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Record W1850460876 · doi:10.1039/c5nr04942c

Nanoantioxidant-driven plasmon enhanced proton-coupled electron transfer

2015· article· en· W1850460876 on OpenAlexaff
Georgios A. Sotiriou, Christoph O. Blattmann, Yiannis Deligiannakis

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPorphyrin and Phthalocyanine Chemistry
Canadian institutionsInstitute of Particle Physics
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPlasmonProton-coupled electron transferElectron transferNanoparticleChemical physicsMaterials sciencePhotochemistryProtonSurface plasmon resonanceMoleculeNanotechnologyChemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Proton-coupled electron transfer (PCET) reactions involve the transfer of a proton and an electron and play an important role in a number of chemical and biological processes. Here, we describe a novel phenomenon, plasmon-enhanced PCET, which is manifested using SiO2-coated Ag nanoparticles functionalized with gallic acid (GA), a natural antioxidant molecule that can perform PCET. These GA-functionalized nanoparticles show enhanced plasmonic response at near-IR wavelengths, due to particle agglomeration caused by the GA molecules. Near-IR laser irradiation induces strong local hot-spots on the SiO2-coated Ag nanoparticles, as evidenced by surface enhanced Raman scattering (SERS). This leads to plasmon energy transfer to the grafted GA molecules that lowers the GA-OH bond dissociation enthalpy by at least 2 kcal mol(-1) and therefore facilitates PCET. The nanoparticle-driven plasmon-enhancement of PCET brings together the so far unrelated research domains of nanoplasmonics and electron/proton translocation with significant impact on applications based on interfacial electron/proton transfer.

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 score0.009
Threshold uncertainty score0.788

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)0.0010.001

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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