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Record W4400121989 · doi:10.3390/appliedchem4030016

Polymeric and Crystalline Materials for Effective and Sustainable CO2 Capture

2024· article· en· W4400121989 on OpenAlexaff
David Gendron, M. Ya. Zakharova

Bibliographic record

VenueAppliedChem · 2024
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsCegep de Thetford
Fundersnot available
KeywordsPolymerGreenhouse gasCarbon capture and storage (timeline)Materials scienceCarbon dioxideIonic liquidEnvironmental scienceNanotechnologyProcess engineeringSustainable energyBiochemical engineeringClimate changeChemistryEngineeringOrganic chemistryRenewable energy

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) is recognized as the primary cause of global warming due to its greenhouse potential. It plays a significant role in contributing to the emissions arising from a variety of anthropogenic activities, such as energy production, transportation, the construction industry, and other industrial processes. Capturing and utilizing CO2 to mitigate its impact on the environment is, therefore, of significant importance. To do so, strategies such as net-zero strategies, deploying capture and storage technologies, and converting CO2 into useful products have been proposed. In this review, we focused our attention on the preparation and performance of polymeric and crystalline materials for efficient CO2 capture. More precisely, we examined MOFs, petroleum-based polymers (amine-based, polymeric ionic liquid, ionic polymer, conjugated macro/micro-cyclic polymer, and porous organic polymer) as well as bio-based polymers for CO2 capture. In brief, the present work aims to guide the reader on the available crafted polymeric and crystalline materials offering a promising avenue towards innovative carbon dioxide capture strategy.

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 score0.057
Threshold uncertainty score0.588

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)0.0000.000

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 head0.003
GPT teacher head0.190
Teacher spread0.188 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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