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Record W2032353565 · doi:10.1039/c0an00070a

Interaction of cationic surfactants with DNA detected by spectroscopic and acoustic wave techniques

2010· article· en· W2032353565 on OpenAlexaff
Tibor Hianik, Xiaomeng Wang, Vadim N. Tashlitsky, Tatiana S. Oretskaya, Slavomíra Poniková, Marián Antalı́k, Jonathan S. Ellis, Michael Thompson

Bibliographic record

VenueThe Analyst · 2010
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPulmonary surfactantCationic polymerizationChemistryEthidium bromideAbsorption (acoustics)PyridiniumQuenching (fluorescence)Intercalation (chemistry)BromideAnalytical Chemistry (journal)MoleculeFluorescenceChlorideDNAInorganic chemistryChromatographyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Interaction of the cationic surfactants benzalkonium chloride and 1-hexadecylpyridinium chloride, in the concentration range 0.1 microM to 1 mM with calf thymus DNA and with short 19-mer double-stranded DNA has been examined in solution using UV absorption and fluorescent spectroscopies and at the liquid-solution interface by thickness-shear mode acoustic wave sensor. Higher concentrations of surfactant resulted in an increase of UV absorption, and decrease of melting temperature and van't Hoff enthalpy of calf thymus DNA. Both surfactants induce fluorescence quenching of ethidium bromide which is also associated with intercalation of the molecules into the nucleic acid strand. The effect of the pyridinium compound is greater than for the other surfactant likely because of the lower size of polar head group in this molecule. With respect to acoustic wave detection at the device surface, for relatively low surfactant concentrations (below 100 microM), decreases of both series resonant frequency and motional resistance were observed. At higher surfactant concentration both parameters increased. These effects are attributed to acoustic coupling processes that occur at the device-film/liquid boundary.

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 score0.003
Threshold uncertainty score0.348

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 head0.227
Teacher spread0.220 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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