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Record W1981397849 · doi:10.1021/jp066357s

Iodination of Gas-Phase-Generated Ag Nanoparticles:  Behavior of the Two Spin Orbit Components of the AgI Exciton in Ag@AgI Core−Shell Nanoparticles

2006· article· en· W1981397849 on OpenAlexaff
David Bue Pedersen, Shiliang Wang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2006
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsNanoparticleExcitonShell (structure)Materials scienceParticle (ecology)Phase (matter)NanotechnologyMolecular physicsChemistryCondensed matter physicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Ag nanoparticles, with diameters estimated to be 17 ± 6 nm, were observed to react readily with I 2 to produce Ag@AgI core−shell nanoparticles. Both spin−orbit components of the exciton associated with the AgI shell were observed in the UV−vis spectra. The band gap of the AgI shell was found to shift to lower energy as the reaction with I 2 proceeded and the shell thickness increased. Shell thickness can be varied by controlling the duration of the exposure of the Ag nanoparticles to iodine. The band gap observed for the Ag@AgI nanoparticles was comparable to that expected of a solid AgI nanoparticle with a diameter of 5−6 nm, as estimated using the Brus formula. The difference between this effective diameter and the actual diameter of the particle (17 ± 6 nm) may be attributable to confinement effects associated with the finite thickness of the AgI shell and/or effects of the Ag core on the AgI shell. The shorter wavelength exciton appeared to be much more sensitive to surface effects, which are pronounced in nanoparticles, and appeared anywhere from 312 to 355 nm. The longer wavelength exciton behaved differently and does not appear to be affected much by such surface effects and therefore seems to be a much more localized electron−hole pair.

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 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 score0.006
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.275
Teacher spread0.245 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

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