Search Results (14,532)

One of the main challenges in hydroxyapatite research is to develop cost-effective synthesis methods that consistently produce materials closely resembling natural bone, while maintaining high biocompatibility, phase purity, and mechanical stability for biomedical applications. Traditional synthetic techniques frequently fail to provide desirable mechanical characteristics and antibacterial activity, necessitating the development of novel strategies based on natural precursors and selective ion doping. The present study aims to explore the possibility of synthesizing hydroxyapatite through the co-precipitation method, followed by a microwave-assisted hydrothermal maturation process. The main CaO sources selected for this study are eggshells and mussel shells. Cu²⁺ and Sr²⁺ ions were added into the hydroxyapatite structure at concentrations of 1% and 5% to investigate their potential for biomedical applications. Furthermore, the morpho-structural and biological properties have been investigated. Results demonstrated the success of hydroxyapatite synthesis and ion incorporation into its chemical structure. Moreover, HAp samples exhibited significant antimicrobial properties, especially the samples doped with 5% Cu and Sr. Additionally, all samples presented good biological activity on MC3T3-E1 osteoblast cells, demonstrating good cellular viability of all samples. Therefore, by correlating the results, it could be concluded that the undoped and doped hydroxyapatite samples are suitable biomaterials to be further applied in orthopedic applications. Full article

The literature clearly indicates that both academia and industry are strongly committed to developing comprehensive processes for spent Li-ion battery (LIB) recycling. In this regard, the current study presents an original contribution by providing a quantitative assessment of a large-scale recycling plant designed for the treatment of completely spent LIBs. In addition to a concept of the basic process, this assessment also considers a case study of a thermal integration and CO₂ capture subsystem. Process flow modeling software was used to evaluate the contribution of all process steps and equipment to overall energy consumption and to mass balance the data required for the technical assessment of the large-scale recycling plant. To underline the advantages and identify the optimal novel process concept, several key performance indicators were determined, such as recovery efficiency, specific energy/material consumption, and specific CO₂ emissions. In addition, the economic potential of the recycling plants was evaluated for the defined case studies based on capital and O&M costs. The results indicate that, even with CO₂ capture applied, the thermally integrated process with the combustion of hydrogen produced in the recycling plant remains the most promising large-scale configuration for spent LIB recycling. Full article

Background and Objectives: Periodontitis is a chronic inflammatory disease that leads to progressive destruction of periodontal tissues and remains a significant global health burden. While conventional therapies such as scaling and root planning offer short-term improvements, they often fall short in maintaining long-term microbial control, underscoring the need for adjunctive strategies. This study evaluated the clinical and microbiological effects of a novel essential oil (EO)-based gel—SmartGel OV—formulated with Origanum vulgare. Materials and Methods: Thirty adults with periodontitis were enrolled in a 4-month observational study, during which SmartGel OV was applied daily via gingival massage. Clinical outcomes and bacterial profiles were assessed through probing measurements and real-time PCR analysis. Additionally, a pilot AI-based tool was explored as a supplemental method to monitor inflammation progression through intraoral images. Results: Significant reductions were observed in Fusobacterium nucleatum and Capnocytophaga spp., accompanied by improvements in clinical markers, including probing depth, bleeding on probing, and plaque index. The AI framework successfully identified visual inflammation changes and supported early detection of non-responsiveness. Conclusions: SmartGel OV demonstrates promise as a natural adjunctive treatment for periodontitis and AI monitoring was included as an exploratory secondary tool to assess feasibility for future remote tracking. Full article

