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Record W2091196400 · doi:10.1021/jp108391e

Controlled Co(II) Doping of Zinc Oxide Nanocrystals

2010· article· en· W2091196400 on OpenAlexaff
D. Scott Bohle, Carla J. Spina

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2010
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsWurtzite crystal structureDopantNanocrystalline materialZincMaterials scienceCobaltDopingPhotocatalysisCobalt oxideOxideChemical engineeringNanotechnologyInorganic chemistryOptoelectronicsChemistryMetallurgyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Dopants are non-native atoms commonly used to modify the properties of bulk semiconductors. In this paper we demonstrate that by controlling the addition of cobalt(II) to growing zinc oxide nanocrystals (ZnO NCs) it is possible to modulate the resulting properties. We show that the environment of cobalt may be controlled by varying the synthetic conditions, mainly through varying the time of dopant-precursor addition and concentration. These conditions prove critical to the resulting Co(II) configuration, which affects both the luminescent and photocatalytic properties of the ZnO NCs. Presynthetic doping with 2% Co(II) results in a 98% quenching of the visible emission of ZnO, whereas the same quantity doped post synthesis results in only a 60% quenching. The environment of cobalt in the ZnO wurtzite lattice is identified through UV−vis spectroscopy. The wurtzite structure of the ZnO lattice for all nanocrystalline species is confirmed through X-ray diffraction patterns obtained from a synchrotron radiation source. Postsynthetically doped Co(II) in ZnO NC is demonstrated to have potential applications as an “on−off” sensor, as exemplified with nitric oxide.

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 score0.002
Threshold uncertainty score0.005

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)0.0000.000

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 head0.253
Teacher spread0.242 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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Same venueThe Journal of Physical Chemistry CSame topicZnO doping and propertiesFrench-language works237,207