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Record W4316669156 · doi:10.1002/anie.202215985

Metal Organic Framework Cubosomes

2023· article· en· W4316669156 on OpenAlexaff
Chen Li, Yi Pan, Tianyu Xiao, Luoxing Xiang, Qian Li, Feng Tian, Ian Manners, Yiyong Mai

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of Victoria
FundersShanghai Jiao Tong UniversityNational Natural Science Foundation of ChinaProgram of Shanghai Academic Research LeaderSalt Science Research Foundation
KeywordsKineticsChemical engineeringBovine serum albuminMaterials scienceMesoporous materialChemistryNanotechnologyChromatographyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We demonstrate a general strategy for the synthesis of ordered bicontinuous‐structured metal organic frameworks (MOFs) by using polymer cubosomes (PCs) with a double primitive structure (Im m symmetry) as the template. The filling of MOF precursors in the open channel of PCs, followed by their coordination and removal of the template, generates MOF cubosomes with a single primitive topology (Pm m) and average mesopore diameters of 60–65 nm. Mechanism study reveals that the formation of ZIF‐8 cubosomes undergoes a new MOF growth process, which involves the formation of individual MOF seeds in the template, their growth and eventual fusion into the cubosomes. Their growth kinetics follows the Avrami equation with an Avrami exponent of n=3 and a growth rate of k=1.33×10−4, indicating their fast 3D heterogeneous growth mode. Serving as a bioreactor, the ZIF‐8 cubosomes show high loading of trypsin enzyme, leading to a high catalytic activity in the proteolysis of bovine serum albumin.

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 score0.002
Threshold uncertainty score0.008

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.270
Teacher spread0.250 · 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

Citations37
Published2023
Admission routes1
Has abstractyes

Explore more

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