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Record W3041938400 · doi:10.1039/d0cs00292e

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

2020· review· en· W3041938400 on OpenAlexafffund
Felix Saraci, Victor Quezada‐Novoa, P. Rafael Donnarumma, Ashlee J. Howarth

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.032
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

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 designNot applicable
Domainnot available
GenreReview

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

Citations443
Published2020
Admission routes2
Has abstractyes

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Same venueChemical Society ReviewsSame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207