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Record W4302024211 · doi:10.1021/acssuschemeng.2c04409

Beckmann Rearrangement with Improved Atom Economy, Catalyzed by Inexpensive, Reusable, Bronsted Acidic Ionic Liquid

2022· article· en· W4302024211 on OpenAlexfundno aff
Alina Brzęczek‐Szafran, Karol Erfurt, Małgorzata Swadźba‐Kwaśny, Tomasz Piotrowski, Anna Chrobok

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2022
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsnot available
FundersNarodowe Centrum Badań i RozwojuNarodowa Agencja Wymiany AkademickiejQueen's UniversityQueen's University Belfast
KeywordsBeckmann rearrangementIonic liquidOleumChemistrySulfuric acidCatalysisAtom economySolventInorganic chemistryIonic bondingAtom (system on chip)Organic chemistryIonComputer science

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Inexpensive protic ionic liquids, synthesized from di- or triamines and excess sulfuric acid, were used as solvents and catalysts for the Beckmann rearrangement. The use of ionic liquids in place of oleum or sulfuric acid allowed the base neutralization step, which is required in the conventional Beckmann rearrangement, to be entirely avoided, thereby dramatically improving the atom economy. Using a biphasic water/organic solvent extraction system, it was possible to recover and reuse the ionic liquid. The proposed method was found applicable for the synthesis of a range of primary and secondary amides.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.169
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2022
Admission routes1
Has abstractyes

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