Search Results (70)

Blood iron levels are related to many health conditions, affecting hundreds of millions of individuals worldwide. To aid in the prevention and treatment of iron-related disorders, previous research has developed a low-cost, accurate, point-of-care method for measuring iron from a single finger-prick blood sample. This study builds upon that work by introducing an improved imaging method that accurately reads sensor images irrespective of variations in environmental illumination and camera quality. Smartphone cameras were used as analytical tools, demonstrating an average coefficient of variation of 5.13% across different phone models, and absorbance results were found to be improved by 8.80% compared to the method in a previous study. The proposed method successfully enhances iron detection accuracy under diverse lighting conditions, paving the way for smartphone-based sensing of other colorimetric reactions involving various analytes. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

Achieving targeted nanoparticle (NP) size and concentration combinations in Pulsed Laser Ablation in Liquid (PLAL) remains a challenge due to the highly nonlinear relationships between laser processing parameters and NP properties. Despite the promise of PLAL as a surfactant-free, scalable synthesis method, its industrial adoption is hindered by empirical trial-and-error approaches and the lack of predictive tools. The current literature offers limited application of machine learning (ML), particularly recommender systems, in PLAL optimization and automation. This study addresses this gap by introducing a ML-based recommender system trained on a 3 × 3 design of experiments with three replicates covering variables, such as fluence (1.83–1.91 J/cm²), ablation time (5–25 min), and laser scan speed (3000–3500 mm/s), in producing magnesium nanoparticles from powders. Multiple ML models were evaluated, including K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest, and Decision trees. The DT model achieved the best performance for predicting the NP size with a mean percentage error (MPE) of 10%. The XGBoost model was optimal for predicting the NP concentration attaining a competitive MPE of 2%. KNN and Cosine similarity recommender systems were developed based on a database generated by the ML predictions. This intelligent, data-driven framework demonstrates the potential of ML-guided PLAL for scalable, precise NP fabrication in industrial applications. Full article

The rapid progress in additive manufacturing (AM) has unlocked significant possibilities for producing 316/316L stainless steel components, particularly in industries requiring high precision, enhanced mechanical properties, and intricate geometries. However, the widespread adoption of AM—specifically Directed energy deposition (DED), selective laser melting (SLM), and electron beam melting (EBM) remains challenged by inherent process-related defects such as residual stresses, porosity, anisotropy, and surface roughness. This review critically examines these AM techniques, focusing on optimizing key manufacturing parameters, mitigating defects, and implementing effective post-processing treatments. This review highlights how process parameters including laser power, energy density, scanning strategy, layer thickness, build orientation, and preheating conditions directly affect microstructural evolution, mechanical properties, and defect formation in AM-fabricated 316/316L stainless steel. Comparative analysis reveals that SLM excels in achieving refined microstructures and high precision, although it is prone to residual stress accumulation and porosity. DED, on the other hand, offers flexibility for large-scale manufacturing but struggles with surface finish and mechanical property consistency. EBM effectively reduces thermal-induced residual stresses due to its sustained high preheating temperatures (typically maintained between 700 °C and 850 °C throughout the build process) and vacuum environment, but it faces limitations related to resolution, cost-effectiveness, and material applicability. Additionally, this review aligns AM techniques with specific defect reduction strategies, emphasizing the importance of post-processing methods such as heat treatment and hot isostatic pressing (HIP). These approaches enhance structural integrity by refining microstructure, reducing residual stresses, and minimizing porosity. By providing a comprehensive framework that connects AM techniques optimization strategies, this review serves as a valuable resource for academic and industry professionals. It underscores the necessity of process standardization and real-time monitoring to improve the reliability and consistency of AM-produced 316/316L stainless steel components. A targeted approach to these challenges will be crucial in advancing AM technologies to meet the stringent performance requirements of various high-value industrial applications. Full article

The performance and scalability of energy storage systems play a key role in the transition toward intermittent renewable energy systems and the achievement of decarbonization targets through means of resilient electrical grids. Despite significant research and technology advancements, the scalability of innovative energy storage systems remains challenging due to the scarcity of raw materials (used for the production of energy storage media, cathodes, anodes, separators, conductive agents, and electrolytes). The European Commission has identified certain raw materials as both economically important and subject to supply risks, designating them as critical and strategic raw materials. In this review, a comprehensive analysis is conducted regarding 28 raw materials and rare earth elements which are essential for the production of batteries, supercapacitors, and other storage systems, emphasizing their criticality, strategic importance, supply chain vulnerabilities, and associated environmental and social impacts. This study also addresses potential substitute materials for energy storage devices and innovations that make these devices recyclable. Future trends are briefly discussed, including advancements in alternative chemistries and innovations to improve energy density in advanced batteries and supercapacitors, paving the way for hybrid energy solutions. Full article

