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Record W3185974931 · doi:10.3390/catal11080893

Methanol to Formaldehyde: An Overview of Surface Studies and Performance of an Iron Molybdate Catalyst

2021· article· en· W3185974931 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCatalysts · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMolybdateCatalysisFormaldehydeMethanolOxideIron oxideCatalyst supportChemistryPhase (matter)Inorganic chemistryMolybdenum oxideSelectivityMaterials scienceChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Formaldehyde is a primary chemical in the manufacturing of various consumer products. It is synthesized via partial oxidation of methanol using a mixed oxide iron molybdate catalyst (Fe2(MoO4)3–MoO3). This is one of the standard energy-efficient processes. The mixed oxide iron molybdate catalyst is an attractive commercial catalyst for converting methanol to formaldehyde. However, a detailed phase analysis of each oxide phase and a complete understanding of the catalyst formulation and deactivation studies is required. It is crucial to correctly formulate each oxide phase and influence the synthesis methods precisely. A better tradeoff between support and catalyst and oxygen revival on the catalyst surface is vital to enhance the catalyst’s selectivity, stability, and lifetime. This review presents recent advances on iron molybdate’s catalytic behaviour for formaldehyde production—a deep recognition of the catalyst and its critical role in the processes are highlighted. Finally, the conclusion and prospects are presented at the end.

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.

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 score0.006
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.067
GPT teacher head0.355
Teacher spread0.288 · 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