In this study, a series of copolymers was synthesized using the promising biodegradable polymers Poly(L-lactic acid) (PLLA), Poly(lactic-co-glycolic acid) (PLGA), and Poly(ethylene adipate) (PEAd), known for their high potential. PEAd was synthesized through a two-step melt polycondensation process and then used to prepare copolymers with PLLA (PLLA-co-PEAd) and PLGA (PLGA-co-PEAd) at weight ratios of 90/10 and 75/25, respectively. The synthesized materials, along with the starting polymers, were extensively characterized for their structure, molecular weight, crystallinity, and thermal behavior. These novel systems exhibit single thermal transitions, e.g., glass transition. The incorporation of PEAd into the copolymers induced a plasticizing effect, evidenced by a consistent decrease in the glass transition temperature. Due to the latter effect in combination with the M_w drop, the facilitation of crystal nucleation was observed. Finally, the results by dielectric spectroscopy on the local and segmental molecular mobility provided additional proof for the homogeneity of the systems, as manifested, e.g., by the recording of single segmental relaxation processes. Overall, the findings indicate that the PLLA-co-PEAd and PLGA-co-PEAd copolymers hold significant potential, and the use of complementary experimental techniques offers valuable insights and indirect indications of their properties. Full article

This study focuses on producing and processing highly aligned tapes from recycled carbon fibers (rCFs). The rCFs are processed with a modified carding machine, oriented through a specialized subsequent process and consolidated into a semi-finished product. These rCF-tapes are placed onto a two-dimensional tool using an adapted automated fiber placement (AFP) technology to demonstrate a novel approach of producing composites from highly oriented recycled materials. The semi-finished stacks are consolidated in a heating press and test coupons are tested according to corresponding standards. The rCF-tapes are evaluated using methods such as tensile and flexural testing and determination of fiber volume content. Mechanical values are assessed by processing various generations of rCF-tapes and comparing them to each other and to virgin fiber tapes (vCF-tapes) made of the same type of carbon fiber and matrix. Microscopic imaging is also performed to analyze the quality of the resulting composites. In this study, a tensile strength of up to 1100 MPa in the fiber direction and stiffness of up to 80 GPa at a fiber volume content (FVC) of approximately 40% were achieved. The results highlight the strong potential and benefits of using highly oriented rCF-tapes and demonstrate the suitability of fiber placement technologies for those recycled materials. Full article

In the renewable energy conversion system, water electrolysis technology is widely regarded as the core means to achieve clean hydrogen production. However, the anodic oxygen evolution reaction (OER) has become a key bottleneck limiting the overall water splitting efficiency due to its slow kinetic process and high overpotential. This study proposes a novel Co₃O₄-TiO₂/CNTs p-n heterojunction catalyst, which was synthesized by hydrothermal method and significantly improved OER activity by combining heterojunction interface regulation and light field enhancement mechanism. Under illumination conditions, the catalyst achieved an overpotential of 390 mV at a current density of 10 mA cm⁻², which is superior to the performance of the dark state (410 mV) and single component Co₃O₄-TiO₂ catalysts. The material characterization results indicate that the p-n heterojunction structure effectively promotes the separation and migration of photogenerated carriers and enhances the visible light absorption capability. This work expands the design ideas of energy catalytic materials by constructing a collaborative electric light dual field regulation system, providing a new strategy for developing efficient and low-energy water splitting electrocatalysts, which is expected to play an important role in the future clean energy production and storage field. Full article

This study introduces the Real-To-Fictional Ontology (RTFO), a structured framework designed to analyze the dynamic relationship between reality and fiction in literary works, with a focus on preparatory materials and their influence on narrative construction. While traditional Italian philology and genetic criticism have distinct theoretical and editorial approaches to avant-text, this ontology addresses their limitations by integrating fine-grained textual analysis with contextual biographical avant-text to enhance character interpretation. Modeled in OWL2, RTFO harmonizes established frameworks such as LRMoo and CIDOC-CRM, enabling systematic representation of narrative elements. The ontology is applied to the case study of Cesare Pavese’s La luna e i falò, with a particular focus on the biographical avant-text of Pinolo Scaglione, the real-life friend who inspired key aspects of the novel. The fragmented and unstable nature of avant-text is addressed through a factoid-based model, which captures character-related traits, states and events as interconnected entities. SWRL rules are employed to infer implicit connections, such as direct influences between real-life contexts and fictional constructs. Application of the ontology to case studies demonstrates its effectiveness in tracing the evolution of characters from preparatory drafts to final texts, revealing how biographical and contextual factors shape narrative choices. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

