in patients with DSI. We propose a series of indicators for the use of L5 laminar hooks in patients with DSI: a low BMI, a PI-LL mismatch < 40.8°, and a C7SVA < 15.8 cm. Full article

Hybrid solar cells, which combine inorganic and organic materials, offer a promising pathway to achieve low cost, flexible, high-performance photovoltaic devices. This work explores the application of oxidative chemical vapor deposition (oCVD) to deposit poly(3,4-ethylenedioxythiophene) (PEDOT) as a transparent hole transfer layer in hybrid solar cells. Unlike solution-processed PEDOT with polystyrene sulfonate solubilizer (PEDOT:PSS), oCVD allows for growing high-purity PEDOT that provides conformal coverage on textured substrates, enabling enhanced antireflective effects and improved charge extraction. We discuss the advantages of oCVD PEDOT in hybrid architecture, its compatibility with textured substrates, and its potential to achieve higher efficiency. Full article

Fly ash from coal-fired power plants is a promising precursor for zeolite synthesis due to its aluminosilicate-rich composition. However, its direct utilization is often limited by impurities and a low silicon-to-aluminum ratio (SAR). This study demonstrates the conversion of Class C fly ash from the Soma thermal power plant (Turkey) into FAU- and CHA-type zeolites through optimized acid leaching and hydrothermal synthesis. Acid treatment increased the SAR from 1.33 to 2.85 and effectively reduced calcium-, sulfur-, and iron-bearing impurities. The SAR enhancement by acid leaching was found to be reproducible among Class C fly ashes, whereas Class F materials exhibited a limited response due to their acid-resistant framework. Subsequent optimization of alkaline fusion-assisted synthesis enabled selective crystallization of FAU and CHA, while GIS and MER appeared under prolonged crystallization or higher alkalinity. SEM revealed distinct morphologies, with MER forming rod-shaped clusters, and CHA exhibiting disc-like aggregates. Water sorption analysis showed superior uptake for metastable FAU (~23 wt%) and CHA (~18 wt%) compared to stable GIS and MER (~12–13 wt%). Overall, this study establishes a scalable and sustainable route for producing high-performance zeolites from industrial fly ash waste, offering significant potential for adsorption-based applications in dehumidification, heat pumps, and gas separation. Full article

Rare-earth barium copper oxide (REBCO) high-temperature superconductors, owing to their ability to maintain high critical current density (J_c) under liquid-nitrogen-temperature and high-magnetic-field conditions, are widely regarded as one of the most promising material systems among all superconductors. This review systematically summarizes fabrication strategies for REBCO coated conductors, with a focus on pulsed laser deposition (PLD) for achieving high-quality epitaxial growth with precise composition control. To enhance in-field performance, strategies for introducing artificial pinning centers (APCs) are examined, including rare-earth element doping, substrate surface decoration, and nanoscale secondary phase incorporation. The mechanisms of vortex pinning from different dimensional defects and their synergistic effects are compared. Finally, we suggest potential future directions aimed at further enhancing the superconducting properties. Full article

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique among transduction processes since it is easy to use, scalable, and compatible with deformable materials. The three main classes of resistive sensors used in soft robotic grippers are systematically examined in this review: ionic sensors, which are emerging multimodal devices that can capture both mechanical and environmental cues; tactile sensors, which detect contact, pressure distribution, and slip; and strain sensors, which monitor deformation and actuation states. Their methods of operation, material systems, fabrication techniques, performance metrics, and integration plans are all compared in the survey. The results show that sensitivity, linearity, durability, and scalability are all trade-offs across sensor categories, with ionic sensors showing promise as a new development for multipurpose soft grippers. There is also a discussion of difficulties, including hysteresis, long-term stability, and signal processing complexity. In order to move resistive sensing from lab prototypes to reliable, practical applications in domains like healthcare, food handling, and human–robot collaboration, the review concludes that developments in hybrid material systems, additive manufacturing, and AI-enhanced signal interpretation will be crucial. Full article

Poly(L-lactide) (PLA) is a promising biopolymer for biomedical applications due to its biodegradability and biocompatibility; however, its brittleness restricts its use in fused deposition modeling (FDM). To overcome this limitation, flexible PLA monofilaments with enhanced mechanical performance and printability were developed. In this study, PLA was melt-blended with acetyl triethyl citrate (ATEC, 1.0–5.0 wt%) as a plasticizer and zinc phenyl phosphonate (TMC-200, 0.3 wt%) as a nucleating agent. It was found that the PLA with 3.0 wt% ATEC (PLA/A) exhibited the greatest flexibility, while the addition of TMC-200 further improved tensile strength and ductility. Specifically, the ternary blend of PLA/TMC-200/ATEC (PLA/T/A) exhibited a synergistic effect, achieving superior mechanical properties (tensile strength: 35.0 MPa, elongation at break: 232.0%, compared to 12.1% for pure PLA) and raising the degree of crystallinity (X_c) from 4.7% to 45.0%. Monofilaments (1.70 ± 0.05 mm) fabricated from PLA/T/A exhibited smooth surfaces, balanced mechanical performance, and excellent cytocompatibility (over 99% cell viability in L929 fibroblasts). Moreover, FDM-printed specimens retained enhanced mechanical and thermal performance, demonstrating material stability after processing. Shelf-life testing further confirmed the structural integrity of PLA/T/A monofilament after 8 weeks at 50 °C. Overall, PLA/T/A provides an effective strategy for producing high-performance, medical-grade PLA monofilaments with improved toughness, printability, and biocompatibility, enabling their application in biomedical 3D printing. Full article

This research investigates a dual-polished surface plasmon resonance sensor based on dual-core photonic crystal fiber, featuring an innovative design aimed at enhancing hemoglobin concentration detection in blood, providing a valuable tool for diagnosing numerous health issues, such as chronic obstructive pulmonary disease. The sensor makes use of an external sensing mechanism and utilizes gold (Au) coating as the plasmonic material, chosen for its strong plasmonic response and excellent chemical stability, ensuring robust performance across the 1.31–1.42 refractive index range. The electromagnetic characteristics and efficacy of the designed sensor were thoroughly investigated using the finite element method. Our proposed sensor demonstrates outstanding performance metrics, attaining peak amplitude sensitivity of about 734 RIU⁻¹, and wavelength sensitivity of 74,000 nm/RIU along with 1.35 × 10⁻⁶ RIU wavelength resolution. It also exhibits a notable Figure of Merit value of 667 for a corresponding Full width at Half Maximum value of 111 nm. Finally, a machine learning model based on linear regression was employed that enables the prediction of any hemoglobin concentration levels corresponding to analyte RI values. These exceptional performance metrics highlight the potential of our sensor as a reliable, cost-effective and highly sensitive solution for real-time biosensing applications. Full article