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Surface activation of graphene oxide nanosheets by ultraviolet irradiation for highly efficient anti-bacterials

2013· article· en· W1999706494 on OpenAlexaff
Murugan Veerapandian, Linghe Zhang, Karthikeyan Krishnamoorthy, Kyusik Yun

Bibliographic record

VenueNanotechnology · 2013
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceGrapheneOxideUltravioletIrradiationUltraviolet irradiationNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

A comprehensive investigation of anti-bacterial properties of graphene oxide (GO) and ultraviolet (UV) irradiated GO nanosheets was carried out. Microscopic characterization revealed that the GO nanosheet-like structures had wavy features and wrinkles or thin grooves. Fundamental surface chemical states of GO nanosheets (before and after UV irradiation) were investigated using x-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy. Minimum inhibitory concentration (MIC) results revealed that UV irradiated GO nanosheets have more pronounced anti-bacterial behavior than GO nanosheets and standard antibiotic, kanamycin. The MIC of UV irradiated GO nanosheets was 0.125 μg ml⁻¹ for Escherichia coli and Salmonella typhimurium, 0.25 μg ml⁻¹ for Bacillus subtilis and 0.5 μg ml⁻¹ for Enterococcus faecalis, ensuring its potential as an anti-infective agent for controlling the growth of pathogenic bacteria. The minimum bactericidal concentration of normal GO nanosheets was determined to be two-fold higher than its corresponding MIC value, indicating promising bactericidal activity. The mechanism of anti-bacterial action was evaluated by measuring the enzymatic activity of β-D-galactosidase for the hydrolysis of o-nitrophenol-β-D-galactopyranoside.

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 score0.010
Threshold uncertainty score0.648

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations76
Published2013
Admission routes1
Has abstractyes

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