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Record W4387674366 · doi:10.1038/s41598-023-44960-4

Enhancing CO2 adsorption capacity of ZIF-8 by synergetic effect of high pressure and temperature

2023· article· en· W4387674366 on OpenAlexafffund
Shan Jiang, Jingyan Liu, Jiwen Guan, Xin Du, Shoushun Chen, Yang Song, Yining Huang

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZeolitic imidazolate frameworkAdsorptionImidazolateMetal-organic frameworkMaterials scienceChemical engineeringFourier transform infrared spectroscopyDiffusionPorosityAmbient pressureHigh pressureChemistryThermodynamicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Metal–organic frameworks (MOFs) and zeolitic imidazolate frameworks (ZIFs) are promising porous materials for adsorption and storage of greenhouse gases, especially CO 2 . In this study, guided by the CO 2 phase diagram, we explore the adsorption behavior of solid CO 2 loaded with ZIF-8 framework by heating the sample under high pressures, resulting in a drastic improvement in the CO 2 uptake. The behavior of CO 2 under simultaneous high temperature (T) and pressure (P) conditions is directly monitored by in situ FTIR spectroscopy. The remarkable enhancement in CO 2 adsorption capability observed can be attributed to the synergetic effect of high T and P: high temperature greatly enhances the transport property of solid CO 2 by facilitating its diffusion into the framework; high pressure effectively modifies the pore size and shape via changing the linker orientation and creating new adsorption sites within ZIF-8. Our study thus provides important new insights into the tunability and enhancement of CO 2 adsorptive capability in MOFs/ZIFs using pressure and temperature combined as a synergetic approach.

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)0.001
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 score0.004
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations26
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207