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Record W2599552753 · doi:10.1021/acsami.6b15989

Magnetic Printing of a Biosensor: Inexpensive Rapid Sensing To Detect Picomolar Amounts of Antigen with Antibody-Functionalized Carbon Nanotubes

2017· article· en· W2599552753 on OpenAlexafffundabout
Abdel Rahman Abdel Fattah, Ahmed M. Abdalla, Sarah Mishriki, Elvira Meleca, Fei Geng, Suvojit Ghosh, Ishwar K. Puri

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBiosensorMaterials scienceCarbon nanotubeNanotechnologyMagnetic nanoparticlesCurrent (fluid)NanoparticleSurface modificationChemical engineering

Abstract

fetched live from OpenAlex

When an antibody (Ab) is immobilized on its surface, a carbon nanotube (CNT) becomes a biosensor that detects the corresponding antigen (Ag) because Ag-Ab complexes formed on the CNT surface moderate the current flow through it. We synthesized a biological ink containing CNTs that are twice functionalized, first with magnetic nanoparticles and thereafter with the anti-c-Myc monoclonal Ab. The ink is pipetted and dynamically self-organized by an external magnetic field into a dense electrically conducting sensor strip that measures the decrease in current when a sample containing c-Myc Ag is deposited on it. Prototypes are rapidly fabricated materials that cost less than 20 cents (Canadian) for each sensor. With larger current decreases due to real-time specific Ag-Ab binding for higher c-Myc concentrations, the biosensor distinguishes between picomolar c-Myc concentrations within a minute, offering proof of concept of a simple, rapid, economical, and sensitive method to detect specific molecules recognizable by Abs.

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 score0.005
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.225
Teacher spread0.215 · 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

Citations19
Published2017
Admission routes3
Has abstractyes

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