Conveyance tube manufacturing is an energy-intensive process which promotes rapid surface oxidation of curved surfaces. Previous works used computational, experimental, and theoretical techniques to assess oxidation of curved surfaces. Fast, flexible computational predictions of oxide thickness can provide the continuous data necessary to generate a spatiotemporal stress profile for application to the high-level Advanced Oxide Scale Failure Diagram (AOSFD) developed in this work. To demonstrate the application of the AOSFD to conveyance tube normalisation, the oxide states after induction vs. gas-barrel heating were compared. Induction heating technology is an example of a current technological development in high-temperature steel processing which offers improved operational control and suitability for decarbonised steel processing. Current frequency control during induction heating decreases the resultant strain magnitude in the oxide thereby eliminating the compressive failure modes observed during gas-barrel heating. Higher-than-typical temperatures result in an increased tensile strain component, increasing interfacial failure probability during induction heating. However, the advantages of induction heating technology, in terms of oxide failure management, can only be achieved with sufficient electromagnetic design controls. This conclusion agrees with purely temperature-based studies of oxide failure, but the AOSFD approach accommodates the mechanical and kinetic phenomena of the oxide by combining diffusion and fracture mechanics analyses into a single diagram which is quick and simple to apply to industrial contexts which demand oxidation control on curved surfaces.

In this study, viscosity of Al–Si alloys was measured as a function of melt superheat and shear rate using rotational rheometer. Al-7 wt pct Si, Al-20 wt pct Si as well as Al-7 wt pct Si-2 wt pct Fe alloys were used to conduct viscosity measurements with the same superheat and shear stress applied. The viscosity results then were fitted upon the Arrhenius model. The viscosity values obtained in this study with rotational rheometer were extremely high (for pure Al, viscosity values varied between 1.4 and 0.3 Pa s) and exhibited a shear thinning behaviour. All alloy samples that were tested in this study exhibited a shear thinning behaviour, which means that viscosity reduces when the shear rate increases and reaches a steady state above a certain shear limit.

Gold nanoparticles (GNPs) of different sizes are used in various biomedical applications. We explored the interaction between differently sized (5-60 nm) citrate-coated GNPs and bovine serum albumin (BSA). Using techniques such as dynamic light scattering, zeta potential, circular dichroism (CD) spectroscopy, time-of-flight secondary ion mass spectrometry (ToF-SIMS), and synchrotron X-ray absorption spectroscopy (XAS), we demonstrate that BSA significantly enhances the colloidal stability of GNPs by preventing aggregation. Additionally, GNPs did not induce unfolding or loss of secondary structure in BSA, as confirmed by CD and ToF-SIMS, suggesting that BSA interacts with GNPs without disrupting its native conformation. Unlike previous studies on the interaction between GNPs and L-cysteine, ToF-SIMS revealed no preferential binding of gold to any specific functional groups in BSA. XAS suggested that BSA adsorption on the GNPs changed their electronic structure (replenishing electrons in the Au 5d orbital). Collectively, our results support the conclusion that BSA adsorbs onto citrate-coated GNPs through non-covalent, long-range interactions that preserve protein structure and enhance colloidal stability.

Conveyance tube manufacturing via a hot-finished, welded route is an energy-intensive process that promotes the rapid surface oxidation of curved surfaces. Previous studies have used computational and theoretical techniques to assess the oxidation of curved surfaces. However, experimental techniques for assessing the oxidation of curved surfaces, as well as for validating existing computational and analytical studies, have significant limitations that impact their ability to accurately recreate industrial processes. The challenges of thermogravimetric analysis (TGA) for in situ tests for the oxidation of cylindrical geometries were investigated, using an as-welded conveyance tube, and compared to an equivalent tube normalised in industry as well as computational predictions for the same geometry and thermal conditions. A core element of this work was the use of a refractory dummy sample to quantify thermal buoyancy and flow-induced vibration. There was a strong agreement between the oxide mass gain predicted by a computational model compared to that of the TGA sample, with only a 5% discrepancy. However, oxide thickness gain, measured using electron microscopy, showed poor agreement, particularly when comparing industrial and experimental results. This was attributed to the need for further work to account for transient heating, oxide porosity, atmospheric composition variation, and the effect of thermomechanical operations during conveyance tube manufacturing, e.g., hydraulic descaling.

