A model to predict image formation in the three-dimensional field ion microscope

This article presents a numerical model dedicated to the simulation of field ion microscopy (FIM). FIM was the first technique to image individual atoms on the surface of a material. By a careful control of the field evaporation of the atoms from the surface, the bulk of the material exposed, and, through a digitally processing a sequence of micrographs, a three-dimensional reconstruction can be achieved. 3DFIM is particularly suited to the direct observation of crystalline defects such as vacancies, interstitials, vacancy clusters, dislocations, and any combinations of theses defects that underpin the physical properties of materials. This makes 3DFIM extremely valuable for many material science and engineering applications, and further developing this technique is becoming crucial. The proposed model enables the simulation of imaging artefacts that are induced by non-regular field evaporation and by the impact of the perturbation of the electric field distribution of the distorted distribution of atoms close to defects. The model combines the meshless algorithm for field evaporation proposed by Rolland et al. (Robin-Rolland Model, or RRM) with fundamental aspects of the field ionization process of the gas image involved in FIM

Atomistic Simulations of Surface Effects Under High Electric Fields

International audienc

Dislocation exhaustion and ultra-hardening of nanograined metals by phase transformation at grain boundaries

The development of high-strength metals has driven the endeavor of pushing the limit of grain size (d) reduction according to the Hall-Petch law. But the continuous grain refinement is particularly challenging, raising also the problem of inverse Hall-Petch effect. Here, we show that the nanograined metals (NMs) with d of tens of nanometers could be strengthened to the level comparable to or even beyond that of the extremely-fine NMs (d ~ 5 nm) attributing to the dislocation exhaustion. We design the Fe-Ni NM with intergranular Ni enrichment. The results show triggering of structural transformation at grain boundaries (GBs) at low temperature, which consumes lattice dislocations significantly. Therefore, the plasticity in the dislocation-exhausted NMs is suggested to be dominated by the activation of GB dislocation sources, leading to the ultra-hardening effect. This approach demonstrates a new pathway to explore NMs with desired properties by tailoring phase transformations via GB physico-chemical engineering

Revealing atomic-scale vacancy-solute interaction in nickel

Imaging individual vacancies in solids and revealing their interactions with solute atoms remains one of the frontiers in microscopy and microanalysis. Here we study a creep-deformed binary Ni-2 at.% Ta alloy. Atom probe tomography reveals a random distribution of Ta. Field ion microscopy, with contrast interpretation supported by density-functional theory and time-of-flight mass spectrometry, evidences a positive correlation of tantalum with vacancies. Our results support solute-vacancy binding, which explains improvement in creep resistance of Ta-containing Ni-based superalloys and helps guide future material design strategies.Comment: Submitted to Physics Review Lette

A New Class of Cluster–Matrix Nanocomposite Made of Fully Miscible Components

Nanocomposite materials, consisting of two or more phases, at least one of which has a nanoscale dimension, play a distinctive role in materials science because of the multiple possibilities for tailoring their structural properties and, consequently, their functionalities. In addition to the challenges of controlling the size, size distribution, and volume fraction of nanometer phases, thermodynamic stability conditions limit the choice of constituent materials. This study goes beyond this limitation by showing the possibility of achieving nanocomposites from a bimetallic system, which exhibits complete miscibility under equilibrium conditions. A series of nanocomposite samples with different compositions are synthesized by the co-deposition of 2000-atom Ni-clusters and a flux of Cu-atoms using a novel cluster ion beam deposition system. The retention of the metastable nanostructure is ascertained from atom probe tomography (APT), magnetometry, and magnetotransport studies. APT confirms the presence of nanoscale regions with ≈100 at% Ni. Magnetometry and magnetotransport studies reveal superparamagnetic behavior and magnetoresistance stemming from the single-domain ferromagnetic Ni-clusters embedded in the Cu-matrix. Essentially, the magnetic properties of the nanocomposites can be tailored by the precise control of the Ni concentration. The initial results offer a promising direction for future research on nanocomposites consisting of fully miscible elements

