Time-temperature Profiles Research Articles

Impact of harvest maturity and storage conditions on tomato quality: A comprehensive experimental and modeling study

The study was applied to fresh tomatoes (Solanum lycopersicum, Marbone variety). First, a field survey was conducted to obtain statistical data on the temperature and duration (minimum, mode, maximum) at different stages in the supply chain. Then, the physicochemical, phytochemical and volatile compounds were measured at harvest and on five different days (3, 7, 11, 14 and 21 days) for two maturity stages at harvest (green-orange and light red) and under three storage temperatures (3 °C, 8 °C and 15 °C). These data were used to evaluate the combined impact of harvest maturity, time and storage temperature on the evolution of the different compounds. From the same data, quality models were developed based on the Arrhenius equation to describe the tomato quality evolution under different storage temperatures for (Z)-3 hexenal, total sum of acids, lycopene and chlorogenic acid. These compounds were chosen because of their relation to commercial and nutritional qualities. The model results were validated using additional data from a different study conducted on one constant temperature profile and two dynamic time-temperature profiles as in a supply chain. Finally, data from the field survey and the quality models were combined in a Monte Carlo process (105 product items) to simulate the variability of time-temperature profiles from the grower to the sales outlet and its impact on the quality attributes. For two maturity stages at harvest, the impact of the supply chain on the quality attributes of tomatoes at the sales outlet was also analyzed. Simulation results show that the maturity stage at harvest significantly impacts the final lycopene content and the total sum of acids in tomatoes at the sales outlet, with fruit harvested at the light red stage containing higher lycopene levels (and a lower hue angle) and a lower total sum of acids in comparison with fruit harvested at the green-orange stage. However, at the sales outlet, the concentrations of chlorogenic acid and (Z)-3-hexenal in fruit harvested at the green-orange and light red stages are similar. This information would be useful for fruit and vegetable stakeholders for time-temperature management of tomato fruit throughout the supply chain in order to optimally preserve the main quality attributes.

The effect of non-uniform skelp temperature on X70 steel coil cooling

This study explored the effect of varying steel skelp temperature (500 °C–600 °C) on the subsequent temperature-time ( T- t) profile during and after coiling of low C (0.05 wt. %) X70 microalloyed steels. Three industrially produced steel coils with similar rough rolling temperatures (∼1050 °C) and finish rolling temperatures (∼850 °C) but different coiling temperatures (500 °C–600 °C) and different skelp thicknesses (19 mm and 25 mm) were studied. The surface temperature of each coil was measured using an infrared video camera. A three-dimensional heat transfer model was developed to predict the T- t profiles. A lower (∼60 °C) initial skelp temperature near the head and tail of the skelp led to a reduced coil temperature. A higher (∼50 °C) temperature near the head and tail region of the skelp resulted in a more uniform coil temperature (a difference of ∼10 °C instead of ∼50 °C).

Comparative fitting methodology of cure kinetics models based on differential scanning calorimetry

The optimization of composite cure cycles is critical in materials engineering, requiring a deep understanding of the material’s cure kinetics. This study employs Differential Scanning Calorimetry (DSC) coupled with advanced fitting algorithms to determine the most accurate cure kinetics models for optimizing time-temperature profiles. Given the wide variability in model selection and fitting approaches in existing literature, our research systematically compares several cure kinetics models and their fitting methodologies to identify the most effective strategies. We highlight the advantages and limitations of each approach, with a particular focus on the robustness of the Levenberg–Marquardt (LM) algorithm compared to the Trust-Region-Reflective (TRR) algorithm. Given the existence of multiple local minima in the objective function, a global approach (genetic algorithm) is employed. Our findings reveal that, although the TRR and LM algorithms are faster, they are less accurate than the genetic algorithm. Moreover, the Kamal-Sourour model shows the highest prediction accuracy among the tested models. This comparative analysis provides a systematic approach and crucial insights into selecting the optimal cure kinetic model and corresponding parameters, based on experimental data, allowing for enhancing the efficiency and accuracy of composite curing processes.

