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Record W3112825666 · doi:10.1002/anie.202013867

Lanthanide‐Based Molecular Cluster‐Aggregates: Optical Barcoding and White‐Light Emission with Nanosized {Ln<sub>20</sub>} Compounds

2020· article· en· W3112825666 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.

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCluster (spacecraft)LanthanideCounterfeitNanoparticleMaterials scienceWhite lightLuminescenceComposition (language)NanotechnologyChemistryOptoelectronicsComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Counterfeit goods represent a major problem to companies, governments, and customers, affecting the global economy. In order to protect the authenticity of products and documents, optical anti‐counterfeit technologies have widely been employed via the use of discrete molecular species, extended metal–organic frameworks (MOFs), and nanoparticles. Herein, for the first time we demonstrate the potential use of molecular cluster‐aggregates (MCA) as optical barcodes via composition and energy transfer control. The tuneable optical properties for the [Ln 20 (chp) 30 (CO 3 ) 12 (NO 3 ) 6 (H 2 O) 6 ], where chp − =deprotonated 6‐chloro‐2‐pyridinol, allow the fine control of the emission colour output, resulting in high‐security level optical labelling with a precise read‐out. Moreover, a unique tri‐doped composition of Gd III , Tb III , and Eu III led to MCAs with white‐light emission. The presented methodology is a unique approach to probe the effect of composition control on the luminescent properties of nanosized molecular material.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.040
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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