MétaCan
Menu
Back to cohort
Record W3109706172 · doi:10.1039/d0nr06989b

Thermal properties of lipid bilayers derived from the transient heating regime of upconverting nanoparticles

2020· article· en· W3109706172 on OpenAlexafffund
Ana R. N. Bastos, Carlos D. S. Brites, Paola A. Rojas‐Gutierrez, Rute A. S. Ferreira, Ricardo L. Longo, Christine DeWolf, John A. Capobianco, Luís D. Carlos

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsConcordia University
FundersFundação para a Ciência e a TecnologiaNatural Sciences and Engineering Research Council of CanadaFundação de Amparo à Ciência e Tecnologia do Estado de PernambucoConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean Regional Development FundHorizon 2020 Framework ProgrammeFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLipid bilayerBilayerMaterials scienceNanoparticleNanoscopic scaleTransient (computer programming)NanotechnologyThermalMembraneChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Heat transfer and thermal properties at the nanoscale can be challenging to obtain experimentally. These are potentially relevant for understanding thermoregulation in cells. Experimental data from the transient heating regime in conjunction with a model based on the energy conservation enable the determination of the specific heat capacities for all components of a nanoconstruct, namely an upconverting nanoparticle and its conformal lipid bilayer coating. This approach benefits from a very simple, cost-effective and non-invasive optical setup to measure the thermal parameters at the nanoscale. The time-dependent model developed herein lays the foundation to describe the dynamics of heat transfer at the nanoscale and were used to understand the heat dissipation by lipid bilayers.

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 score0.009
Threshold uncertainty score0.338

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 head0.044
GPT teacher head0.217
Teacher spread0.173 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNanoscaleSame topicQuantum Dots Synthesis And PropertiesFrench-language works237,207