Recent advances in the kinetics of normal/abnormal grain growth: a review

Najafkhani, Fateme; Kheiri, Sara; Pourbahari, B.; Mirzadeh, Hamed

doi:10.1007/s43452-021-00185-8

Cited by 80 publications

(26 citation statements)

References 129 publications

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“…Consequently, the degree of distribution of low-angle grain boundaries slightly decreased at 300 • C. Then, at 400 and 500 • C, the samples gained higher angle grain boundaries (such as 60°) (Figure S5). At high annealing temperatures (400 and 500 • C), the main grain size distribution trend is bimodal, indicating abnormal grain growth (Figure 5c) [50,51]. The kinetics of abnormal growth is based on the recrystallization and dynamic recovery from the plastic deformation that occurs at elevated temperatures [52,53].…”

Section: Resultsmentioning

confidence: 99%

Microstructure Evolution in Plastic Deformed Bismuth Telluride for the Enhancement of Thermoelectric Properties

et al. 2022

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Thermoelectric generators are solid-state energy-converting devices that are promising alternative energy sources. However, during the fabrication of these devices, many waste scraps that are not eco-friendly and with high material cost are produced. In this work, a simple powder processing technology is applied to prepare n-type Bi2Te3 pellets by cold pressing (high pressure at room temperature) and annealing the treatment with a canning package to recycle waste scraps. High-pressure cold pressing causes the plastic deformation of densely packed pellets. Then, the thermoelectric properties of pellets are improved through high-temperature annealing (500 ∘C) without phase separation. This enhancement occurs because tellurium cannot escape from the canning package. In addition, high-temperature annealing induces rapid grain growth and rearrangement, resulting in a porous structure. Electrical conductivity is increased by abnormal grain growth, whereas thermal conductivity is decreased by the porous structure with phonon scattering. Owing to the low thermal conductivity and satisfactory electrical conductivity, the highest ZT value (i.e., 1.0) is obtained by the samples annealed at 500 ∘C. Hence, the proposed method is suitable for a cost-effective and environmentally friendly way.

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Section: Resultsmentioning

confidence: 99%

Microstructure Evolution in Plastic Deformed Bismuth Telluride for the Enhancement of Thermoelectric Properties

et al. 2022

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“…However, it is a common case that inhibitory proteins increase after shell damage (Yi et al, 2017;Yang et al, 2020). Shells are known to be unable grow infinitely and contain amazing regular structures (Kei and Takenori, 2015), which are limited to geometric energy balance and SMPs (Chen et al, 2019;Zhang et al, 2019;Najafkhani et al, 2021). Grain growth is always constrained by grain-boundary dynamics affected by SMPs (Polowczyk et al, 2016;Yi et al, 2017;Zhang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Insights from tyrosinase into the impacts of modified morphology of calcium carbonate on the nacre formation of pearl oysters

Xiong

Cao

et al. 2022

Front. Mar. Sci.

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Tyrosinase is a type-3 copper protein with six conserved histidine residues within the copper-binding sites. It participates in mollusk nacre formation. Here, we identified nacreous-layer-specific tyrosinases (NLSTyr) from Pinctada fucata martensii (PmTyr-4 and PmTyr-6), as well as their homologs in Pinctada maxima (PmaxTyr and PmaxTyr4) and Pinctada margaritifera (PmarTyr and PmarTyr-4), which encoded tyrosinases without the six conserved histidine residues within the copper-binding sites. PmTyr-4 and PmTyr-6 mRNAs were spatially concentrated in the mantle central and pearl sac, which are the organs responsible for nacre formation. During shell regeneration and pearl formation, PmTyr-4 and PmTyr-6 were also significantly highly expressed in the mantle and pearl sac. RNA interference showed that PmTyr-4 participated in nacreous-layer formation. The recombinant protein of PmTyr-4 (rPmTyr-4) inhibited the calcium carbonate precipitation rate. Correspondingly, calcium carbonate crystallization assay showed that the aragonite crystals of the rPmTyr-4 group were smaller than those of the control group. Moreover, the calcite and aragonite morphologies of the rPmTyr-4 group were modified compared with the control group. These results suggested that NLSTyr in pearl oyster inhibited calcium carbonate precipitation and affected crystal morphologies during nacre formation. Our findings provided new insights into the evolution and function gain of tyrosinase in Mollusk.

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“…Grain growth is of paramount importance owing to its impact on material properties [44,45]. Consequently, it has been studied substantially from atomistic to continuum scales [45][46][47][48]. At the mesoscale, several models have been proposed to simulate this phenomenon by using the PF method.…”

Section: Modelmentioning

confidence: 99%

Accelerating phase-field-based simulation via machine learning

Peivaste¹,

Siboni²,

Alahyarizadeh³

et al. 2022

Preprint

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Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly when applied to large, complex systems. To reduce such computational costs, a Unet-based artificial neural network is developed as a surrogate model in the current work. Training input for this network is obtained from the results of the numerical solution of initial-boundary-value problems (IBVPs) based on the Fan-Chen model for grain microstructure evolution. In particular, about 250 different simulations with varying initial order parameters are carried out and 200 frames of the time evolution of the phase fields are stored for each simulation. The network is trained with 90% of this data, taking the i-th frame of a simulation, i.e. order parameter field, as input, and producing the (i + 1)-th frame as the output. Evaluation of the network is carried out with a test dataset consisting of 2200 microstructures based on different configurations than originally used for training. The trained network is applied recursively on initial order parameters to calculate the time evolution of the phase fields. The results are compared to the ones obtained from the conventional numerical solution in terms of the errors in order parameters and the system's free energy. The resulting order parameter error averaged over all points and all simulation cases is 0.005 and the relative error in the total free energy in all simulation boxes does not exceed 1%.

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Recent advances in the kinetics of normal/abnormal grain growth: a review

Cited by 80 publications

References 129 publications

Microstructure Evolution in Plastic Deformed Bismuth Telluride for the Enhancement of Thermoelectric Properties

Microstructure Evolution in Plastic Deformed Bismuth Telluride for the Enhancement of Thermoelectric Properties

Insights from tyrosinase into the impacts of modified morphology of calcium carbonate on the nacre formation of pearl oysters

Accelerating phase-field-based simulation via machine learning

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