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Record W2019317982 · doi:10.1039/c4tb01161a

Water-soluble photoluminescent <scp>d</scp>-mannose and <scp>l</scp>-alanine functionalized silicon nanocrystals and their application to cancer cell imaging

2014· article· en· W2019317982 on OpenAlexafffund
Yi Zhai, Mita Dasog, Ryan B. Snitynsky, Tapas K. Purkait, Maryam Aghajamali, Allison H. Hahn, Christopher B. Sturdy, Todd L. Lowary, Jonathan G. C. Veinot

Bibliographic record

VenueJournal of Materials Chemistry B · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsNational Institute for NanotechnologyAlberta Glycomics CentreUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Glycomics CentreUniversity of AlbertaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsSurface modificationNanocrystalPhotoluminescenceBiomoleculeMaterials scienceNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

Herein, we report the straightforward synthesis, photoluminescent properties, and cell imaging studies of d-mannose and l-alanine functionalized silicon nanocrystals (SiNCs). Tailoring nanocrystal surface functionalization is essential to interfacing SiNCs with their environment and rendering them stable - surface modification also offers the opportunity to target specific cell types for imaging. A simple and versatile surface modification procedure was developed to tether biomolecules onto the SiNC surfaces and render them water-soluble. The presented approach is precious metal-catalyst free, straightforward, and provides carbohydrate and amino acid functionalized SiNCs. The functionalized SiNCs have been investigated by fluorescence microscopy and our results indicate that they can be internalized by MCF-7 human breast cancer cells as shown in the cell imaging studies. The obtained SiNCs were characterized using FTIR, XPS, PL, and TEM.

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 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 score0.081
Threshold uncertainty score0.719

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)0.0000.000

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 head0.004
GPT teacher head0.226
Teacher spread0.222 · 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

Citations45
Published2014
Admission routes2
Has abstractyes

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