MétaCan
Menu
Back to cohort
Record W2324205549 · doi:10.1021/ac4024654

Parts-per-Million of Polyethylene Glycol as a Non-Interfering Blocking Agent for Homogeneous Biosensor Development

2013· article· en· W2324205549 on OpenAlexaff
Biwu Liu, Po‐Jung Jimmy Huang, Xu Zhang, Feng Wang, Rachel Pautler, Alexander C. F. Ip, Juewen Liu

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryPolyethylene glycolPEG ratioBiosensorPolymerPolystyreneAdsorptionEthylene glycolMembraneHomogeneousCovalent bondChemical engineeringCombinatorial chemistryNanotechnologyChromatographyOrganic chemistryBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Many homogeneous assays are complicated by the adsorption of probe molecules by the surface of reaction vessels, which are often made of polypropylene or polystyrene-based plastics. To solve this problem, many protein and surfactant-based blocking agents are used. However, these blockers may interfere with intended assays by sequestering transition-metal ions, inducing protein denaturing, generating air bubbles or making pores in membranes. Coating surfaces with polyethylene glycol (PEG) through covalent linkages has been proven to be an effective method to minimize protein adsorption. However, this method is more difficult to apply on plastic surfaces and is quite expensive. While unmodified PEG is often considered as a nonadsorbing polymer, in this Technical Note, we report that PEG at very low concentration (ppm level) can still effectively block plastic surfaces. This method works for DNA, protein, and liposome-based assays as long as the molecular weight of PEG is greater than 2000. PEG works because of multivalent hydrophobic interaction from its repeating methylene units. This Technical Note will not only facilitate biosensor development, but also enhance our understanding of the interaction between various molecules and plastic surfaces.

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 score0.008
Threshold uncertainty score0.866

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 head0.271
Teacher spread0.260 · 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

Citations54
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207