Diffusion-defining atomic-scale spinodal decomposition within nanoprecipitates

Orthacker, Angelina; Haberfehlner, Georg; Taendl, Johannes; Poletti, María Cecilia; Sonderegger, Bernhard; Kothleitner, Gerald

doi:10.1038/s41563-018-0209-z

Cited by 49 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the volume fraction of B2 nanoprecipitates remains almost unchanged with the aging time prolonging in the current alloy, the classic Ostwald ripening theory proposed by Philippe and Voorhees (i.e. the PV theory) could be applied to study the coarsening behaviour of B2 nanoparticles, which has been extensively used in multicomponent alloys [32,33]. Such a time-dependent coarsening process could be described with the following Equation (1):…”

Section: Resultsmentioning

confidence: 99%

Coherent precipitation and stability of cuboidal B2 nanoparticles in a ferritic Fe–Cr–Ni–Al superalloy

Wang

Niu

et al. 2021

Materials Research Letters

View full text Add to dashboard Cite

The present work developed a new Fe-based superalloy of Fe-10.9Cr-13.9Ni-6.4Al-2.2Mo-0.5W-0.04Zr-0.005B (weight percent, wt. %) with cuboidal B2 nanoparticles coherently precipitated into the body-centred-cubic (BCC) matrix. This alloy possesses excellent microstructural stability at 973 K with a slow particle coarsening rate and the B2 nanoprecipitates still keep cuboidal after 1000 h-aging, which is ascribed to the moderate lattice misfit ( = 0.24-0.67%) between BCC and B2 phases. Also, the current alloy exhibits a prominent mechanical property with a high yield strength of σ YS = 238-258 MPa at 973 K. IMPACT STATEMENTA Fe-based superalloy with cuboidal B2 nanoparticles coherently precipitated into BCC matrix exhibits high microstructural stability and prominent mechanical property at 973 K.

show abstract

Section: Resultsmentioning

confidence: 99%

Coherent precipitation and stability of cuboidal B2 nanoparticles in a ferritic Fe–Cr–Ni–Al superalloy

Wang

Niu

et al. 2021

Materials Research Letters

View full text Add to dashboard Cite

show abstract

“…(Models with maximal degrees per variable d ¼ 3; 4; 5 have been computed as well to validate that the correct degree d ¼ 4 can indeed be recovered. This degree d ¼ 4 can be obtained from equation (4) and the parameters obtained from the thermodynamic database COST 531. 41 ) Once the matrices with the unknown coefficients U ð1Þ , U ð2Þ , U ð3Þ are found, the factor matrices A ðnÞ , _ A ðnÞ and € A ðnÞ , for n ¼ 1; 2; 3, are constructed using equation (20) and basis matrices B, _ B, € B, which are evaluated on a grid with δ x ¼ 0:0001.…”

Section: Thermodynamic Tensor Model Trainingmentioning

confidence: 99%

“…High entropy or multi-principle element alloys, for example, can give access to a wide range of properties and combinations of properties that cannot be obtained in alloys based on a single major element. 1 Moreover, the large range over which the diffusion properties of the different elements can vary gives rise to complex precipitation, interdiffusion, and coarsening paths, [2][3][4] resulting in new ways to stabilize metastable phases or structures for extended times. 5 Adding too many alloying elements may, however, also unnecessarily complicate material recovery and recycling.…”

Section: Introductionmentioning

confidence: 99%

Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction

et al. 2020

View full text Add to dashboard Cite

Multicomponent alloys show intricate microstructure evolution, providing materials engineers with a nearly inexhaustible variety of solutions to enhance material properties. Multicomponent microstructure evolution simulations are indispensable to exploit these opportunities. These simulations, however, require the handling of high-dimensional and prohibitively large data sets of thermodynamic quantities, of which the size grows exponentially with the number of elements in the alloy, making it virtually impossible to handle the effects of four or more elements. In this paper, we introduce the use of tensor completion for highdimensional data sets in materials science as a general and elegant solution to this problem. We show that we can obtain an accurate representation of the composition dependence of high-dimensional thermodynamic quantities, and that the decomposed tensor representation can be evaluated very efficiently in microstructure simulations. This realization enables true multicomponent thermodynamic and microstructure modeling for alloy design.npj Computational Materials (2020) 6:2 ; https://doi.

show abstract

“…14 Over the last few years, analytical tomography has thereby become an important characterization method in nanoscale materials science. 13,[15][16][17][18] The 3D resolution of a sinogram is generally limited by the number of projections which can be acquired in an experiment and by the achievable tilt range. Reasons for these limitations are the overall dose the sample can sustain, the accuracy of the sample positioning mechanism for very small tilt steps, the geometry of the sample or the sample holder, as well as the limited time for an experiment.…”

Section: Introductionmentioning

confidence: 99%

“…A recent example is the unveiling of elemental concentration variations within spherical precipitates in an AlMgScZr-alloy. 17 In such situations it would not be possible to use TV regularization. Beyond overcoming this drawback, a second novelty of our approach is that we developedand make publicly available 37a Python 38 /OpenCL 39 -based implementation for all components of the reconstruction algorithm, which is crucial in particular for 3D reconstructions as it significantly reduces the computation time.…”

Section: Introductionmentioning

confidence: 99%

Total generalized variation regularization for multi-modal electron tomography

et al. 2019

Self Cite

View full text Add to dashboard Cite

In multi-modal electron tomography, tilt series of several signals such as X-ray spectra, electron energyloss spectra, annular dark-field, or bright-field data are acquired at the same time in a transmission electron microscope and subsequently reconstructed in three dimensions. However, the acquired data are often incomplete and suffer from noise, and generally each signal is reconstructed independently of all other signals, not taking advantage of correlation between different datasets. This severely limits both the resolution and validity of the reconstructed images. In this paper, we show how image quality in multimodal electron tomography can be greatly improved by employing variational modeling and multichannel regularization techniques. To achieve this aim, we employ a coupled Total Generalized Variation (TGV) regularization that exploits correlation between different channels. In contrast to other regularization methods, coupled TGV regularization allows to reconstruct both hard transitions and gradual changes inside each sample, and links different channels at the level of first and higher order derivatives.This favors similar interface positions for all reconstructions, thereby improving the image quality for all data, in particular, for 3D elemental maps. We demonstrate the joint multi-channel TGV reconstruction on tomographic energy-dispersive X-ray spectroscopy (EDXS) and high-angle annular dark field (HAADF) data, but the reconstruction method is generally applicable to all types of signals used in electron tomography, as well as all other types of projection-based tomographies.

show abstract

Diffusion-defining atomic-scale spinodal decomposition within nanoprecipitates

Cited by 49 publications

References 30 publications

Coherent precipitation and stability of cuboidal B2 nanoparticles in a ferritic Fe–Cr–Ni–Al superalloy

Coherent precipitation and stability of cuboidal B2 nanoparticles in a ferritic Fe–Cr–Ni–Al superalloy

Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction

Total generalized variation regularization for multi-modal electron tomography

Contact Info

Product

Resources

About