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Record W2669795922 · doi:10.1002/cjce.22926

Folate functionalized silicon nanowires with highly enhanced adhesion to cancer cells

2017· article· en· W2669795922 on OpenAlexvenueno aff
Saeid Shadmani, Zeinab Salehi, Hamid Doosthosseini, S. Mohajerzadeh, Sahar Roozbahani

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSurface modificationSilanizationTriethoxysilaneMaterials scienceFourier transform infrared spectroscopyChemical engineeringNanotechnologyAdhesionTransmission electron microscopyAttenuated total reflectionNanowireComposite material

Abstract

fetched live from OpenAlex

Abstract In this study, we implemented a functionalization process on the surface of silicon nanowires (SiNWs) which were fabricated by chemical vapour deposition (CVD) method in a low‐pressure CVD (LPCVD) chamber. Surface functionalization was carried out in three stages: hydroxylation of the surface with piranha solution, silanization with a solution of 3‐Aminopropyl triethoxysilane (APTES) in n ‐hexane, and finally functionalization with folic acid as a detection agent. Optical spectroscopy and electron microscopy techniques were used to experimentally characterize materials. Surface functionalization was evaluated using Attenuated Total Reflectance Fourier‐Transform Infrared spectroscopy (FTIR‐ATR), Field Emission Scanning Electron Miscoscopy (FESEM), and Transmission Electron Microscopy (TEM). Cell adhesion to functionalized and non‐functionalized silicon nanowires was compared; a cell assay analysis on lung metastatic cells was conducted. Lung cancer metastatic QUDB cells were seeded on a SiNWs surface in RPMI1640 complete medium. To evaluate the adhesion of cancer cells to these surfaces FESEM imaging was used. The number of cancer cells trapped on the folic acid functionalized SiNWs was found to be approximately 3 times higher than that of the non‐functionalized ones.

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 score0.037
Threshold uncertainty score0.386

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 head0.008
GPT teacher head0.201
Teacher spread0.192 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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