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Record W4411742266 · doi:10.26434/chemrxiv-2025-t2d9p

Accessing the Pores and Unlocking Open Metal Sites in a Rare-Earth Cluster-Based Metal–Organic Framework

2025· preprint· en· W4411742266 on OpenAlexafffund
Hudson A. Bicalho, Clara V. Diniz, Zoey Davis, Christopher Copeman, Ashlee J. Howarth

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsRare earthCluster (spacecraft)MetalEarth (classical element)Metal-organic frameworkRare-earth elementEarth scienceMaterials scienceChemistryGeologyComputer scienceMetallurgyPhysicsPhysical chemistryAdsorption

Abstract

fetched live from OpenAlex

While post-synthetic modification (PSM) of metal–organic frameworks (MOFs) through solvent assisted ligand incorporation (SALI) has been extensively studied, particularly in Zr6-based MOFs, this PSM approach is largely overlooked in the literature on rare-earth (RE) cluster-based MOFs. In this work, we explore SALI in Y-CU-45 (CU = Concordia University), a Y6-MOF analo-gous to Zr-MOF-808 with a 6-connected Y6-node. The structural connectivity of Y-CU-45 allows for the existence of six coordi-natively unsaturated sites per cluster. We previously demonstrated that these sites are capped by modulators used in the synthesis of Y-CU-45, which partially occlude the pores of the MOF. By performing SALI on Y-CU-45 with seven different ligands: for-mate, acetate, trifluoroacetate, pivalate, benzoate, 2,3,4,5-tetrafluorobenzoate, and 2,6-bis(trifluoromethyl)benzoate, we demon-strate trends relating the acidity of the incoming ligand to the SALI efficiency. Furthermore, we show that the pores of Y-CU-45 can be made more accessible while demonstrating the potential for open metal sites in this RE cluster-based MOF.

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 categoriesScholarly communication
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.003
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 head0.043
GPT teacher head0.315
Teacher spread0.273 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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