Considerations for the safe handling and processing of unstable materials

AbstractWe evaluate the instability hazards of initiators, monomers, and other unstable materials during storage and handling in individual containers, pallets of containers, and storage and process vessels. We propose a simplistic model to analyze the temperature–time profile of an unstable chemical, given heat transfer from/to the environment, heat generation due to decomposition and other reaction kinetics, and heat generated from auxiliary equipment (e.g., agitators/mixers, pumps, vessel jackets). Lumped capacitance‐based heat transfer and decomposition kinetics are integrated to predict self‐accelerating decomposition temperature (SADT), self‐accelerating polymerization temperature (SAPT), and the temperature profile given exposure conditions (e.g., ambient temperature and additional sources of heat). Case studies related to the storage and processing of unstable liquid monomers (i.e., methyl methacrylate, MMA), and organic peroxides, (i.e., tert‐butyl peroxybenzoate, TBPB), are presented. We also provide recommendations on determining safe storage and processing temperatures and steps to prevent emergency situations and include an overview of thermal analysis and data treatment procedures which might affect chemical process safety recommendations.

Investigating the Effects of Induction Heating on a Submerged Hollow Cylindrical Element

ABSTRACTThis article proposes an induction heating element that addresses heat transfer and thermal homogenization while being fully submerged in a liquid, specifically focusing on heating of water as a case study. The design calculations, fabrication, and realization of a resonant induction heating system that heats the proposed heating element are presented. The geometric shape of proposed heating element is a hollow cylinder that maintains structural strength while delivering required thermal energy because of an external time‐varying magnetic field. Detailed mathematical modeling of the proposed heating element's equivalent electric circuit has been presented while considering physical parameters including diameter, thickness, height, material properties, magnetic field frequency, and skin effect. A laboratory scale prototype of the induction heating system including the proposed heating element has been built, and experiments have been demonstrated. The experimental results of the working induction heating resonant inverter and RLC resonant circuit inductance and capacitance with induction load are presented. Additionally, the results of thermographic images and temperature–time profile of the water sample, which was heated using the proposed heating element, are provided to demonstrate the heating element's reliability and practicality. This research contributes to the field of induction heating technology by investigating a heating element that is submerged in a liquid medium, resulting in a uniform temperature distribution throughout the liquid. The conventional method of heating water in a stainless steel cup‐shaped container is an inefficient heat transfer process. In stainless steel cup‐shaped containers, a portion of the energy input used to boil the water is lost to the environment through the surface area of the container that is not in direct contact with the water. This heat loss occurs due to the container's design, which exposes a large surface area to the surrounding environment, allowing thermal energy to dissipate through conduction, convection, and radiation.

UNMASKING THE POWER OF METAL OXIDE NANOPARTICLES FOR SELF-CLEANING HEAVY DENIM: INVESTIGATING AND OPTIMIZING PROCESS PARAMETERS

The textile industry is consistently integrating cutting-edge technologies, introducing materials with multifaceted features, such as odour resistance, hydrophobicity, durability, and self-cleaning capabilities. This transformation is facilitated by the application of nanotechnology. The focal point of this experimental endeavour lies in closing the gap in employing ZnO nanoparticles on high GSM denim fabric, enhancing self-cleaning, UV protection, and antimicrobial capabilities. Initially, a solution containing nanoparticles, binder, softener, ethanol, and their auxiliaries was formulated. Zinc oxide nanoparticles, along with their auxiliaries, were applied to the denim using the pad-dry-cure and pad-dry-steam methods. Various trials were conducted to optimize the concentration of ZnO NPs, with the formulation containing 5% ZnO NPs, 5% binder, and 25% ethanol. Experimentation ensued to establish optimal time–temperature profiles for drying, curing, and steaming. Comprehensive evaluations, including drop tests, stain tests, colour strength assessments, crocking tests, SEM analysis, antimicrobial testing and UV protection factor determination were conducted on the samples. The study revealed that fixation of the self-cleaning finish by curing gives better results than by steaming. SEM examination highlighted the distribution of ZnO nanoparticles on the denim fabric. Lastly, antimicrobial properties were assessed, concluding that treated fabrics exhibited antibacterial activity compared to untreated fabrics.