A joint distribution of significant wave heights ($H_s$) and peak wave periods ($T_p$) in the northwestern South China Sea is created using a conditional distribution model in this work. An unstructured triangular mesh wave model covering the northwestern South China Sea is established based on the third-generation spectral wave model SWAN. This wave model has been extensively validated against field data and was run from 1979 to 2020 to generate long enough one-hourly $H_s$ and $T_p$. Four probability density functions including Normal, Lognormal, Gamma and 3P Weibull distributions are adopted to construct the marginal independent distribution of $H_s$. The results show that the 3P Weibull distribution is more suitable in fitting the marginal distribution of $H_s$ compared to the other three distributions. Three combinations of dependence functions ($\mu \mathrm{and} \sigma$), namely, power3 and exp3, insquare2 and asymdecrease3, and logistics4 and alpha3, are used to create the Normal and Lognormal distributions for $T_p$. The estimations of dependence functions and corresponding fitted $T_p$ demonstrate that the $\mu \mathrm{and} \sigma$ using power3 and exp3 perform best in producing the conditional distribution of $T_p$. In addition, the environmental contours derived by an IFORM are used to generate the extreme sea states with return periods of 1, 5, 10, 25, 50 and 100 years. Full article

The synthesis of nanoparticles (NPs) via laser ablation synthesis in solution (LASiS) is a promising method for sustainable and efficient nanoparticle fabrication. This work investigates the transition from one-factor-at-a-time experimentation to a more efficient, multivariate approach for optimising NP production efficiency. By applying the Industry 4.0 principles, the objective is to digitise and automate laboratory processes to increase productivity and robustness. Design of Experiments (DoE) strategies, Taguchi orthogonal arrays and full-factorial design (FFD), have been employed to enhance laser ablation processes. Both models confirmed that increasing laser power led to higher colloid absorbance, with the Taguchi DoE offering rapid initial process mapping and FFD providing a higher-resolution analysis. The optimal laser repetition rate of 30 kHz was identified as a balance between pulse energy and thermal effects on the target, maximising ablation efficiency. The Taguchi model had a prediction of NP size with an R² value of 0.49, while the FFD struggled with accurate size prediction. Additionally, this study introduced a recirculation flow regime as a rapid test platform for predicting optimal conditions for continuous flow production. Using a semi-autonomous DoE platform decreased the operator involvement and increased the process selectivity. This proof-of-concept for on-the-bench NP rapid manufacturing demonstrated how efficient NP synthesis processes can be developed by clarifying the effects of varying parameters on colloid productivity, paving the way for broader industrial applications in the future. Full article

Electrospun nanofibrous mats, consisting of chitosan (CS) and polyvinylpyrrolidone (PVP), were constructed with the addition of graphene oxide (GO) for enhancement of delivery of the 5-Fluorouracil (5-Fu) chemotherapy drug. Upon studying the range of GO concentrations in CS/PVP, the concentration of 0.2% w/v GO was chosen for inclusion in the drug delivery model. SEM showed bead-free, homogenous fibres within this construct. This construct also proved to be non-toxic to CaCo-2 cells over 24 and 48 h exposure. The construction of a drug delivery vehicle whereby 5-Fu was loaded with and without GO in various concentrations showed several interesting findings. The presence of CS/PVP was revealed through XPS, FTIR and Raman spectroscopies. FTIR was also imperative for the analysis of 5-Fu while Raman exclusively highlighted the presence of GO in the samples. In particular, a detailed analysis of the IR spectra recorded using two FTIR spectrometers, several options for determining the concentration of 5-Fu in composite fibre systems CS/PVP/5-Fu and GO/CS/PVP/5-Fu were demonstrated. By analysis of Raman spectra in the region of D and G bands, a linear dependence of ratios of integrated intensities of A_D and A_G on the intensity of host polymer band at 1425 cm⁻¹ vs. GO content was found. Both methods, therefore, can be used for monitoring of GO content and 5-Fu release in studied complex systems. After incorporating the chemotherapy drug 5-Fu into the constructs, cell viability studies were also performed. This study demonstrated that GO/CS/PVP/5-Fu constructs have potential in chemotherapy drug delivery systems. Full article

