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Record W4291900859 · doi:10.1038/s41467-022-32418-6

Efficient separation of butane isomers via ZIF-8 slurry on laboratory- and pilot-scale

2022· article· en· W4291900859 on OpenAlexaff
Mingke Yang, Huishan Wang, Julian Y. Zuo, Chun Deng, Bei Liu, Liya Chai, Kun Li, Xiao Han, Peng Xiao, Xiao-Hui Wang, Wan Chen, Xiaowan Peng, Yu Han, Zixuan Huang, Baocan Dong, Chang‐Yu Sun, Guangjin Chen

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsIsobutaneButaneSorptionPetrochemicalSlurryMetal-organic frameworkChemical engineeringGas separationMaterials scienceSelectivityChemistryMembraneOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

n-butane and isobutane are important petrochemical raw materials. Their separation is challenging because of their similar properties, including boiling point. Here, we report a zeolitic imidazolate framework-8 (ZIF-8)/N,N-Dimethylpropyleneurea (DMPU)-water slurry as sorption material to separate butane mixtures. The isobutane/n-butane selectivity of ZIF-8/DMPU-water slurries is as high as 890 with high kinetic performance, which transcends the upper limit of various separation materials or membranes reported in the literature. More encouragingly, a continuous pilot separation device was established, and the test results show that the purity and recovery ratio of isobutane product are 99.46 mol% and 87%, respectively, which are superior to the corresponding performance (98.56 mol% and 54%) of the industrial distillation tower. To the best of our knowledge, the use of metal-organic frameworks (MOFs) for gas separation in pilot scale remains underexplored, and thus this work provides a step forward to the commercial application of MOFs in gas separation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.701

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.278
Teacher spread0.265 · 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

Citations50
Published2022
Admission routes1
Has abstractyes

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