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Record W3166570024 · doi:10.1002/cjce.24216

Experimental methods in chemical engineering: Mössbauer spectroscopy

2021· article· en· W3166570024 on OpenAlexaffvenue
Claudia L. Bianchi‬, Ridha Djellabi‬‬‬‬‬‬‬‬, Alessandro Ponti, Gregory S. Patience, Ermelinda Falletta

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMössbauer spectroscopyRaman spectroscopySpectroscopyMagnetiteConversion electron mössbauer spectroscopyMaterials scienceChemistryMössbauer effectCrystallographyPhysicsMetallurgyOptics

Abstract

fetched live from OpenAlex

Abstract When a free nucleus absorbs or emits a gamma ray, it recoils to conserve energy, just like a gun recoils after shooting a bullet. Nuclei bound to a crystal lattice conserve energy when they absorb or emit gamma rays from a nuclear transition as they are fixed so their movement is restricted. This restriction is recoilless nuclear resonance fluorescence—the Mössbauer effect. The energy transmitted through a sample reveals its electronic and molecular structure and magnetic properties but only when the atoms in the source and sample are the same isotope— 57 Co/ 57 Fe is the most common couple. So, many of its applications are to identify iron species or how they change as a function of environmental conditions, like corrosion. A bibliometric map identified six major clusters centred around: nanoparticles and magnetite (Fe 3 O 4 ), crystal structure and spectroscopy, oxidation and catalysis, X‐ray diffraction (XRD) and Raman spectroscopy, 57 Fe and cathodes, and Co and thin films. In the last 30 years, the number of articles per year that mention the technique has hovered around 1250. More recently, Mössbauer spectroscopy has experienced a great rediscovery, particularly in the industrial sector for the solution of some problems, but also in space exploration.

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 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 score0.026
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.263
Teacher spread0.252 · 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

Citations27
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicIron oxide chemistry and applicationsFrench-language works237,207