Biogenic calcium carbonate forms the inorganic component of seashells, otoliths, and many marine skeletons, and its formation is directed by an ordered template of macromolecules. Classical nucleation theory considers crystal formation to occur from a critical nucleus formed by the assembly of ions from solution. Using cryotransmission electron microscopy, we found that template-directed calcium carbonate formation starts with the formation of prenucleation clusters. Their aggregation leads to the nucleation of amorphous nanoparticles in solution. These nanoparticles assemble at the template and, after reaching a critical size, develop dynamic crystalline domains, one of which is selectively stabilized by the template. Our findings have implications for template-directed mineral formation in biological as well as in synthetic systems.

The initial stages of template-controlled CaCO3 formation revealed by Cryo-TEM

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Selective laser melting of an equiatomic CoCrFeMnNi high-entropy alloy: Processability, non-equilibrium microstructure and mechanical property

Grain boundary segregation leads to nanoscale chemical variations that can alter a material's performance by orders of magnitude (e.g., embrittlement). To understand this phenomenon, a large number of grain boundaries must be characterized in terms of both their five crystallographic interface parameters and their atomic-scale chemical composition. We demonstrate how this can be achieved using an approach that combines the accuracy of structural characterization in transmission electron microscopy with the 3D chemical sensitivity of atom probe tomography. We find a linear trend between carbon segregation and the misorientation angle ω for low-angle grain boundaries in ferrite, which indicates that ω is the most influential crystallographic parameter in this regime. However, there are significant deviations from this linear trend indicating an additional strong influence of other crystallographic parameters (grain boundary plane, rotation axis). For high-angle grain boundaries, no general trend between carbon excess and ω is observed; i.e., the grain boundary plane and rotation axis have an even higher influence on the segregation behavior in this regime. Slight deviations from special grain boundary configurations are shown to lead to unexpectedly high levels of segregation.

Quantification of Grain Boundary Segregation in Nanocrystalline Material

https://hal.archives-ouvertes.fr/hal-01832872/document

Segregation assisted grain boundary precipitation in a model Al-Zn-Mg-Cu alloy

Analysis and design of materials and fluids requires understanding of the fundamental relationships between structure, composition, and properties. Dislocations and grain boundaries influence microstructure evolution through the enhancement of diffusion and by facilitating heterogeneous nucleation, where atoms must overcome a potential barrier to enable the early stage of formation of a phase. Adsorption and spinodal decomposition are known precursor states to nucleation and phase transition; however, nucleation remains the less well-understood step in the complete thermodynamic sequence that shapes a microstructure. Here, we report near-atomic-scale observations of a phase transition mechanism that consists in solute adsorption to crystalline defects followed by linear and planar spinodal fluctuations in an Fe-Mn model alloy. These fluctuations provide a pathway for austenite nucleation due to the higher driving force for phase transition in the solute-rich regions. Our observations are supported by thermodynamic calculations, which predict the possibility of spinodal decomposition due to magnetic ordering.

/pdf/phase-nucleation-through-confined-spinodal-fluctuations-at-1ics9l1e7l.pdf

Phase nucleation through confined spinodal fluctuations at crystal defects evidenced in Fe-Mn alloys

We introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrations on the measurement of the thickness, composition, and interfacial excess of the decorated interface are systematically analyzed and discussed.

An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography

This paper address the problem of temporal pattern mining from multiple data streams containing temporal events. Temporal events are considered as real world events aligned with comprehensive starting and ending timing information rather than simple integer timestamps. Predefined relations, such as "before" and "after", describe the heterogeneous relationships hidden in temporal data with limited diversity. In this work, the relationships among events are learned dynamically from the temporal information. Each event is treated as an object with a label and numerical attributes. An online-offline model is used as the primary structure for analyzing the evolving multiple streams. Different distance functions on temporal events and sequences can be applied depending on the application scenario. A prefix tree is introduced for a fast incremental pattern update.Events in the real world usually persist for some period. It is more natural to model events as intervals with temporal information rather than as points on the timeline. Based on the representation proposed in this work, our approach can also be extended to handle interval data. Experiments show how the method, with richer information and more accurate results than the state-of-the-art, processes both point-based and interval-based event streams efficiently.

Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering

Almost all activities observed in nowadays applications are correlated with a timing sequence. Users are mainly looking for interesting sequences out of such data. Sequential pattern mining algorithms aim at finding frequent sequences. Usually, the mined activities have timing durations that represent time intervals between their starting and ending points. The majority of sequential pattern mining approaches dealt with such activities as a single point event and thus lost valuable information in the collected patterns. Recently, some approaches have carefully considered this interval-based nature of the events, but they have major limitations. They concentrate only on the order of events without taking the durations of the gaps between them into account and usually employ a binary representation to describe patterns. To resolve these problems, we propose the PIVOTMiner, an interval-based data mining algorithm using a geometric representation approach of intervals. Noisy events can be served with the geometric representation and a fuzzy set can be retrieved from the geometric patterns. PIVOTMiner can flexibly work on data presented as any number of not necessarily aligned interval sequences and in particular can utilize data presented as single interval sequence stream without the need to create samples. Our experimental results on both synthetic and real-world smart home datasets show that the information presented in our mined patterns are richer than those of most state-of-the-art algorithms while spending considerably smaller running times.

A geometric approach for mining sequential patterns in interval-based data streams

Frequent itemset mining is substantially studied in the past decades. In varies practical applications, frequent patterns are obvious and expected, while really interesting information might hide in obscure rarity. However, existing rare pattern mining approaches are time and memory consuming due to their apriori based candidate generation step. In this paper, we propose an efficient rare pattern extraction algorithm, which is capable of extracting the complete set of rare patterns using a top-down traversal strategy. A negative item tree is employed to accelerate the mining process. Pattern growth paradigm is used and therefore avoids expensive candidate generation.

Yifeng Lu

Papers

Phase nucleation through confined spinodal fluctuations at crystal defects evidenced in Fe-Mn alloys

An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography

Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering

A geometric approach for mining sequential patterns in interval-based data streams

Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal