MétaCan
Menu
Back to cohort
Record W2512190249 · doi:10.1039/c6lc00867d

Fabrication of polydimethylsiloxane (PDMS) nanofluidic chips with controllable channel size and spacing

2016· article· en· W2512190249 on OpenAlexafffund
Ran Peng, Dongqing Li

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsPolydimethylsiloxaneFabricationMaterials scienceNanotechnologyChannel (broadcasting)OptoelectronicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The ability to create reproducible and inexpensive nanofluidic chips is essential to the fundamental research and applications of nanofluidics. This paper presents a novel and cost-effective method for fabricating a single nanochannel or multiple nanochannels in PDMS chips with controllable channel size and spacing. Single nanocracks or nanocrack arrays, positioned by artificial defects, are first generated on a polystyrene surface with controllable size and spacing by a solvent-induced method. Two sets of optimal working parameters are developed to replicate the nanocracks onto the polymer layers to form the nanochannel molds. The nanochannel molds are used to make the bi-layer PDMS microchannel-nanochannel chips by simple soft lithography. An alignment system is developed for bonding the nanofluidic chips under an optical microscope. Using this method, high quality PDMS nanofluidic chips with a single nanochannel or multiple nanochannels of sub-100 nm width and height and centimeter length can be obtained with high repeatability.

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 score0.022
Threshold uncertainty score0.447

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 head0.007
GPT teacher head0.181
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

Citations65
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueLab on a ChipSame topicNanopore and Nanochannel Transport StudiesFrench-language works237,207