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Record W1976569396 · doi:10.1039/c4an02150a

Rapidly prototyped multi-scale electrodes to minimize the voltage requirements for bacterial cell lysis

2015· article· en· W1976569396 on OpenAlexafffund
Christine M. Gabardo, Aaron Kwong, Leyla Soleymani

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersResearch and Innovation FoundationOntario Ministry of Research and InnovationMcMaster UniversityNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Foundation for Innovation
KeywordsLysisElectrodeScale (ratio)VoltageChemistryMaterials scienceEngineeringElectrical engineeringPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Lab-on-a-chip systems used for nucleic acid based detection of bacteria rely on bacterial lysis for the release of cellular material. Although electrical lysis devices can be miniaturized for on-chip integration and reagent-free lysis, they often suffer from high voltage requirements, and rely on the use of off-chip voltage supplies. To overcome this barrier, we developed a rapid prototyping method for creating multi-scale electrodes that are structurally tuned for lowering the voltage needed for electrical bacterial lysis. These three-dimensional multi-scale electrodes – with micron scale reaction areas and nanoscale features – are fabricated using benchtop methods including craft cutting, polymer-induced wrinkling, and electrodeposition, which enable a lysis device to be designed, fabricated, and optimized in a matter of hours. These tunable electrodes show superior behaviour compared to lithographically-prepared electrodes in terms of lysis efficiency and voltage requirement. Successful extraction of nucleic acids from bacterial samples processed by these electrodes demonstrates the potential for these rapidly prototyped devices to be integrated within practical lab-on-a-chip systems.

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)0.001
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 score0.257
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.311
Teacher spread0.278 · 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

Citations25
Published2015
Admission routes2
Has abstractyes

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