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Record W1964720946 · doi:10.1002/smll.200700155

Nanorod Heterostructures Showing Photoinduced Charge Separation

2007· article· en· W1964720946 on OpenAlexafffund
Sandeep Kumar, Marcus Jones, Shun S. Lo, Gregory D. Scholes

Bibliographic record

VenueSmall · 2007
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeterojunctionNanorodMaterials sciencePhotoinduced charge separationNanocrystalPhotoluminescenceSemiconductorBand gapNanotechnologyOptoelectronicsCharge carrierNanoscopic scaleCharge (physics)PhotocatalysisArtificial photosynthesisChemistryPhysics

Abstract

fetched live from OpenAlex

Size- and shape-dependent property modifications of semiconductor nanocrystals have been a subject of intense interest because of their potential for future engineering devices. The bandgap and related optical-property tuning of these materials are mainly governed by the nature of their band edges. In addition, fusing one type of nanocrystal over another enables further control of material properties that are dependent on the relative alignments of their energy levels. On a molecular scale, the synthesis of supramolecular compounds has inspired advances in theories for photoinduced charge transfer. Heterostructured nanocrystals potentially provide a nanoscale analog of such systems. A method for preparing heterostructured nanocrystals of complex morphologies showing photoinduced charge separation is presented. It is shown that the energy and lifetime of the charge-transfer photoluminescence band can be tuned by changing the relative alignment of band edges in CdSe/CdTe heterostructure nanorods. The long-lived charge transfer states in these type II semiconductors may make them attractive for photovoltaic applications.

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 score0.004

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 head0.043
GPT teacher head0.271
Teacher spread0.228 · 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

Citations199
Published2007
Admission routes2
Has abstractyes

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