MétaCan
Menu
Back to cohort
Record W2312584942 · doi:10.1021/jp405732k

Interaction of Pristine and Functionalized Carbon Nanotubes with Lipid Membranes

2013· article· en· W2312584942 on OpenAlexafffund
Svetlana Baoukina, Luca Monticelli, D. Peter Tieleman

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Health SolutionsAlberta Innovates - Technology FuturesCompute Canada
KeywordsCarbon nanotubeMembraneMaterials scienceCarbon nanobudNanotechnologyFrit compressionMechanical properties of carbon nanotubesMolecular dynamicsBiological membraneCarbon fibersChemical engineeringOptical properties of carbon nanotubesNanotubeCarbon nanotube supported catalystCarbon nanofiberComposite materialChemistryComputational chemistry

Abstract

fetched live from OpenAlex

Carbon nanotubes are widely used in a growing number of applications. Their interactions with biological materials, cell membranes in particular, is of interest in applications including drug delivery and for understanding the toxicity of carbon nanotubes. We use extensive molecular dynamics simulations with the MARTINI model to study the interactions of model nanotubes of different thickness, length, and patterns of chemical modification with model membranes. In addition, we characterize the interactions of small bundles of carbon nanotubes with membrane models. Short pristine carbon nanotubes readily insert into membranes and adopt an orientation parallel to the plane of the membrane in the center of the membrane. Larger aggregates and functionalized nanotubes exhibit a range of possible interactions. The distribution and orientation of carbon nanotubes can be controlled by functionalizing the nanotubes. Free energy calculations provide thermodynamic insight into the preferred orientations of different nanotubes and quantify structural defects in the lipid matrix.

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 score0.002
Threshold uncertainty score0.202

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations71
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicLipid Membrane Structure and BehaviorFrench-language works237,207