A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization

Imaging individual solute atoms at crystalline imperfections in metals

Directly imaging all atoms constituting a material and, maybe more importantly, crystalline defects that dictate materials\u27 properties, remains a formidable challenge. Here, we propose a new approach to chemistry-sensitive field-ion microscopy (FIM) combining FIM with time-of-flight mass-spectrometry (tof-ms). Elemental identification and correlation to FIM images enabled by data mining of combined tof-ms delivers a truly analytical-FIM (A-FIM). Contrast variations due to different chemistries is also interpreted from density-functional theory (DFT). A-FIM has true atomic resolution and we demonstrate how the technique can reveal the presence of individual solute atoms at specific positions in the microstructure. The performance of this new technique is showcased in revealing individual Re atoms at crystalline defects formed in Ni–Re binary alloy during creep deformation. The atomistic details offered by A-FIM allowed us to directly compare our results with simulations, and to tackle a long-standing question of how Re extends lifetime of Ni-based superalloys in service at high-temperature

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

This is a viewpoint paper on recent progress in the understanding of the microstructure–property relations of advanced high-strength steels (AHSS). These alloys constitute a class of high-strength, formable steels that are designed mainly as sheet products for the transportation sector. AHSS have often very complex and hierarchical microstructures consisting of ferrite, austenite, bainite, or martensite matrix or of duplex or even multiphase mixtures of these constituents, sometimes enriched with precipitates. This complexity makes it challenging to establish reliable and mechanism-based microstructure–property relationships. A number of excellent studies already exist about the different types of AHSS (such as dual-phase steels, complex phase steels, transformation-induced plasticity steels, twinning-induced plasticity steels, bainitic steels, quenching and partitioning steels, press hardening steels, etc.) and several overviews appeared in which their engineering features related to mechanical properties and forming were discussed. This article reviews recent progress in the understanding of microstructures and alloy design in this field, placing particular attention on the deformation and strain hardening mechanisms of Mn-containing steels that utilize complex dislocation substructures, nanoscale precipitation patterns, deformation-driven transformation, and twinning effects. Recent developments on microalloyed nanoprecipitation hardened and press hardening steels are also reviewed. Besides providing a critical discussion of their microstructures and properties, vital features such as their resistance to hydrogen embrittlement and damage formation are also evaluated. We also present latest progress in advanced characterization and modeling techniques applied to AHSS. Finally, emerging topics such as machine learning, through-process simulation, and additive manufacturing of AHSS are discussed. The aim of this viewpoint is to identify similarities in the deformation and damage mechanisms among these various types of advanced steels and to use these observations for their further development and maturation

On chemically sensitive atomic scale imaging

Der Vorgänger der Atomsonden-Tomographie (APT), die Feldionenmikroskopie (FIM) genannt, ist bekannt für seine hohe räumliche Auflösung, allerdings auf Kosten der chemischen Identität. Um eine verbesserte räumliche Genauigkeit zu erreichen, werden hier Data-Mining-Routinen entwickelt. Unter Anwendung dieser Routinen werden Daten aus einer Folge von FIM-Bildern von W extrahiert. Die Bildstörungen werden durch Vergleich mit atomistischen und FIM-Bildsimulationen analysiert und zeigen, dass die abgebildeten atomaren Verschiebungen eine Folge der Umverteilung des elektrostatischen Feldes sind. Um die Unfähigkeit der FIM, zwischen Atomen verschiedener chemischer Spezies zu unterscheiden, anzugehen, wird die Entwicklung einer chemisch empfindlichen FIM detailliert beschrieben. Diese Technik wird verwendet, um die Trennung von Re zu einer Versetzung in einer kriechverformten Ni-Legierung zu zeigen.The predecessor of Atom probe tomography (APT), called the field ion microscopy (FIM) is known for its high spatial resolution, but at the expense of the chemical identity. To achieve improved spatial accuracy, data mining routines are developed here. Applying these routines, data extracted from a sequence of FIM images of W. The imaging distortions are analysed by comparing with atomistic and FIM image simulations, showing that the imaged atomic displacements are a consequence of the electrostatic field redistributions. To tackle the FIM's inability to discern between atoms of different chemical species, the development of a chemically-sensitive FIM is detailed. This technique is used to show the segregation of Re to a dislocation in a creep deformed Ni alloy