Remote laser welding (RLW) technology has become a prominent joining technology in automotive industries, offering high production throughput and cost-effectiveness. Recent advancements in RLW processes such as beam oscillation have led to an increased number of input process parameters, enabling precise control over the heat input to weld metallic materials. A critical necessity in laser welding entails selecting robust process parameters that satisfy all weld quality indicators or key performance indicators (KPIs) during two stages: production stage (often implemented as robotic welding); and repair/rework stage (implemented as cobotic/manual welding to identify process parameters for weld defects) as addressing these factors in both stages is necessary to satisfy near-zero-defect strategy for some e-mobility products.. This research presents a comprehensive methodology that encompasses the following key elements: (i) the development of physics-based simulations to establish the correlation between KPIs and process parameters; (ii) the integration of a sequential modelling approach that strikes a balance between accuracy and computation time to survey the parameter space; and (iii) development of the process capability space for the quick selection of robust process parameters. Three physical phenomena are considered in the development of numerical models, which are (i) heat transfer, (ii) fluid flow and (iii) material diffusion to investigate the effect of process parameters on the weld thermal cycle, solidification parameters and solute intermixing layer during laser welding of dissimilar highstrength aluminium alloys. The governing physical phenomena are decoupled sequentially, and KPIs are estimated based on the governing phenomena. At each step, the process capability space is defined over the parameters space based on the constraints specific to the current physical phenomena. The process capability space is determined by the constraints based on the KPIs. The process capability space provides the initial combination of process parameter space during the early design stage, which satisfies all the KPIs, thus decreasing the number of experiments. The proposed methodology provides a unique capability to (i) simulate the effect of process variation as generated by the manufacturing process, (ii) model quality requirements with multiple and coupled quality requirements, and (iii) optimise process parameters under competing quality requirements.

Conveyance tube manufacturing via a hot-finished, welded route is an energy-intensive process which promotes rapid surface oxidation. During normalisation at approximately 950 °C to homogenise the post-weld microstructure, an oxide mill scale layer grows on tube outer surfaces. Following further thermomechanical processing, there is significant yield loss of up to 3% of total feedstock due to scale products, and surface degradation due to inconsistent scale delamination. Delaminated scale is also liable to contaminate and damage plant tooling. The computational thermochemistry software, Thermo-Calc 2023b, with its diffusion module, DICTRA, was explored for its potential to investigate oxidation kinetics on curved geometries representative of those in conveyance tube applications. A suitable model was developed using the Stefan problem, bespoke thermochemical databases, and a numerical solution to the diffusion equation. Oxide thickness predictions for representative curved surfaces revealed the significance of the radial term in the diffusion equation for tubes of less than a 200 mm inner radius. This critical value places the conveyance tubes’ dimensions well within the range where the effects of a cylindrical coordinate system on oxidation, owing to continuous surface area changes and superimposed diffusion pathways, cannot be neglected if oxidation on curved surfaces is to be fully understood.

The effective diffusion of hydrogen in a cylindrical geometry, mimicking a pipe with a coating defect, was investigated by modelling and verified experimentally. Analytical solutions for the extreme cases of no hydrogen uptake and constant hydrogen uptake were developed. Simulations showed less than 5% error, when compared to two well documented reference cases for diffusion. The intermediate case of modelling kinetic controlled hydrogen uptake was investigated and the effects of time, position, effective diffusion coefficient, corrosion protection current density and their binary interactions, were reported. Modelling results show, that certain parameter combinations such as effective hydrogen diffusion coefficients between 10-11 10-9.5 m2 s-1, particularly in combination with a hydrogen uptake jH of more than 0.1 A m-2, may lead to locally increased hydrogen concentrations in the material.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

The blast furnace is an important part of the steelmaking process, and its main function is to melt and reduce oxygen from the iron ore for subsequent processing into the steel-ironmaking process. Due to its complexity, Blast Furnaces need to operate near their practical limits because of economic and environmental constraints. The capacity to monitor and regulate the process's thermal condition is, however, constrained by the harsh operating conditions inside the furnace. The amount of silicon present in pig iron, which is the metallic iron generated by the blast furnace process, serves as a crucial indicator of the furnace's thermal condition. Therefore, the creation of a predictive model is essential to assist in proactive control of the furnace's thermal condition because measurements of this crucial variable can only be sampled at sporadic and irregular intervals and analysis of the sample introduces a substantial delay. In this paper, an improved hybrid modelling methodology is introduced for blast furnace operation, which integrates physical and data-driven models. Deep Learning based Autoencoders are used for the prediction of the changes in silicon concentration with respect to time and that helps users to avoid running frequent and costly feature pre-processing procedures and correlation studies. Integrating the physical model improved the prediction accuracy compared to a purely data-driven model.

