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Record W6946057769 · doi:10.25545/pxbzkn

Regenerable Luminescent Triarylborane-Functionalized Rare-Earth Metal-Organic Frameworks as Solid-State Small Molecule Sensors

2025· dataset· en· W6946057769 on OpenAlexaff

Bibliographic record

VenueUNB Dataverse · 2025
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLuminescenceLanthanideEuropiumSmall moleculeSteric effectsLigand (biochemistry)Rational designFluorideMolecule

Abstract

fetched live from OpenAlex

Abstract: Fluoride anions (F-) are commonly found in everyday items and known to have positive medicinal uses. Despite their importance, overconsumption can lead to dental and skeletal fluorosis, amongst other health issues. In pursuit of a more effective method to detect trace amounts of anions in aqueous media, we synthesized two triarylborane-functionalized lanthanide metal-organic frameworks (LnBMOFs) to act as solid-state luminescent sensors. The LnBMOFs, EuBMOF and TbBMOF use europium and terbium, respectively as these metal ions display strong luminescent properties. The electrophilic nature of the triarylborane ligand makes it an ideal candidate for sensing high affinity fluoride, whilst also providing steric bulk to enhance the selectivity and stability of the LnBMOFs. We further demonstrate that LnBMOFs are capable of sensing other common anions including cyanide and hydroxide, expanding the scope of these sensors, while maintaining a high degree of sensitivity. The structural design of these LnBMOFs provide a turn-on/off effect, where the luminescence is regenerated and maintains stability through several cycles upon washing with water.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4650.018

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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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 routes1
Has abstractyes

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