Modelling of Joule-Thomson cooling effect using a modified shift-DeepONet method for predicting hydrate onset during CO2 sequestration

Carbon Storage in underground depleted oil and gas reservoirs play a key role in reducing anthropogenic Carbon Dioxide (CO2) emissions around the world. Injection of CO2 at high pressure into a low pressure depleted gas formations containing methane (CH4) leads to Joule-Thompson (J-T) cooling, possibly resulting in injection blocking precipitation of CO2 or CH4 hydrates. Here, we present a deep neural operator-based approach to model the J-T effects during CO2 injection in depleted reservoirs to demonstrate the effectiveness of Deep Operator Networks (DeepONets) in modelling piece-wise functions with sharp changes in gradient for cases of varying permeabilities. We propose a modified version of the shift-DeepONet model using non-linear constructors in the neural network architecture to accurately model temperature and pressure profiles in space and time. The modified shift-DeepONet model achieves a higher accuracy in modelling temperature for permeability in the range of 1.01mD to 101mD with three to up to ten times improvement in reducing Relative L2 (RL2) norm and with a testing RL2 norm of 3.8 × 10−4 compared to 2.97 × 10−3 for the Vanilla DeepONet. Using the temperature and pressure profiles, estimates are made for hydrate onset in space and time. The proposed modified shift-DeepONet method achieves high accuracy in hydrate classification with precision values > 0.99, recall values > 0.86 and F1 scores >0.92 in classification of CO2 or CH4 hydrate onset during CO2 injection into depleted gas reservoirs.

Analysis of thermal cycles during DED-Arc of high-strength low-alloy steel and microstructural evolution

Additive manufacturing (AM), specifically Directed Energy Deposition (DED)-Arc, holds great potential for the fabrication of individually designed components. In the construction industry, the demand for tailored steel nodes is increasing, particularly for large-scale structures, where high-strength steel deployment can significantly reduce material consumption and production time. However, the thermal material deposition process has a strong impact on the technical properties of high-strength steel. Parameters like heat input, interpass temperature (IPT), and cooling strategies modulate cooling times and peak temperatures in thermal cycles, which are crucial for estimating mechanical properties indispensable for safety certification. The development of digital twins or digital shadows emerges as the primary approach for quality assurance and certification, deploying real-time monitoring of manufacturing parameters such as voltage, current, wire feed, thermal history, and bead shapes. This data can be correlated with specific regions in the fabricated part to draw quality inferences. To interpret this data effectively, an understanding of the influence of these process parameters on resultant properties is indispensable. Given that the resulting microstructure strongly depends on the thermal history in AM parts, temperature monitoring during DED-Arc using thermography emerges as a promising method. This article aims at providing fundamental knowledge about phase transformations of a high strength steel feedstock under repeated reheating and gaining comprehensive knowledge about microstructural evolution during DED-Arc Additive Manufacturing. This is done by the analysis of time-temperature profiles, resulting microstructure and mechanical properties, linking it to CCT diagrams. A novel approach for describing the degree of tempering in different regions of the samples by deploying tempering parameters for anisothermal heat treatments is presented. By using this approach, an estimation about the resulting microstructure and hence the mechanical properties can be drawn.

Bifonazole: a missing case of polymorphism

AbstractBifonazole, an imidazole-based antifungal drug, can be easily amorphized and crystallizes upon reheating at moderate scanning rate. Driven by recent results in literature reporting for this compound the appearance of polymorphism under different thermal histories, the present study presents an extensive calorimetric characterization of bifonazole in its crystalline and amorphous state. A rich scenario in crystallization behaviour has been found, implying that the behaviour observed on reheating scans can strongly depend on the previous thermal history and on the distribution in number and size of crystalline regions. In particular, the decrease in melting enthalpy during reheating, related to the increase of the heating rate, can be stated to be the result of a partial crystallization of the sample. Additionally, the temperature intervals more favourable to crystal nucleation and growth, respectively, have been determined, thanks to the choice of ad hoc time–temperature profiles. Finally, also the nucleation occurrence in time at high temperature has been investigated using a suitable DSC protocol. The overall results show how complex can be the cold-crystallization behaviour of a molecular liquid.