The corrosion behaviour of coated stainless steel as bipolar plate material in PEM fuel cell applications and its improvement through the surface modifications was investigated. A commercial SS316L stainless steel grade was deposited with thin multi-layer coatings, consisting of a carbon top layer and a chromium interlayer of two different thicknesses. Interfacial contact resistance measurements revealed that the applied Cr/C coatings are highly conductive and surpass the ICR criteria, suggested by the U.S. Department of Energy. Corrosion resistance was thoroughly analysed by potentiodynamic polarisation and cyclic voltammetry in 0.5 M H_2SO_4 at room temperature and 80 °C, respectively, combined with numerical modelling. Electrochemical results agree well with numerical modelling, including the dissolution of metallic species, local pH-shifts and changes of electrolyte conductivity. Furthermore, the study shows that the application of a Cr/C coating significantly reduces the current density in the passive region during potentiodynamic polarisation and lowering the corrosion rate of the steel substrate by at least a factor of two.

This study investigated the effect of beam oscillation on the solidification behaviour during laser welding of Al-5754 to Al-6061 alloy. In this study, a finite element model has been developed to simulate temperature and fluid flow fields by implementing different combinations of volumetric heat source models. Solidification parameters such as temperature gradient (G), solidification rate (R), cooling rate (G × R) and G/R are evaluated to understand the mechanism of microstructure formation. It was found that beam oscillation improves the tensile strength by 21.4% for full penetration welding due to an increase in the percentage of formation of equiaxed grains. Modelling results revealed that the cooling rate increases with an increase in oscillation frequency. However, tensile strength followed a parabolic distribution with a peak at the oscillation frequency of 300 Hz.

Uncertainty quantification (UQ) is deemed critical in steel reheating simulations due to the significant input uncertainties arising when defining steel surface properties and atmospheric furnace conditions. In order to conduct UQ, the study utilizes polynomial chaos expansion, which has been found to significantly curtail the computational effort needed to obtain reliable convergent statistics for the model of interest. Results from a comprehensive UQ analysis of a walking‐beam reheat furnace simulated using Tata Steel's reheat furnace control model, online slab temperature calculation, are presented. Slab temperature evolution and oxide scale growth are chosen as the study's QoIs. The analysis reveals that at the earlier stages of reheating, the majority of the output variance in slab temperature can be traced back exclusively to the emissivity of the slab surface, and the majority of the output variance in oxide scale growth is traced back to the combination of slab's surface emissivity and the initial scale thickness found on steel products prior to reheating. However, as the steel product advances toward the furnace's discharge end, inputs related to oxide scale growth become increasingly important, ultimately becoming the most influential input parameters, although the dynamics of this transition differ between the QoIs.

Biocompatible gold nanoparticles (AuNPs) are used in wound healing due to their radical scavenging activity. They shorten wound healing time by, for example, improving re-epithelialization and promoting the formation of new connective tissue. Another approach that promotes wound healing through cell proliferation while inhibiting bacterial growth is an acidic microenvironment, which can be achieved with acid-forming buffers. Accordingly, a combination of these two approaches appears promising and is the focus of the present study. Here, 18 nm and 56 nm gold NP (Au) were prepared with Turkevich reduction synthesis using design-of-experiments methodology, and the influence of pH and ionic strength on their behaviour was investigated. The citrate buffer had a pronounced effect on the stability of AuNPs due to the more complex intermolecular interactions, which was also confirmed by the changes in optical properties. In contrast, AuNPs dispersed in lactate and phosphate buffer were stable at therapeutically relevant ionic strength, regardless of their size. Simulation of the local pH distribution near the particle surface also showed a steep pH gradient for particles smaller than 100 nm. This suggests that the healing potential is further enhanced by a more acidic environment at the particle surface, making this strategy a promising approach.

