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Record W4242558343 · doi:10.1002/9783527697106.ch4

Photon‐In Photon‐Out Spectroscopic Techniques for Materials Analysis: Some Recent Developments

2018· other· en· W4242558343 on OpenAlexafffund
Tsun‐Kong Sham

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicGa2O3 and related materials
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsXANESPhotonAbsorption (acoustics)SpectroscopyMaterials scienceStreak cameraTwo-photon absorptionOptoelectronicsExcited stateFluorescenceLuminescenceOpticsPhysicsAtomic physicsLaser

Abstract

fetched live from OpenAlex

This chapter presents the recent developments of two synchrotron materials analysis techniques and their synergy under the common theme of photon-in photon-out spectroscopic techniques. The first technique is fluorescence yield (FLY) and inverse partial fluorescence yield (IPFY) for X-ray absorption near edge structure (XANES) measurements using Si drift detectors; a study of LiFePO 4 (LFP) is used as the example. In the soft X-ray region, nearly all the photons are absorbed because the samples are often thicker than the attenuation length. The second technique is 2D XANES-X-ray excited optical luminescence (XEOL) spectroscopy in both the energy and time domain for the investigation of the band gap and optical properties of light-emitting ZnO-GaN nanostructure solid solutions. The lifetime of the optical decay of XEOL (TRXEOL) from GaN-ZnO (GZNO) has been investigated using an optical streak camera (OSC) with a fast sweep. The chapter then discusses the prospects of the techniques for future applications and other related studies.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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
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".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractyes

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