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Record W3041938400 · doi:10.1039/d0cs00292e

Rare-earth metal–organic frameworks: from structure to applications

2020· review· en· W3041938400 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemical Society Reviews · 2020
Typereview
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsRare earthMetal-organic frameworkAstrobiologyNanotechnologyMaterials scienceChemistryEarth scienceGeologyPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

In the past 30 years, metal-organic frameworks (MOFs) have garnered widespread attention owing to their diverse chemical structures, and tunable properties. As a result, MOFs are of interest for a wide variety of potential applications spanning multiple scientific and engineering disciplines. MOFs have been synthesized using several elements from the periodic table, including those with metal nodes containing s-, p-, d-, and f-block elements. MOFs synthesized with rare-earth (RE) elements, which include scandium, yttrium and the series of fifteen lanthanides are an intriguing family of MOFs from the standpoint of both structure and function. While RE-MOFs can possess many of the same properties common to all MOF families (i.e., permanent porosity, tunable pore size/shape, accessible Lewis acidic sites), they can also display unique structures and properties owing to the high coordination numbers and distinct optical properties of RE-elements. In this review, we present the progress, and highlight several discoveries from research conducted on the topic of RE-MOFs. First, diverse structures of RE-MOFs are presented, divided into classes based on the composition of the RE-metal node being RE(iii)-ions, RE(iii)-chains, or RE(iii)-clusters. Then, several potential applications of RE-MOFs are presented, highlighting examples in the areas of chemical sensing, white light emission, biological imaging, drug delivery, near infrared emission, catalysis, gas adsorption, and chemical separations.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0190.004

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 head0.032
GPT teacher head0.297
Teacher spread0.266 · 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