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Record W4285398573 · doi:10.1149/ma2022-01451885mtgabs

Correlating Raman and X-Ray Absorption Spectroscopy to Analyze Defects in Hematite Photoandoes

2022· article· en· W4285398573 on OpenAlexaff
Yutong Liu, Rodney D. L. Smith

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHematiteRaman spectroscopySpectroscopyAbsorption spectroscopyMaterials scienceAnalytical Chemistry (journal)Absorption (acoustics)ChemistryMineralogyOpticsPhysics

Abstract

fetched live from OpenAlex

Over two decades of research on hematite photoanodes have generated significant information on the electronic structure, photophysics and electron transfer mechanism for the oxygen evolution reaction. A surprisingly high degree of variability in photoelectrocatalytic performance continues across the literature despite these advances, however, suggesting missing information and uncontrolled variables. This encouraged us to pursue spectroscopic methods capable of identification, and possibly quantification of specific defects within the hematite lattice, and to map their presence to behaviors observed in photoelectrochemistry. We approach this issue by applying structure-property analysis to hematite samples treated under either O 2 or N 2 environments with variable performance in photoelectrocatalytic oxygen evolution. X-ray absorption fine-structure spectroscopy and Raman spectroscopy can provide short-range coordination shells and longer-range order feature respectively to describe the structure of samples across the series. Different correlations between these structural parameters and photoelectrochemical performance reveal distinct defects for sample sets annealed in O 2 or N 2 . These distortions can be observed by processed Raman spectrum data, suggesting that it may be possible to calibrate the width, energy, and intensity of peaks in Raman spectra to enable direct analysis of defects in hematite photoanodes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.250
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.242
Teacher spread0.233 · 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.

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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