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Record W2980497300 · doi:10.1002/anie.201912654

A Supramolecular Artificial Light‐Harvesting System with Two‐Step Sequential Energy Transfer for Photochemical Catalysis

2019· article· en· W2980497300 on OpenAlexaff
Min Hao, Guangping Sun, Minzan Zuo, Zuqiang Xu, Yuan Chen, Xiao‐Yu Hu, Leyong Wang

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsSupramolecular chemistryPhotochemistryNanoreactorArtificial photosynthesisAcceptorFluorescenceChemistryCatalysisPhotocatalysisOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

An artificial light-harvesting system with sequential energy-transfer process was fabricated based on a supramolecular strategy. Self-assembled from the host-guest complex formed by water-soluble pillar[5]arene (WP5), a bola-type tetraphenylethylene-functionalized dialkyl ammonium derivative (TPEDA), and two fluorescent dyes, Eosin Y (ESY) and Nile Red (NiR), the supramolecular vesicles achieve efficient energy transfer from the AIE guest TPEDA to ESY. ESY can function as a relay to further transfer the energy to the second acceptor NiR and realize a two-step sequential energy-transfer process with good efficiency. By tuning the donor/acceptor ratio, bright white light emission can be successfully achieved with a CIE coordinate of (0.33, 0.33). To better mimic natural photosynthesis and make full use of the harvested energy, the WP5⊃TPEDA-ESY-NiR system can be utilized as a nanoreactor: photocatalyzed dehalogenation of α-bromoacetophenone was realized with 96 % yield in aqueous medium.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.237
Teacher spread0.226 · 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 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

Citations320
Published2019
Admission routes1
Has abstractyes

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