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Vibrational Spectroscopy in the Electron Microscope

2016· other· en· W3090168866 on OpenAlexaff
Ondrej L. Krivanek, Toshihiro Aoki, Philip E. Batson, Peter A. Crozier, Niklas Dellby, Raymond F. Egerton, Tracy C. Lovejoy, Peter Rez

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAtomic physicsSpectroscopyMolecular vibrationScanning transmission electron microscopyInfrared spectroscopyResolution (logic)ElectronMolecular physicsChemistryMaterials scienceScanning electron microscopePhysicsOpticsRaman spectroscopy

Abstract

fetched live from OpenAlex

Vibrational spectroscopy in the scanning transmission electron microscope (STEM) was introduced two years ago [1, 2], and it has made much progress since. It has opened a new window on the world of materials, in which nothing is quite like it was before. The main vibrational modes occur at energies of 0‐500 meV, and exploring them requires a monochromated STEM‐EELS system with an energy resolution The energy of vibrational modes is given byΔE = ħ √(k/m), wherekis the force constant of the atomic bond andmthe effective mass of the vibrating nucleus. Strongly bonded light atoms give the highest vibrational energies, starting with hydrogen, an element that is nearly invisible in traditional electron microscopy. Fig. 1(a) shows a vibrational spectrum of Ca(OH)2[3], in which the peak at 452 meV is due to O‐H stretch, and Fig. 1(b) shows the particle from which the spectrum was recorded. Fig. 1(c) shows how the strength of the vibrational peak varied with the distance from the particle: the signal decayed only gradually outside the particle, and was still 50% strong 35 nm away. Fig. 2 shows an EEL spectrum of guanine compared to an IR spectrum from the same specimen [4]. The agreement between the two types of spectra is very good. EELS has worse energy resolution (~10 meV), but much better spatial resolution than regular IR. As is typical of vibrational spectroscopies, the different peaks can be assigned to different types of bonds and vibration modes (see the inset in Fig. 1). In order to minimize radiation damage, both the OH and guanine spectra were acquired in an “aloof” mode, with the electron beam parked just outside the sample [1, 3‐5]. Aloof spectroscopy makes it possible to select the maximum energy of the beam‐sample interaction, simply by adjusting the beam‐sample distance [4,5]. Its great import to vibrational EELS is that the vibrational signal can be excited even when the interaction energy is limited so that ionization damage of the sample cannot occur. It may even be possible to spatially map the vibrational features of a beam‐sensitive sample by “coarse step (leapfrog) scanning”: scanning with a discrete pixel increment of 10‐100 nm, so that even though the area that the beam traverses in each new position is essentially destroyed, large parts of the sample are not touched by the beam and remain in a pristine state [6].

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.008

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 head0.301
Teacher spread0.296 · 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 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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Citations2
Published2016
Admission routes1
Has abstractyes

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