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Record W4385144038 · doi:10.1093/micmic/ozad067.941

Phase Imaging Annihilation of Dislocation at Crystal Surface

2023· article· en· W4385144038 on OpenAlexaff
Rodney Herring

Bibliographic record

VenueMicroscopy and Microanalysis · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials scienceDislocationAnnihilationCrystal (programming language)Phase (matter)Surface (topology)CrystallographyCondensed matter physicsComposite materialGeometryPhysicsChemistryNuclear physicsComputer science

Abstract

fetched live from OpenAlex

Dislocations are arguably the most important defect in crystals. They determine the material’s strength, destroy electronic devices and degrade sensors, among other things. Interestingly, at their core is an undefined point, a singularity created by having a one atomic plane displacement, i.e., the Burgers vector, B , representing a 2π phase shift over a 360-degree rotation. Moreover, crystal surfaces cannot hold strain, which must somehow destabilize the dislocation when it annihilates at the surface. Recently, the core of the dislocation has been able to be phase imaged by the interference of two symmetrically Bragg diffracted beams [1]. As well, the bottom surface of the crystal has recently been phase imaged using differential phase contrast [2]. These two new phase imaging capabilities reported here have been used to image a dislocation passing from the top to the bottom surfaces of a gold specimen (Fig. 1). As the dislocation approaches the bottom surface, the dislocation can be seen to become unstable and then split to form a triple point (Fig. 2). The partial dislocations formed wrap around a 3D defect, likely a pit (Fig. 2). It is hypothesized that the dislocations at the triple point cross-slip from (111) onto three equivalent {111} planes. The partial dislocations form stacking faults, which are the surfaces of the pit (Fig. 3). The dislocations at three edges are shared among the three surfaces, which reduces their formation energy, E, by one half, i.e., E = 3 x 1/6 d<211> (Fig. 3), which is equivalent to half the Burgers vector of the original dislocation, E = ½ B<110> for FCC gold. The other half of the dislocation strain relaxes the structure providing energy to nucleate the 3D volume of the pit. The relaxed structure is too small to hold its Au atom, ejecting it, likely onto the surface of the crystal (Fig. 3). Thus, the energy required to nucleate the pit is ½ E/B<110l> where E is the material’s elastic modulus. For Au, the pit formation energy is ½ 76 GPa / 0.235 nm = 1.6 nN. Pits are known to exist on the surface of materials as traditionally seen by etching. This is the first time that their formation has been seen from the perspective of the inside of the crystal to the surface. A model is proposed based upon the destabilization of a dislocation to form three connected stacking fault surfaces to form a 3D pit. Simulations are still required to verify the proposed pit formation mechanism from this experimental observation [3]. – a) Bright field TEM image of a dislocation in Au specimen at exact Bragg diffraction, b) schematic of interfering symmetrically diffracted beams using an electron biprism, c) interferogram of dislocation in a). – a), Reconstructed phase image of dislocation in Fig. 1 showing annihilation of 2π shift at top (T) and bottom (B) surfaces, b) Magnified view of bottom surface showing unstable strain (US) along the dislocation just before creating a pit, c) 3x phase enhancement revealing triple point (TP) within circle formed by partial dislocations, which create three surfaces forming the pit. – Stacking fault surfaces outlined by red, green, and blue lines formed from partial dislocations having <211> Burgers vectors and {111} habit planes. The relaxation of the surfaces by ½ B ejects the corner atom to establish the nucleation of the 3D volume of the pit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.244
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractno

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