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Record W2793027042 · doi:10.1002/smll.201800013

Detection of Individual Molecules and Ions by Carbon Nanotube‐Based Differential Resistive Pulse Sensor

2018· article· en· W2793027042 on OpenAlexafffund
Ran Peng, Xiaowu Tang, Dongqing Li

Bibliographic record

VenueSmall · 2018
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeMaterials scienceMoleculeResistive touchscreenNanotechnologyRhodamine BRhodamineIonFluorescenceChemistryComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents a new method of sensing single molecules and cations by a carbon nanotube (CNT)-based differential resistive pulse sensing (RPS) technique on a nanofluidic chip. A mathematical model for multichannel RPS systems is developed to evaluate the CNT-based RPS signals. Individual cations, rhodamine B dye molecules, and ssDNAs are detected successfully with high resolution and high signal-to-noise ratio. Differentiating ssDNAs with 15 and 30 nucleotides are achieved. The experimental results also show that translocation of negatively charged ssDNAs through a CNT decreases the electrical resistance of the CNT channel, while translocation of positively charged cations and rhodamine B molecules increases the electrical resistance of the CNT. The CNT-based nanofluidic device developed in this work provides a new avenue for single-molecule/ion detection and offers a potential strategy for DNA sequencing.

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 score0.012
Threshold uncertainty score0.441

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 head0.011
GPT teacher head0.194
Teacher spread0.183 · 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

Citations38
Published2018
Admission routes2
Has abstractyes

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