Inverse design of the radiation temperature for indirect laser-driven equation-of-state measurement

The theoretical design for the time profile of radiation temperature plays an important role in indirect laser-driven equation-of-state measurement, which severely relies on a large number of radiation hydrodynamic simulations. In this work, we provide a concise data-driven method for optimizing the radiation temperature profile, which combines a time-varying Volterra model with an improvement achieved by data generation via radiation hydrodynamic simulations utilizing random perturbations in a skew normal distribution as inputs. We find that the time-varying Volterra model can be used to investigate the time-dependent relationship between the radiation temperature and the key physical quantities of interest, such as shock-wave velocity and ablation drive pressure. With this method, we realize the inverse designs of the radiation temperature profiles for planar dynamic shock and ramp compressions according to the desired shock-wave velocity and drive pressure, respectively, which shows the advantage of practical application in experiments.

Processing-microstructure relationships in ferrous alloys via mixed powder laser powder bed fusion

Three different approaches to control the phase formation in powder bed fusion - laser beam (PBF-LB) printing using a powder mixture of duplex stainless steel (DSS) 2507 and austenitic stainless steel 316 L were examined: (i) in situ solid state reheating, (ii) utilization of an island scan strategy, and (iii) switching process parameters between layers of a build. Analytical modeling was used to determine the time-temperature profiles during each of these processes, and was combined with thermodynamic simulations through Thermo-Calc to predict and explain the phase formation measured experimentally. It was found that the reheating from additional layers was critical to the nucleation of austenite, even with the increased Ni content in powder mixtures. This was evident in the island scan strategy prints where the corners of the islands showed significantly less austenite (∼45%) as compared to the centers of the islands (∼96%), as well as in the multi-layer samples where alternating laser power for fixed chemical composition of powder mixture resulted in varying content from ∼75% (lower power) to ∼90% (higher power). Additionally, in situ reheating using a lower power laser was capable of minimally increasing the austenite content layer-by-layer, by raising the temperature of the layer above the nucleation temperature of austenite. These results showcase the capability of PBF-LB to manipulate phase structure in ways not possible through traditional manufacturing techniques, as well as the ability of Thermo-Calc to accurately predict the trends observed that can be applied to other alloy systems.

Iron oxide nanoparticle synthesis: Simulation-based comparison of laboratory- and pilot plant-scale spray-flame synthesis

This study analyzes burner scale-dependent effects for iron-oxide nanoparticles synthesis in spray flames through a combined experimental and numerical approach. A laboratory- and a pilot plant-scale synthesis facility generate iron oxide nanoparticles from iron nitrate dissolved in ethanol/2-ethylhexanoic acid solvents on the 0.5 and 15 g/h scale, respectively. Phase Doppler measurements supply initial conditions for spray development in the numerical approach, while in situ OH* chemiluminescence and multi-line nitric oxide laser-induced fluorescence (NO-LIF) thermometry provide experimental data for comparison with simulation results of the pilot plant burner. Ex situ particle sizing by gas adsorption according to Brunauer-Emmet-Teller (BET) and transmission electron microscopy (TEM), along with phase composition determination via X-ray diffraction (XRD), are used to compare products of both burners and the corresponding simulations. The numerical approach employs a Reynolds-averaged Eulerian–Lagrangian description of the flow in combination with a flamelet/progress variable (FPV) combustion and monodisperse particle model to reflect spray-flame synthesis. Despite similar chemiluminescence between experiment and simulation, more significant discrepancies are observed in NO-LIF thermometry and particle sizing. Nanoparticle formation and growth at both burner scales is investigated using the numerical method. Special attention is directed to the high-temperature particle residence time (HTPRT). Notably, the average temperature–time profiles particles experience are almost identical in the burners, although their geometric scales and designs differ substantially. Simulation results show that while the primary particle diameter remains mostly consistent, the pilot-scale burner produces larger particle agglomerates than the laboratory burner. The difference is attributed to an increased particle number concentration during the initial formation of soft agglomerates. The findings demonstrate that, despite retaining a similar HTPRT, the overall flow conditions and liquid spray dispersion, which impact the distribution of the particle number concentration, influence the final agglomerate size.

