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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 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

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.

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)0.002
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 score0.051
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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