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Record W4200535306 · doi:10.1126/science.abi7281

A scalable metal-organic framework as a durable physisorbent for carbon dioxide capture

2021· article· en· W4200535306 on OpenAlexaff
Jian‐Bin Lin, Tai Nguyen, Ramanathan Vaidhyanathan, Jake Burner, Jared M. Taylor, Hana Durekova, Farid Akhtar, Roger K. Mah, Omid Ghaffari Nik, Stefan Marx, Nicholas Fylstra, S.S. Iremonger, Karl W. Dawson, Partha Sarkar, Pierre Hovington, Arvind Rajendran, Tom K. Woo, George K. H. Shimizu

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of OttawaUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMetal-organic frameworkCarbon dioxideSorptionPhysisorptionDurabilityRelative humidityChemical engineeringMaterials scienceCarbon fibersComposite numberEnvironmental scienceChemistryAdsorptionComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

A hydrophobic CO 2 physisorbent Most materials for carbon dioxide (CO 2 ) capture of fossil fuel combustion, such as amines, rely on strong chemisorption interactions that are highly selective but can incur a large energy penalty to release CO 2 . Lin et al . show that a zinc-based metal organic framework material can physisorb CO 2 and incurs a lower regeneration penalty. Its binding site at the center of the pores precludes the formation of hydrogen-bonding networks between water molecules. This durable material can preferentially adsorb CO2 at 40% relative humidity and maintains its performance under flue gas conditions of 150°C. —PDS

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)0.000
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
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.013
GPT teacher head0.253
Teacher spread0.241 · 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

Citations978
Published2021
Admission routes1
Has abstractyes

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