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Record W4386608896 · doi:10.1021/acsami.3c10648

Composition Control in Molecular Cluster-Aggregates: A Toolbox for Optical Output Tunability <i>via</i> Energy Transfer Pathways

2023· article· en· W4386608896 on OpenAlexafffund
Claudia Manuela Santos Calado, Diogo A. Gálico, Muralee Murugesu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceCluster (spacecraft)LanthanideNanoparticleLuminescenceChemical physicsEnergy transferNanotechnologyIonOptoelectronicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Composition control is a powerful tool for obtaining high-performance lanthanide (Ln) luminescent materials with adjustable optical outputs. This strategy is well-established for hierarchically structured nanoparticles, but it is rarely applied to molecular compounds due to the limited number of metal centers within a single unit. In this work, we present a series of molecular cluster-aggregates (MCAs) with an icosanuclear core {Ln 2 Eu 2 Tb 16 } (Ln = Ce, Pr, Nd, Sm, Gd, Dy, Ho, Er, Tm, and Yb) in which we explore composition control, akin to nanoparticles, to modulate the optical output. More specifically, we target to understand how the presence of a third Ln III doping ion would impact the well-known Tb III → Eu III energy transfer and the ratiometric optical thermometry performance based on the Tb III /Eu III pair. Photophysical properties at room and at varying temperatures were investigated. Based on experimental data and well-established intrinsic features, such as spin–orbit coupling strength and Ln III 4 f energy levels’ structure, we discuss the possible luminescent processes present in each MCA and provide insight into qualitative trends that can be rationally correlated throughout the series.

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 categoriesMeta-epidemiology (narrow)
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 score0.038
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 communication0.0000.000
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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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