Industrial scale trials to develop new steel grades are energy intensive as well as expensive, therefore it is more efficient to do laboratory scale casting trials of different steel grades in order to evaluate the casting microstructure of a new steel grade. A conventional square ingot, has advantages and disadvantages. On one hand, it has a well- known geometry and the size of the mould can easily be adjusted. On the other hand, the cooling rate and thus heat extraction is not comparable to that during continuous casting of slabs. Therefore, features related to the solidification, such as grain size and segregation are not comparable to a continuously cast product. A wedge mould set-up was developed in-house to study the effect of cooling rate on the as-cast solidification structure and micro segregation at laboratory scale. Different cooling rates were achieved by making five steps of different thicknesses, leading to microstructures, comparable with the continuously cast product. To validate the results of the wedge mould ingots, slab samples with comparable chemistry as the ingots have been taken from a conventional slab caster and a thin slab caster. The as-cast microstructure of the ingots and slab samples were compared through the primary and secondary dendrites arm spacing measured along the thickness of all samples. Cooling rates for each step of the wedge mould samples were calculated based on the dendrite arm spacing and compared with the cooling rates based on the thermocouple measurements. The microstructure and cooling rates at two steps of the wedge mould correlate well with the thin and conventional slab samples, making these steps particularly useful for assessment of castability of new steel grades.

An analytical solution for the effect of particle size on the current density and near-surface ion distribution around spherical nanoparticles is presented in this work. With the long-term aim to support predictions on corrosion reactions in the human body, the spherical diffusion equation was solved for a set of differential equations and algebraic relations for pure unbuffered and carbonate buffered solutions. It was shown that current densities increase significantly with a decrease in particle size, suggesting this will lead to an increased dissolution rate. Near-surface ion distributions show the formation of a steep pH-gradient near the nanoparticle surface ( < 6 mu m) which is further enhanced in the presence of a carbonate buffer (< 2 mu m). Results suggest that nanoparticles in pure electrolytes not only dissolve faster than bigger particles but that local pH-gradients may influence interactions with the biological environment, which should be considered in future studies. (C) 2022 The Author(s). Published by Elsevier Ltd.

The electrochemical reduction of CO2 has the potential to become a key technology in the transformation to a sustainable circular carbon economy. Formic acid could be an important platform chemical for the eco-friendly chemical industries of tomorrow. However, in most cases the reduction of CO2 is performed in highly alkaline electrolyte systems yielding formates instead of formic acid. Furthermore, existing cell configurations do not allow an operation at acidic conditions below the pK(a) of formic acid over a long period of time. Both challenges make such a process unprofitable since they are associated with high costs for downstream processing. The main challenge, however, is the so-called "carbonate problem", which is the non-faradaic formation of bicarbonate and carbonate from CO2 and OH- that cuts the carbon selectivity to faradaic products to half, even at 100 % faradaic efficiency. In the present work we evaluate three different cell configurations, using gas diffusion electrodes (GDEs) with a tin-oxide catalyst, at an industrially relevant current density of 200 mA cm(-2). The quantification of outlet CO2 allows us to compare carbon selectivities, while the variation of current densities supported by numerical simulation results give insights into the local pH value inside the GDE. We demonstrate that an electrolyzer equipped with a single-layer GDE, a liquid electrolyte, and a zero-gap anode can achieve and sustain low pH values, especially below the pKa of formic acid. Altogether this paves the way for an industrial production of formic acid.

In the present study, a finite element based numerical model is developed to evaluate heat transfer and fluid flow during the laser welding process with a moving heat source. The developed model solves the fully coupled equations of incompressible fluid flow and heat transfer. In this study, laser beam welding involving non-oscillating to oscillating beam is compared with both conditions under similar heat input per unit length i.e., same power and welding speed. Dimensionless coefficients for mass and heat transport were used to analyse the effects of Marangoni flow and thermal buoyancy. Varying combinations of radius and frequency of oscillations are studied at a constant circumferential velocity.

