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Record W4312115324 · doi:10.1039/d2sc05915k

Novel lanthanide nanoparticle frameworks for highly efficient photoluminescence and hypersensitive detection

2022· article· en· W4312115324 on OpenAlexaff
Yuan-Jun Tong, Lu-Dan Yu, Nan Li, Minhui Shen, Xiaoru Peng, Huangsheng Yang, Yu‐Xin Ye, Fang Zhu, Janusz Pawliszyn, Jianqiao Xu, Gangfeng Ouyang

Bibliographic record

VenueChemical Science · 2022
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersSpecial Project for Research and Development in Key areas of Guangdong ProvinceFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaGuangzhou Municipal Science and Technology ProjectNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsLanthanideLuminescenceUranylPhotoluminescenceNanoparticleCovalent bondMaterials scienceLigand (biochemistry)NanotechnologyFluorescenceChemical engineeringIonChemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

assembly of luminescent LnCs. The confined LnCs are termed nanoparticles (NPs) to distinguish them from the pure LnCs. Secondary micropores with predictable sizes are successfully formed between the walls of the nanochannels and the orderly aligned NPs therein. By using a small organic ligand that can efficiently sensitize Ln(iii) cations in the assembly processes, the obtained composites show high quantum yields above 20%. The fluorescence can even be effectively maintained across nine pH units. The secondary micropores further enable the unambiguous discrimination of six methinehalides and ultrasensitive detection of uranyl ions. This study provides a new type of luminescent material that has potential for sensing and light emitting.

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 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 score0.027
Threshold uncertainty score0.443

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 studies0.0010.000
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2022
Admission routes1
Has abstractyes

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