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Record W2946978818 · doi:10.1002/anie.201906350

Efficient and Selective Methane Borylation Through Pore Size Tuning of Hybrid Porous Organic‐Polymer‐Based Iridium Catalysts

2019· article· en· W2946978818 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.

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsMethaneIridiumCatalysisBorylationChemistryChemical engineeringPorosityHydrateYield (engineering)HeteroatomMaterials scienceOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract As a new energy source that could replace petroleum, the global reserves of methane hydrate (combustible ice) are estimated to be approximately 20 000 trillion cubic meters. A large amount of methane hydrate has been found under the seabed, but the transportation and storage of methane gas far from coastlines are technically unfeasible and expensive. The direct conversion of methane into value‐added chemicals and liquid fuels is highly desirable but remains challenging. Herein, we prepare a series of iridium complexes based on porous polycarbazoles with high specific areas and good thermochemical stabilities. Through structure tuning we optimized their catalytic activities for the selective monoborylation of methane. One of these catalysts (CAL‐3‐Ir) can produce methyl boronic acid pinacol ester (CH 3 Bpin) in 29 % yield in 9 h with a turnover frequency (TOF) of approximately 14 h −1 . Because its pore sizes favor monoborylated products, it has a high chemoselectivity for monoborylation (CH 3 Bpin:CH 2 (Bpin) 2 =16:1).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score1.000

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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