MétaCan
Menu
Back to cohort
Record W2262488452 · doi:10.1021/acs.jpcc.5b10646

Molecular Dynamics Studies for Optimization of Noncovalent Loading of Vinblastine on Single-Walled Carbon Nanotube

2016· article· en· W2262488452 on OpenAlexafffund
Zixian Li, Tiffany Tozer, Laleh Alisaraie

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMemorial University of Newfoundland
FundersResearch and Development Corporation of Newfoundland and LabradorCompute Canada
KeywordsCarbon nanotubeMaterials scienceNanotechnologyDrug deliveryNanotubeVinblastineZigzagCarbon nanobudMolecular dynamicsCarbon fibersChemical engineeringComputational chemistryChemistryOptical properties of carbon nanotubesComposite material

Abstract

fetched live from OpenAlex

Carbon nanotubes (CNTs) have become one of the most promising candidates for transporting drugs to target sites because of their size scale, huge surface area, and high cellular uptake. Many experimental studies of carbon nanotube drug delivery have been performed in the past decade. However, interactions with one of the essential antimitotic agents—vinblastine—and carbon nanotubes have yet to be investigated. Here we present computational studies of the interactions between vinblastine and carbon nanotubes under different conditions. We studied vinblastine–carbon nanotube interactions with one to three vinblastine molecules loaded, with armchair, chiral, and zigzag tube structures, with nonfunctionalized and ester-functionalized carbon nanotubes at 277 and 300 K. Terminal esterification of carbon nanotubes strengthened the drug–carrier interactions of all systems at 300 K. The functionalized carbon nanotubes of armchair type were suitable for drug delivery at both 277 and 300 K due to the strong drug–carrier interactions. The functionalized chiral nanotubes have been shown to be especially effective for the drug transportation at 277 K due to the enhanced drug–carrier interactions at the low temperature.

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)0.001
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 score0.032
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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

Citations47
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicCarbon Nanotubes in CompositesFrench-language works237,207