Porosity prediction in laser-based powder bed fusion of polyamide 12 using infrared thermography and machine learning

Achieving precise control in laser-based powder bed fusion of polymers is crucial for ensuring the structural integrity of aerospace and automotive components. Closed-loop feedback control systems using process monitoring techniques, such as infrared thermography, have the potential to provide reliable production by controlling the temperature of the melt. However, challenges arise from complex interactions among variables, such as part geometry, scan strategy, and laser parameters, affecting the melted polymer's temperatures and subsequent particle fusion. Thus, the correlation between thermal signals and resulting part density still needs to be clarified. In this work, a machine learning algorithm is trained to predict local porosity or, rather, solidity based on thermal and temporal features extracted from the melt's temperature-time profile. This enables statistical techniques to assess the contribution of the melt's thermal and temporal features in the decision-making process for evaluating porosity, along with the influence of voxel size and configuration. The in-situ process signature is measured using infrared thermography, and the porosity is analyzed by X-ray micro-computed tomography. The 2D thermal data is first converted into voxel information and then stitched with the 3D mircoCT data in a second step. The resulting 3D thermal features and porosity matrices are downsampled and utilized to train a machine learning algorithm (lightGBM). Models with high prediction accuracy are achieved using a small voxel size to avoid over-homogenizing features and by utilizing thermal signals from adjacent voxels to determine porosity in a volume element. The highest predictor for resulting porosity is the peak temperature of the melt during laser exposure. Interlayer effects, such as sufficient reheating of subsurface layers, are the second-highest indicator for dense parts. Furthermore, the model's performance is also affected by intra-layer effects, including the peak temperature of adjacent voxels and the cooling behavior after laser exposure. This research has several implications for industry, as it enables the detection of process defects based on in-situ process monitoring data without post-process material testing. Moreover, identifying thermal signal ranges that lead to the highest porosity can reduce the number of experiments needed for material qualification processes.

Numerical simulation of temperature profile during laser cladding of nickel powder for in situ repairing

A 3-D Finite Element (FE) model was established to simulate the laser cladding of nickel powder for in situ repairing AISI 410 stainless steel. The temperature distribution field was estimated at six different points using low laser power of 450 W and a scanning speed of 2 mm/s. This study used a moving laser heat source in COMSOL to simulate the laser cladding process. The proposed FE model investigated the temperature field in two cases: (1) in situ repairing the defect by laser cladding without preheating and (2) in situ repairing the defect by laser cladding technique after preheating the cavity made on the substrate. A time-temperature profile was obtained after the simulation in both cases. The results of both cases were compared and analyzed. When the cladding material is poured into the preheated cavity, its temperature rises due to the heating effect obtained from the cavity, resulting in a higher maximum temperature after the cladding process. It was observed that after the cladding process, the maximum temperature was higher in the case of laser cladding after preheating the substrate (2400.49 K) compared to the maximum temperature in cladding without preheating the substrate (1770 K). Therefore, a molten pool is formed, solidifying rapidly to form a clad coating with better metallurgical bonding and enhanced surface properties.

