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Record W4311325161 · doi:10.54097/hset.v21i.3185

Synthesis of ethanol, methanol and carboxylic acids using copper-based catalysts

2022· article· en· W4311325161 on OpenAlexaff
Xiyu Fan, Jilin Gong, Linyan Zhou

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMethanolCatalysisReuseChemistryCarboxylic acidEthanolCopperCarbon dioxideGreenhouse gasOrganic chemistryEnvironmental scienceEnvironmental chemistryWaste managementEcologyEngineering

Abstract

fetched live from OpenAlex

Recently, organic synthesis using carbon dioxide (CO2) has been actively studied for possible countermeasures against global warming caused by greenhouse gas emissions. The recycling and reuse of CO2 can reduce the amount of CO2 in the atmosphere and avoid further damage to the earth’s ecological environment. For example, many kinds of methods are advanced to prepare useful chemicals from CO2, where these useful chemicals include ethanol, methanol and carboxylic acid. The introduction of catalysts in CO2-based chemical synthesis can speed up the reaction and also tune the product species. The transition metal complexes have become essential in those synthesis reactions. As catalysts, they have a broad prospect because it is cheap in price, vast in reserves and less harmful to the environment. This research summarizes the function of copper-based catalysts in synthesis of different compounds, such as ethanol, methanol and carboxylic acid. The use of these catalysts is expected to provide a new idea for subsequent CO2-based organic synthesis.

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 score0.217
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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