Effect of interstitial and substitution alloying elements on the intrinsic stacking fault energy of nanocrystalline fcc-iron by atomistic simulation study

Mohammadzadeh, Mina; Mohammadzadeh, Roghayeh

doi:10.1007/s00339-017-1297-3

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…Recent experiments have shown that the underlying cause of the unique combination is the change in the deformation mechanisms from slip to twinning to transformation-induced plasticity [9][10][11][12][13][14]. These mechanism changes have been directly correlated to the stacking fault energy (SFE) of alloys, where it is observed that lowering the SFE favors the sequential change in the deformation mechanisms [9,[13][14][15][16][17][18][19]. Li et al [20] experimentally showed this phenomenon in Fe 80−x Mn x Co 10 Cr 10 , where the change in Mn from 45% to 30% resulted in the sequential observation of three deformation mechanisms with simultaneous decrease in SFE and increase in both strength and ductility.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

Arora

Aidhy

2020

Metals

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Recent works have revealed a unique combination of high strength and high ductility in certain compositions of high-entropy alloys (HEAs), which is attributed to the low stacking fault energy (SFE). While atomistic calculations have been successful in predicting the SFE of pure metals, large variations up to 200 mJ/m2 have been observed in HEAs. One of the leading causes of such variations is the limited number of atoms that can be modeled in atomistic calculations; as a result, due to random distribution of elements in HEAs, various nearest neighbor environments may not be adequately captured in small supercells resulting in different SFE values. Such variation further increases with the increase in the number of elements in a given composition. In this work, we use machine learning to overcome the limitation of smaller system sizes and provide a methodology to significantly reduce the variation and uncertainty in predicting SFEs. We show that the SFE can be accurately predicted across the composition ranges in binary alloys. This capability then enables us to predict the SFE of multi-elemental alloys by training the model using only binary alloys. Consequently, SFEs of complex alloys can be predicted using a binary alloys database, and the need to perform calculations for every new composition can be circumvented.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

Arora

Aidhy

2020

Metals

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“…A solid based ratio of SIA to PDMS proportional to 1:2 that is, (0.5 wt%) and PDMS (1 wt%) in the coating solution, gives an approximate image of the surface conditions, considering the following assumptions: The distribution of nanoparticles in the PDMS layer is almost uniform. The entire space between the nanoparticles is filled with PDMS. According to DLS test, nanoparticles are completely spherical. From the calculations presented in Table 3, basically, a packing factor higher than 0.74 (which belongs to an FCC lattice configuration) is not possible for uniform sphere particles. Two hypotheses can be suggested: First, due to the particle size distribution, smaller particles may have filled the empty spaces among the larger particles (interstitial substitution), 33 therefore we may face a packing factor as high as 83%. Second, by comparing the volume fraction of SIA nanoparticles and PDMS before mixing (i.e., 83% SIA) with the FCC lattice factor (74%), it can be concluded that some of the nanoparticles most probably have migrated out of the matrix after drying step and formation of the coating layer, as our observations disclosed it.…”

Section: Resultsmentioning

confidence: 99%

“…From the calculations presented in Table 3, basically, a packing factor higher than 0.74 (which belongs to an FCC lattice configuration) is not possible for uniform sphere particles. Two hypotheses can be suggested: First, due to the particle size distribution, smaller particles may have filled the empty spaces among the larger particles (interstitial substitution), 33 therefore we may face a packing factor as high as 83%. Second, by comparing the volume fraction of SIA nanoparticles and PDMS before mixing (i.e., 83% SIA) with the FCC lattice factor (74%), it can be concluded that some of the nanoparticles most probably have migrated out of the matrix after drying step and formation of the coating layer, as our observations disclosed it.…”

Section: Edx Analysismentioning

confidence: 99%

On performance of polycarbonate/silica aerogel nanoparticle mixed matrix hollow fiber membrane coated with polydimethylsiloxane for membrane distillation

Rezaei

Hashemifard

Abbasi

2022

J of Applied Polymer Sci

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The membrane distillation process is one of the most important membrane technologies being developed for water desalination, which has the ability to compete with expensive processes such as reverse osmosis. In this study, the performance of polycarbonate (PC) hollow fiber membranes modified with silica aerogel nanoparticles (SIA-NPs) and polydimethylsiloxane (PDMS) coating solution for direct contact membrane distillation was investigated. The membranes were fabricated by dry jet-wet spinning method. In this respect, 0.25, 0.5, and 1 wt% of SIA nanoparticles were coated over the hollow fiber surface in order to improve the performance of the membranes. The results indicated that the optimum loading of SIA was 0.5 wt% at which the contact angle of the surface shifted from 77.5 to 100 relative to the unmodified PC membrane.Although the addition of SIA nanoparticles to the membrane matrix did not alter the membrane conductivity, it increased the membrane thickness, thereby reduced the heat loss of the membrane. Accordingly, the results of performance tests for the membrane distillation (MD) showed that by adding 0.5% SIA nanoparticles to the membrane bulk and coating the surface with 0.5% SIA nanoparticles in 1.0% PDMS solution and 96% increase in energy efficiency, 33% increase in water flux, as well as higher stability of salt rejection were obtained. This study proved that the application of SIA nanoparticles have a great potential to modify and improve MD performance.

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“…The deformation mechanisms of γ mainly depend on the structure stacking fault energy (SFE) [58], which is a function of the chemical composition and temperature [59][60][61]. These mechanisms can be of different types: (a) the displacive SIT/DIT are promoted if SFE <18 mJ m −2 , (b) twinning occurs if SFE is in the range 18-45 mJ m −2 and (c) dislocation glide happens if SFE >45 mJ m −2 [62].…”

Section: Stacking Fault Energymentioning

confidence: 99%

Future Trends on Displacive Stress and Strain Induced Transformations in Steels

Eres-Castellanos

García‐Mateo

Caballero

2021

Metals

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Displacive stress and strain induced transformations are those transformations that occur when the formation of martensite or bainitic ferrite is promoted by the application of stress or strain. These transformations have been shown to be one of the mechanisms by which the mechanical properties of a microstructure can be improved, as they lead to a better ductility and strength by the transformation induced plasticity effect. This review aims to summarize the fundamental knowledge about them, both in fully austenitic or in multiphase structures, pointing out the issues that—according to the authors’ opinion—need further research. Knowing the mechanisms that govern the stress and strain induced transformation could enable to optimize the thermomechanical treatments and improve the final microstructure properties.

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Effect of interstitial and substitution alloying elements on the intrinsic stacking fault energy of nanocrystalline fcc-iron by atomistic simulation study

Cited by 15 publications

References 38 publications

Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

On performance of polycarbonate/silica aerogel nanoparticle mixed matrix hollow fiber membrane coated with polydimethylsiloxane for membrane distillation

Future Trends on Displacive Stress and Strain Induced Transformations in Steels

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