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Record W4390227430 · doi:10.1002/cctc.202301455

Patent Literature on Epoxidation of Propylene Over Silver Catalysts Using Molecular Oxygen – A Critical Industrial Review

2023· article· en· W4390227430 on OpenAlexaff
Adam Chojecki, Christopher R. Ho, V.J. Sussman

Bibliographic record

VenueChemCatChem · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsCatalysisMolecular oxygenChemistryOxygenOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract We summarize the patent literature related to the direct oxidation of propylene‐to‐propylene oxide (DOPO) over silver catalysts. Inventions claiming new compositions, preparation methods, and/or alternative process options are illustrated with data sampled from patent documents. Early claims focused on powders of silver (silver oxide) alloyed with and/or surface‐modified with one‐two promoterswhere silver was in large excess (>80 wt%). In the following decades, inventors refined their approach and pursued alternative bulk synthetic methods supplemented with impregnation to produce formulationscontaining lower amounts of silver in combination with various other transition metals, halides, alkalis and/or alkaline earth elements. Simultaneously, process development has facilitated increased PO selectivity via the co‐feeding of water and/or carbon dioxide at volume percent levels combined with smaller ppmV concentrations of organic chlorides and nitrogenoxides. At the end we briefly discuss how non‐catalytic oxidative routes to propylene oxide provide additional insights for new catalyst development. While these approaches hardly exceed 60 % PO selectivity, they may shed light on design criteria for catalysts capable of achieving that goal. Success in the field will require strong fundamental understanding of the activation of dioxygen on catalytic surfaces to define design requirements for silver catalystswith high selectivity to PO via direct oxidation.

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)0.002
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 score0.002
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.327
Teacher spread0.249 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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