This study investigates the printability limitations of 3D-printed continuous 316L stainless steel fibre-reinforced polymer composites obtained using the Materials Extrusion (MEX) technique. The objective was to better understand the geometric printing limitations of composites fabricated using continuous steel fibres, based on a combination of bending stiffness testing and piezoresistive property studies. The 0.5 mm composite filaments used in this study were obtained by co-extruding polylactic acid (PLA), with a 316 L stainless steel fibre (SSF) bundle. The composite printability limitations were evaluated by the printing of a series of ’teardrop’ shaped geometries with angles in the range from 5° to 90° and radii between 2 and 20 mm. The morphology and dimensional measurements of the resulting PLA-SSF prints were evaluated using $\mu$CT scanning, optical microscopy, and calliper measurements. Sample sets were compared and statistically examined to evaluate the repeatability, turning ability, and geometrical print limitations, along with dimensional fluctuations between designed and as-printed structures. Comparisons of the curvature bending stiffness were made with the PLA-only polymer and with 3D-printed nylon-reinforced short and long carbon fibre composites. It was demonstrated that the stainless steel composites exhibited an increase in bending stiffness at smaller radii. The change in piezoresistance response of the PLA-SSF with load applied during the curvature bending stiffness testing demonstrated that the 3D-printed composites may have the potential for use as structural health monitoring sensors. Full article

This study investigates the effects of process parameters in the powder bed fusion–laser beam (PBF-LB) process on IN939 samples. The parameters examined include laser power (160, 180, and 200 W), laser scanning speed (400, 800, and 1200 mm/s), and hatch distance (50, 80, and 110 μm). The study focuses on how these parameters affect surface roughness, relative density, defect formation, and the microstructure of the samples. Surface roughness analysis revealed that the average surface roughness (Sa) values of the sample ranged from 4.6 μm to 9.5 μm, while the average height difference (Sz) varied from 78.7 μm to 176.7 μm. Furthermore, increasing the hatch distance from 50 μm to 110 μm while maintaining constant laser power and scanning speed led to a decrease in surface roughness. Relative density analysis indicated that the highest relative density was 99.35%, and the lowest was 93.56%. Additionally, the average porosity values were calculated, with the lowest being 0.06% and the highest reaching 9.18%. Although some samples had identical average porosity values, they differed in porosity/mm² and average Feret size. Variations in relative density and average porosity were noted in samples with the same volumetric energy density (VED) due to different process parameters. High VED led to large, irregular pores in several samples. Microcracks, less than 50 μm in length, were present, indicating solidification cracks. The microstructural analysis of the XZ planes revealed arc-shaped melt pools, columnar elongated grains aligned with the build direction, and cellular structures with columnar dendrites. This study provides insights for optimizing PBF-LB process parameters to enhance the quality of IN939 components. Full article

Floating structures are subject to complex marine conditions. To ensure their safety, reliability analysis needs to be conducted during the design phase. However, because of the complexity of traditional full long-term analysis, the environmental contour method (ECM) based on the inverse reliability method, which can combine accuracy and efficiency, is extensively used. Due to the unique environment in the South China Sea, the probabilistic characteristics of three-dimensional (3D) environmental parameters of wind, wave and current are investigated. The ECs of the target sea are established via the ECM based on both the inverse first-order reliability method (IFORM) and inverse second-order reliability method (ISORM). It is found that the sea state forecasted by ISORM is more extreme and may lead to a more conservative design than IFORM. Furthermore, the wind–wave–current combination coefficient matrixes developed using the 3D ECs are proposed for the design of FOWTs in the South China Sea. The validity and practicality of the contours and matrixes are tested by using a floating offshore wind turbine (FOWT) as a numerical example. Then, the short-term response of the structure under the combined wind, wave and current conditions is calculated, providing a theoretical reference for the design of FOWTs. Full article

Is additive manufacturing (AM) a sustainable process? Can the process be optimised to produce sustainable AM parts and production techniques? Additive manufacturing offers the production of parts made of different types of materials in addition to the complex geometry that is difficult or impossible to produce by using the traditional subtractive methods. This study is focused on the optimisation of laser powder bed fusion (L-PBF), one of the most common technologies used in additive manufacturing and 3D printing. This research was carried out by modulating the build layer thickness of the deposited metal powder and the input volumetric energy density. The aim of the proposed strategy is to save the build time by maximizing the applied layer thickness of nitinol powder while retrieving the different AM part properties. The saving in the process time has a direct effect on the total cost of the produced part as a result of several components like electric energy, inert gas consumption, and labour. Nickel-rich nitinol (52.39 Ni at.%) was selected for investigation in this study due to its extremely high superplastic and shape memory properties in addition to the wide application in various industries like aerospace, biomedical, and automotive. The results obtained show that significant energy and material consumption can be found by producing near full dens AM parts with limited or no alteration in chemical and mechanical properties. Full article

