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Record W2095047104 · doi:10.1002/smll.200600558

Evolutionary Shape Control During Colloidal Quantum‐Dot Growth

2007· article· en· W2095047104 on OpenAlex
P. Sreekumari Nair, Karolina P. Fritz, Gregory D. Scholes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSmall · 2007
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan Foundation
KeywordsNanocrystalColloidNanocrystalline materialNanotechnologyMaterials scienceQuantum dotNanometreShape changeReagentChemical physicsChemistryBiophysics

Abstract

fetched live from OpenAlex

Size-dependent optical properties of semiconductor nanocrystals are of great interest because of the myriad of phenomena stemming from them. The preparation of more complex colloidal shapes will facilitate the systematic study of shape-dependent phenomena. It is shown that a strategy to obtain systematically more complex nanocrystal structures is to exert a sequence of shape-directing steps during the colloidal growth. Using experiments based on multiple reagent injections we show how changes in the type of surfactant introduced during growth of CdSe nanocrystals promotes shape evolution. On this basis, we propose a means to achieve a further generation of shape design in nanometer-sized colloids by using a series of growth steps, each one building from the previous conditions of shape as well as surface-specific reactivity. To understand the shape formation and stability in nanocrystalline colloids, and particularly the importance of surface ligands, we introduce an analogy with the thermodynamics of droplets.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.198
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.019
GPT teacher head0.215
Teacher spread0.197 · 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