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Convergent‐Beam <scp>EMCD</scp> : Benefits, Pitfalls, and Applications

2016· other· en· W4241092333 on OpenAlexaff
Stefan Löffler, Walid Hetaba

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMcMaster University
FundersAustrian Science Fund
KeywordsDiffractionBeam (structure)OpticsDetectorMaterials scienceAperture (computer memory)PhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Energy‐loss magnetic chiral dichroism (EMCD) [1] is a state‐of‐the‐art technique to measure magnetic properties on the nanoscale using TEM and EELS. Since its first experimental realization a decade ago, it has seen tremendous progress and many applications, including the analysis of phase transitions [2] or of magnetic nanoparticles [3,4]. EMCD exploits the spin‐orbit interaction in the sample that gives rise to different probabilities for the transfer of ±1ħ of orbital angular momentum (OAM) to the probe beam. The net OAM of the probe beam is then measured interferometrically in the diffraction plane. The classical EMCD approach uses an incident plane wave and a “point‐like” detector placed on the Thales circle through two diffraction spots. This, however, has two major shortcomings: on the one hand, it limits the best achievable spatial resolution to the size of the selected area aperture; on the other hand, it places the detector far off‐axis where the intensity is very low. Thus, it is difficult or even impossible to use for many applications, including many application‐relevant cases such as nanoparticles, interfaces, defects, or beam‐sensitive materials where long exposure times are not possible. Here, we investigate the benefits of using a convergent incident beam for EMCD [5,6] by simulating the EMCD effect using the multislice algorithm [7] together with the mixed dynamic form factor (MDFF) approach [8] for a 10 nm Fe sample oriented in a systematic row condition including the (2 0 0) diffraction spot and an incident beam energy of 300 kV. As shown in fig. 1 for an Fe model system, a visible EMCD signal occurs up to large convergence angles as used in contemporary, Cs‐corrected STEMs. However, the region of large EMCD signal is “pushed out” away from the Thales circle towards the rim of the diffraction disks with increasing convergence angles. This is natural as the overall effect is expected to average out inside the diffraction disks. At the same time, it gives a good rule of thumb regarding the optimal position of the detector. In addition, we investigate the signal‐to‐noise ratio (SNR) as a function of convergence and collection angles. For practical applications, the SNR actually plays a much more important role than the theoretical expected EMCD effect as even a high EMCD signal is useless if it is well below the noise level. As shown in fig. 2, the SNR is highest for medium convergence and collection angles of the order of the Bragg angle (~7 mrad for the present model system), while the EMCD effect under these conditions is only slightly smaller than for the “classical” case of small convergence and collection angles and a detector positioned on the Thales circle. Thus, it is not necessary (and, indeed, counter‐productive) to use a “point‐like” detector and parallel illumination which severely limits the recorded intensity as well as the spatial resolution. Our analysis shows that convergent beam EMCD is not only possible, but actually is superior to classical EMCD in several aspects – most notably spatial resolution and SNR. This makes it the ideal tool for characterizing magnetic properties on the nanoscale, including the technologically relevant question of how the magnetic behavior changes at interfaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.006

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 head0.007
GPT teacher head0.221
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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