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Record W2577886222 · doi:10.1021/jacs.6b10455

Ultralow Parasitic Energy for Postcombustion CO<sub>2</sub> Capture Realized in a Nickel Isonicotinate Metal–Organic Framework with Excellent Moisture Stability

2017· article· en· W2577886222 on OpenAlexafffund
Shyamapada Nandi, Sean P. Collins, Debanjan Chakraborty, Debasis Banerjee, Praveen K. Thallapally, Tom K. Woo, Ramanathan Vaidhyanathan

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaIndian Institute of Science Education and Research PuneMinistry of Education, India
KeywordsChemistryMetal-organic frameworkAdsorptionNickelMoistureWork (physics)Water vaporMetalChemical engineeringThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Metal–organic frameworks (MOFs) have attracted significant attention as solid sorbents in gas separation processes for low-energy postcombustion CO 2 capture. The parasitic energy (PE) has been put forward as a holistic parameter that measures how energy efficient (and therefore cost-effective) the CO 2 capture process will be using the material. In this work, we present a nickel isonicotinate based ultramicroporous MOF, 1 [Ni-(4PyC) 2 ·DMF], that has the lowest PE for postcombustion CO 2 capture reported to date. We calculate a PE of 655 kJ/kg CO 2, which is lower than that of the best performing material previously reported, Mg-MOF-74. Further, 1 exhibits exceptional hydrolytic stability with the CO 2 adsorption isotherm being unchanged following 7 days of steam-treatment (>85% RH) or 6 months of exposure to the atmosphere. The diffusion coefficient of CO 2 in 1 is also 2 orders of magnitude higher than in zeolites currently used in industrial scrubbers. Breakthrough experiments show that 1 only loses 7% of its maximum CO 2 capacity under humid conditions.

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)0.001
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 score0.006
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations161
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207