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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 by ΔE = ħ √(k/m) , where k is the force constant of the atomic bond and m the 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 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 teacher head, not a consensus.

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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