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Record W2088863665 · doi:10.1111/php.12176

Amplified Production of Singlet Oxygen in Aqueous Solution Using Metal Enhancement Effects

2013· article· en· W2088863665 on OpenAlexafffund
Sara M. Mooi, Belinda Heyne

Bibliographic record

VenuePhotochemistry and Photobiology · 2013
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSinglet oxygenRose bengalPhotosensitizerCovalent bondNanoparticlePhotochemistryMetalSilver nanoparticlePhotodynamic therapyMaterials scienceNanotechnologyAqueous solutionChemistryOxygenOrganic chemistry

Abstract

fetched live from OpenAlex

Studies involving metal enhancement effects have gained popularity, and enhancement of fluorescence due to the close proximity of a dye molecule to a metal nanoparticle is well documented. Although enhancement of singlet oxygen production by metal has been reported, studies are relatively scarce and so far only stationary silver island films have been proven to be adequate to do so. Herein, we describe the synthesis and characterization of core-shell nanoparticles on which a photosensitizer acting as source of singlet oxygen has been covalently attached to the nanoparticle surface. As a proof of concept, silver nanoparticles with a diameter around 68 nm were chosen as the metallic core, and were coated by a silica shell of about 22 nm in thickness. The silica shell plays a dual role as a spacer and a medium onto which the photosensitizer, rose bengal (RB), has been covalently attached. These novel core-shell nanoparticles allow for the amplification of singlet oxygen production by 3.8 times, which is similar to the amplification found for RB in proximity of silver island films.

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 score0.003
Threshold uncertainty score0.814

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 head0.262
Teacher spread0.247 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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