This study focused on the poly(DL-lactide-co-glycolide)-block-poly(ethylene glycol)-block-poly(DL-lactide-co-glycolide) (PLGA-PEG-PLGA) triblock copolymer, which was recently reported as a novel material for polymeric nanoparticles to replace poly(DL-lactide-co-glycolide) (PLGA) as a drug carrier for prednisolone (PSL), and aimed to evaluate the efficacy of PSL-loaded PLGA-PEG-PLGA nanoparticles (NPs) against allergic contact dermatitis (ACD). PSL-loaded PLGA-PEG-PLGA NPs were prepared using the nanoprecipitation method, and their particle size distribution and mean particle size were measured using dynamic light scattering. 1-Fluoro-2,4-dinitrobenzene (DNFB) was used to create a mouse model of contact hypersensitivity (CHS). PSL-loaded PLGA-PEG-PLGA NPs were administered before sensitization with DNFB, and the therapeutic effect was evaluated by quantifying intracutaneous TNF-α and IL-4 levels suing ELISA. When PSL-loaded PLGA-PEG-PLGA NPs were administered before sensitization, TNF-α expression and IL-4 statements were significantly lower in the PSL-loaded PLGA-PEG-PLGA NP group than in the non-treated group. No significant difference was observed between the PSL-loaded PLGA-PEG-PLGA NP and PSL-loaded ointment groups, even though the steroid dose was 40 times lower than in the PSL-containing ointment. These results suggest that PSL-loaded PLGA-PEG-PLGA NPs may have a better effect in the treatment of ACD than PSL-loaded PLGA NPs. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Nanotechnology offers innovative methodologies for enhancing the diagnosis and treatment of ovarian cancer by utilizing specialized nanoparticles. The utilization of nanoparticles offers distinct advantages, specifically that these entities enhance the bioavailability of therapeutic agents and facilitate the targeted delivery of pharmacological agents to neoplastic cells. A diverse array of nanoparticles, including but not limited to liposomes, dendrimers, and gold nanoparticles, function as proficient carriers for drug delivery. Nevertheless, notwithstanding the auspicious potential of these applications, challenges pertaining to toxicity, biocompatibility, and the necessity for comprehensive clinical evaluations pose considerable barriers to the widespread implementation of these technologies. The incorporation of nanotechnology into clinical practice holds the promise of significantly transforming the management of ovarian cancer, offering novel diagnostic tools and therapeutic strategies that enhance patient outcomes and prognoses. In summary, the deployment of nanotechnology in the context of ovarian cancer epitomizes a revolutionary paradigm in medical science, amalgamating sophisticated materials and methodologies to enhance both diagnostic and therapeutic outcomes. Continued research and development endeavors are essential to fully realize the extensive potential of these innovative solutions and address the existing challenges associated with their application in clinical settings. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

The traction behavior in cryogenic solid-lubricated ball bearings (CSLBBs) used in liquid rocket engines (LREs) affects not only the dynamic response of the bearing but also the lubricity and wear characteristics of the solid lubrication coating. The traction coefficient between the ball and raceway depends on factors such as contact material, relative sliding velocity, and contact pressure. However, existing traction curve models for CSLBBs typically consider only one or two of these factors, limiting the accuracy and applicability of theoretical predictions. In this study, a novel traction model for CSLBBs is proposed, which incorporates the combined effects of contact material, relative sliding velocity, and contact pressure. Based on this model, a tribo-dynamic framework is developed to investigate the tribological and dynamic behavior of CSLBBs. The model is validated through both theoretical analysis and experimental data. Results show that the inclusion of solid lubricant effects significantly alters the relative sliding and frictional forces between the rolling elements and the raceway. These changes in turn influence the impact dynamics between the rolling elements and the cage, leading to notable variations in the bearing’s vibrational response. The findings may offer valuable insights for the wear resistance and vibration reduction design of CSLBBs. Full article

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Full article

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes several feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article