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Record W4320032476 · doi:10.1126/sciadv.ade6975

One-dimensional alignment of defects in a flexible metal-organic framework

2023· article· en· W4320032476 on OpenAlexfundno aff
Yao Fu, Alexander C. Forse, Zhengzhong Kang, Matthew J. Cliffe, Weicheng Cao, Jinglin Yin, Lina Gao, Zhenfeng Pang, Tian He, Qinlong Chen, Qi Wang, Jeffrey R. Long, Jeffrey A. Reimer, Xueqian Kong

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceBasic Energy SciencesOffice of ScienceNational Natural Science Foundation of ChinaU.S. Department of EnergyPhilomathia FoundationChina Scholarship Council
KeywordsCrystallinityFlexibility (engineering)Materials scienceMetal-organic frameworkLattice (music)Chemical physicsPolymerNanotechnologyChemistryPhysicsAdsorptionComposite material

Abstract

fetched live from OpenAlex

Crystalline materials are often considered to have rigid periodic lattices, while soft materials are associated with flexibility and nonperiodicity. The continuous evolution of metal-organic frameworks (MOFs) has erased the boundaries between these two distinct conceptions. Flexibility, disorder, and defects have been found to be abundant in MOF materials with imperfect crystallinity, and their intricate interplay is poorly understood because of the limited strategies for characterizing disordered structures. Here, we apply advanced nuclear magnetic resonance spectroscopy to elucidate the mesoscale structures in a defective MOF with a semicrystalline lattice. We show that engineered defects can tune the degree of lattice flexibility by combining both ordered and disordered compartments. The one-dimensional alignment of correlated defects is the key for the reversible topological transition. The unique matrix is featured with both rigid framework of nanoporosity and flexible linkage of high swellability.

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)0.001
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 score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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.

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

Citations21
Published2023
Admission routes1
Has abstractyes

Explore more

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