The IEC 61400-1 standard specifies design load cases (DLCs) to be considered in the design of wind turbine structures. Specifically, DLC 2.3 considers the occurrence of a gust while the turbine shuts down due to an electrical fault. Originally, this load case used a deterministic wind event called the extreme operating gust (EOG), but the standard now also includes an approach for calculating the extreme response based on stochastic simulations with turbulent wind. This study presents and compares existing approaches with novel probabilistic design approaches for DLC 2.3 based on simulations with turbulent wind. First, a semiprobabilistic approach is proposed, where the inverse first-order reliability method (iFORM) is used for the extrapolation of the response for electrical faults occurring at a given rate. Next, three probabilistic approaches are formulated for the calculation of the reliability index, which differs in how the aggregation is performed over wind conditions and whether faults are modeled using a Poisson distribution or just by the rate. An example illustrates the methods considering the tower fore-aft bending moment at the tower base and shows that the approach based on iFORM can lead to reductions in material usage compared to the existing methods. For reliability assessment, the probabilistic approach using the Poisson process is needed for high failure rates, and the reliabilities obtained for designs using all semiprobabilistic methods are above the target level, indicating that further reductions may be obtained via the use of probabilistic design methods. Full article

This work uses the environmental contour line approach to estimate the long-term extremes of carbon emission generated by a bulk carrier operating in different sea state conditions, utilising short-term analyses of the ship propulsion energy efficiency as a function of hull resistance in calm water due to appendages, aerodynamic resistance, and added wave resistance, resulting in the required permanent delivered power and the one induced by the waves. The analysis accounts for the ship’s main characteristics, operational profile based on mission conditions, and wave climatic data. All sources of inherent uncertainties are accounted for through the variability in the 3 h extreme value in any sea state in the long term, and the inverse first-order reliability method (IFORM) is employed in predicting the extreme operational carbon intensity indicator (CII). This study develops proper wave scatter diagrams as a function of the route description. The CII measures the energy efficiency of the installed propulsion system, accounting for the ship’s operational characteristics, such as the annual fuel consumption with corresponding CO₂ factors, annual distance travelled, and capacity. The present study is limited to one operation route but can be extended to any other possible voyage or sea area. The estimated CII defined from the complete probabilistic characterisation of the sea state conditions conditional to the short-term maximum response is a rational approach that can be used for optimising the ship’s main characteristics, propulsion system, operational profile, and chosen route to achieve the best ship performance and energy efficiency. Full article

This study investigates the feasibility of 3D printing continuous stainless steel fibre-reinforced polymer composites. The printing study was carried out using 316L stainless steel fibre (SSF) bundles with an approximate diameter of 0.15 mm. This bundle was composed of 90 fibres with a 14 μm diameter. This fibre bundle was first coated with polylactic acid (PLA) in order to produce a polymer-coated continuous stainless steel filament, with diameters tailored in the range from 0.5 to 0.9 mm. These filaments were then used to print composite parts using the material extrusion (MEX) technique. The SSF’s volume fraction ($V_f$) was controlled in the printed composite structures in the range from 4 to 30 $V_f$%. This was facilitated by incorporating a novel polymer pressure vent into the printer nozzle, which allowed the removal of excess polymer. This thus enabled the control of the metal fibre content within the printed composites as the print layer height was varied in the range from 0.22 to 0.48 mm. It was demonstrated that a lower layer height yielded a more homogeneous distribution of steel fibres within the PLA polymer matrix. The PLA-SSF composites were assessed to evaluate their mechanical performance, volume fraction, morphology and porosity. Composite porosities in the range of 2–21% were obtained. Mechanical testing demonstrated that the stainless steel composites exhibited a twofold increase in interlaminar shear strength (ILSS) and a fourfold increase in its tensile strength compared with the PLA-only polymer prints. When comparing the 4 and 30 $V_f$% composites, the latter exhibited a significant increase in both the tensile strength and modulus. The ILSS values obtained for the steel composites were up to 28.5 MPa, which is significantly higher than the approximately 13.8 MPa reported for glass fibre-reinforced PLA composites. Full article