Enhancement of strength and ductile-brittle transition temperature of SA508 Gr.3 low-alloy steel by controlling heat accumulation in laser powder-directed energy deposition

The safety and longevity of small modular reactors are affected by reactor pressure vessels, which are complex integral components made of SA508 Gr.3 low-alloy steel. In this study, the impacts of heat accumulation on the microstructural and mechanical characteristics (tensile properties and Charpy V-notched impact energy) of SA508 Gr.3 steel fabricated using laser powder-directed energy deposition were investigated. Rectangular samples were prepared using long raster and short raster scanning strategies for changing heat buildup, and the mechanical tests were conducted depending on build direction. The time-temperature profile measured at a fixed point in the long raster sample showed a lower maximum temperature and a higher cooling rate, indicating lower heat accumulation compared to that of the short raster sample. In each build direction, the yield strength of the long raster sample was 45.8 %–60.5 % higher and its ductile-brittle transition temperature was 76.8–103.8 °C lower than that of the short raster sample. Additionally, compared to conventionally made samples and without requiring heat treatment, the long raster sample exhibited over a 45 % increase in yield strength and a 22.7 °C reduction in the ductile-brittle transition temperature. The superior combination in long raster samples is induced by smaller effective grain size, smaller cementite, and a higher pre-existing dislocation density. The results emphasize the importance of controlling heat accumulation throughout the additive manufacturing process and provide valuable insights into the use of additive manufacturing for manufacturing reactor pressure vessels in the nuclear industry.

Investigation on the residual mechanical properties of glulam after exposure to elevated temperature

For structures that experience a fire event without collapse, determination of the residual capacity is required. The objective of this research is to determine the residual properties of glued laminated (glulam) timber after exposure to elevated temperature to enable post-fire structural evaluation. The residual compressive and shear properties of glulam were experimentally measured after exposure to a range of elevated temperatures (50, 100, 150, 200 and 250 ℃), considering also the effect of heating duration (30, 60 and 90 min) and cooling method (air cooling and water cooling). To account for transient temperature gradients, an effective temperature of the specimen was defined based on the dynamic time-temperature profile. For parallel-to-grain compressive strength, the reduction in residual strength remains within 20% of the initial strength as long as the effective temperature has not exceeded 170 ℃, but it drops severely to exceed 50% at 210 ℃. For shear strength, the permanent reduction is also within 20% after exposure to effective temperature lower than 190 ℃ and air cooling. Water-cooling leads to greater permanent reduction of mechanical properties compared with air-cooling. After first temperature exposure, additional cycles of heating-cooling (by air) do not further degrade the properties. Based on the test results, predictive formulae are proposed for the residual mechanical properties of glulam.

Crystalline Microstructure, Microsegregations, and Mechanical Properties of Inconel 718 Alloy Samples Processed in Electromagnetic Levitation Facility

The solidification of Inconel 718 alloy (IN718) from undercooled liquid is studied. The solidification kinetics is evaluated in melted and undercooled droplets processed using the electromagnetic levitation (EML) technique by the temperature–time profiles and solid/liquid (S/L) interface movement during recalescence. The kinetics is monitored in real time by special pyrometrical measurements and high-speed digital camera. It is shown that the growth velocity of γ-phase (the primary phase in IN718), the final crystalline microstructure (dendritic and grained), and the mechanical properties (microhardness) are strongly dependent on the initial undercooling ΔT at which the samples started to solidify with the originating γ-phase. Particularly, with the increase in undercooling, the secondary dendrite arm spacing decreases from 28 μm to 5 μm. At small and intermediate ranges of undercooling, the solidified droplets have a dendritic crystalline microstructure. At higher undercooling values reached in the experiment, ΔT>160 K (namely, for samples solidified with ΔT=170 K and ΔT=263 K), fine crystalline grains are observed instead of the dendritic structure of solidified drops. Such change in the crystalline morphology is qualitatively consistent with the behavior of crystal growth kinetics which exhibits the change from the power law to linear law at ΔT≈160 K in the velocity–undercooling relationship (measured by the advancement of the recalescence front in solidifying droplets). Study of the local mechanical properties shows that the microhardness increases with the increase in the γ″-phase within interdendritic spacing. The obtained data are the basis for testing the theoretical and computational of multicomponent alloy samples.

