MétaCan
Menu
Back to cohort
Record W2007008488 · doi:10.1142/s179360471330003x

SYNTHESIS, PHASE TRANSFER AND SURFACE MODIFICATION OF HYDROPHOBIC QUANTUM DOTS FOR BIOAPPLICATIONS

2013· article· en· W2007008488 on OpenAlexaff
Ruili Zhang, Xiao Zhang, Xiaoyu Li, Ping Yang

Bibliographic record

VenueFunctional Materials Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsTrinity CollegeUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceUniversity of JinanNational Natural Science Foundation of China
KeywordsMaterials scienceSilanizationQuantum dotSurface modificationPhotoluminescenceNanotechnologyPhase (matter)CoatingChemical engineeringPolymerNanoparticleOptoelectronicsComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

We review the preparation, phase transfer, surface modification and possible bioapplications of hydrophobic CdSe based quantum dots (QDs). CdSe cores with rod and spherical morphologies were prepared through adjusting preparation conditions. The photoluminescence (PL) of the QDs depended strongly on preparation conditions. The QDs were coated with semiconductor shells to improve their PL properties. Anisotropic growth occurred during shell coating. Core/shell QDs revealed tunable PL and high PL efficiencies up to 90%. The phase transfer of QDs from oil phase to water phase was carried out via polymer or a sol–gel process. The silanization of the QDs plays an important role for the sol–gel process. Because of a SiO 2 coating, the surface modification of the QDs for bioapplications became easy. After transferring into water phase, the QDs still retained high PL efficiency. Because of their high PL, these biofunctional materials could provide a platform for various 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 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 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 score0.008
Threshold uncertainty score0.999

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)0.0020.000

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 head0.035
GPT teacher head0.244
Teacher spread0.209 · 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.

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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueFunctional Materials LettersSame topicQuantum Dots Synthesis And PropertiesFrench-language works237,207