Remote Laser Welding (RLW) of Aluminium alloys has significant importance in lightweight manufacturing to decrease the weight of the body in white. It is critical to understand the physical process of transport phenomena during welding which is highly related to the mechanical performance of the joints. To investigate the underlying physics during welding and to understand the influence of beam oscillation on heat transfer, fluid flow and material mixing a transient three-dimensional Finite Element (FE) based Multiphysics model has been developed and validated from the experiments. The effect of welding speed, oscillation amplitude and oscillation frequency on the fusion zone dimensions, flow profile, vorticity profile, cooling rate and thermal gradient during the butt welding of Al-5754 to Al-6005, with sinusoidal beam oscillation, is analysed. It was found that one additional vortex is formed during beam oscillation welding due to the churning action of the oscillating beam. With the increase in oscillation amplitude, welds become wider and the depth of penetration decreases. An increase in oscillation frequency leads to an increase in the flow rate of the molten metal suggesting that the beam oscillation introduces a churning action that leads to an increase in mixing. It was highlighted that the material mixing depends on both diffusion and convection.

We summarize the results of a computational study involved with uncertainty quantification (UQ) in a benchmark turbulent burner flame simulation. UQ analysis of this simulation enables one to analyze the convergence performance of one of the most widely used uncertainty propagation techniques, polynomial chaos expansion (PCE) at varying levels of system smoothness. This is possible because in the burner flame simulations, the smoothness of the time-dependent temperature, which is the study's quantity of interest (QoI), is found to evolve with the flame development state. This analysis is deemed important as it is known that PCE cannot construct an accurate data-fitted surrogate model for nonsmooth QoIs, and thus, estimate statistically convergent QoIs of a model subject to uncertainties. While this restriction is known and gets accounted for, there is no understanding whether there is a quantifiable scaling relationship between the PCE's convergence metrics and the level of QoI's smoothness. It is found that the level of QoI's smoothness can be quantified by its standard deviation allowing to observe its effect on the PCE's convergence performance. It is found that for our flow scenario, there exists a power-law relationship between a comparative parameter, defined to measure the PCE's convergence performance relative to Monte Carlo sampling, and the QoI's standard deviation, which allows us to make a more weighted decision on the choice of the uncertainty propagation technique.

Gold nanoparticles are interesting for nanobiomedical applications, such as for drug delivery and as diagnostic imaging contrast agents. However, their stability and reactivity in-vivo are influenced by their surface properties and size. Here, we investigate the electrochemical oxidation of differently sized citrate-coated gold nanoparticles in the presence and absence of L-cysteine, a thiol-containing amino acid with high binding affinity to gold. We found that smaller sized (5, 10 nm) gold nanoparticles were significantly more susceptible to electrochemical L-cysteine interactions and/or L-cysteine-facilitated gold oxidation than larger (20, 50 nm) sized gold nanoparticles, both for the same mass and nominal surface area, under the conditions investigated (pH 7.4, room temperature, stagnant solutions, and scan rates of 0.5 to 450 mV s −1 ). The electrochemical measurements of drop-casted gold nanoparticle suspensions on paraffin-impregnated graphite electrodes were susceptible to the quality of the electrode. Increased cycling resulted in irreversible oxidation and detachment/oxidation of gold into solution. Our results suggest that L-cysteine-gold interactions are stronger for smaller nanoparticles.

This research aims to explore the impact of welding process parameters and beam oscillation on weld thermal cycle during laser welding. A three-dimensional heat transfer model is developed to simulate the welding process, based on finite element method. The results obtained from the model pertaining to thermal cycle and weld morphology are in good agreement with experimental results found in the literature. The developed heat transfer model can quantify the effect of welding process parameters (i.e. heat source power, welding speed, radius of oscillation, and frequecy of oscillation) on the intermediate performance indicators (IPIs) (i.e. peak temperature, heat-affected zone (HAZ) volume, and cooling rate). Parametric contour maps for peak temperature, HAZ volume, and cooling rate are developed for the estimation of the process capability space. An integrated approach for rapid process assessment, and process capability space refinement, based on IPIs is proposed. The process capability space will guide the identification of the initial welding process parameters window and helps in reducing the number of experiments required by refining the process parameters based on the interactions with the IPIs. Among the IPIs, the peak temperature indicates the mode of welding while the HAZ volume and cooling rate represent weld quality. The regression relationship between the welding process parameters and the IPIs is established for quick estimation of IPIs to replace time-consuming numerical simulations. The application of beam oscillation widens the process capability space, making the process parameter selection more flexible due to the increase in distance from the tolerance boundaries.