Modeling and experimental validation of the time-temperature profile, pectin methylesterase inactivation, and ascorbic acid degradation during the continuous flow microwave-assisted pasteurization of orange juice

This work evaluated the effect of microwave-assisted pasteurization on pectin methylesterase (PME) and ascorbic acid (AA) in orange juice (OJ) and proposed a model to predict the time-temperature profile of the process and consequent effect on inactivation and quality. Fresh OJ was pasteurized in a pilot-scale unit at four processing temperatures (70, 80, 90 and 100) °C, five holding times, using two heating systems (conventional and microwave). The experimental overall heat transfer coefficient was determined from the heat loads and correlated with Reynolds and Prandtl numbers. The best-adjusted correlation was used to predict the time-temperature profile considering a unidimensional mathematical model for flow and heat transfer. Predicted and experimental temperatures showed differences below 0.9 °C for all conditions. The model was able to predict PME inactivation and AA retention, indicating that microwave pasteurization of OJ can be as efficient as conventional heating on PME inactivation with less impact on AA.

High entropy alloy cladding on mild steel substrate using microwave energy at 2.45 GHz: An insight from multi-physics simulation study to experimental investigations

Microwave hybrid heating (MHH) has emerged as a novel technology for cladding high-entropy alloy (HEA) powders on metallic substrates. The existence of a multi-material system (Al, Co, Cr, Ni, and Ti powder) in HEA provides difficulties in microwave cladding. These problems are addressed in the current study by employing silicon carbide as a susceptor material for MHH to microwave-clad AlCoCrNiTi HEA on a mild steel substrate. A multi-physics model of the microwave cladding setup was constructed to investigate the cladding properties. The model was verified using experimental data, and simulation was used to examine the electric field, resistive losses, energy distributions, and temperature-time profile. The simulation results indicated that the electric field intensity, resistive losses, and energy distribution were at their maximum near the susceptor and the cladded surface. As a result, the HEA-cladded surface reached a temperature of 1030 °C at the central plane. This is comparable to the experimental temperature of 1041 °C in 41 min, which shows an error of <10 %. From the microstructural investigation, the HEA powder exhibited a flaky microstructure and a uniform distribution of all the constituents, resulting in a solid metallurgical link between the HEA powder and substrate. The surface hardness of mild steel components was enhanced intensely by three times with the inclusion of AlCoCrNiTi HEA cladding.

МЕХАНІЗМИ МАСОПЕРЕНОСУ ІНДІЮ В Cd(Zn)Te ПРИ ДІЇ НАНОСЕКУНДНИХ ЛАЗЕРНИХ ІМПУЛЬСІВ

The processes of heating, melting and ablation during nanosecond laser irradiation of metal film (In) / CdTe structures and CdZnTe solid solutions are considered. Time and coordinate (in depth) temperature profiles during nanosecond laser irradiation of the system the film In/CdTe were calculated. Melting thresholds of indium and CdTe in the In/CdTe film system have been established. The profile of the distribution of indium atoms in cadmium telluride after a single laser irradiation of the In/CdTe structure, accompanied by the formation of an inversion layer (n-type), was obtained and theoretically described; a peak at a depth of 6 nm was detected. The mass transfer coefficients of indium in CdTe in different regions were determined during nanosecond laser irradiation of the In/CdTe structure with a thickness of the In film of 30 nm on the metal side at Epad = 100 mJ/cm2. It was established that the mass transfer coefficient of In atoms in CdTe during nanosecond laser irradiation of the In/CdTe film structure depends on the distance from the CdTe surface and increases, which is associated with a rapid change over time in the inhomogeneous deformation of the crystal lattice in the process of indium diffusion. The obtained value of the coefficient of laser-induced mass transfer of In in CdTe by an order of magnitude is much higher than the diffusion coefficients of impurities under normal annealing conditions with the diffusion method of impurity introduction and is commensurate with the self-diffusion coefficient in a number of liquid metals.