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Record W2406119977

Synthesis and Characterization of Zinc Oxide Nanoparticles for Antibacterial Applications

2016· article· en· W2406119977 on OpenAlexvenueno aff
S. Naseem Shah, Shujaat Ali, Sajid Ali, Muhammad Naeem, Yasmeen Bibi, Sharafat Ali, Syed Masood Raza, Yousuf Khan, Sikander Khan Sherwani

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2016
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
Fundersnot available
KeywordsZincCrystalliteScanning electron microscopeNanoparticleMaterials scienceAntibacterial activityNuclear chemistryAbsorption edgeEnergy-dispersive X-ray spectroscopySpectroscopyAbsorption spectroscopyBand gapPrecipitationAbsorption (acoustics)Chemical engineeringNanotechnologyAnalytical Chemistry (journal)ChemistryBacteriaOrganic chemistryOpticsMetallurgyComposite materialOptoelectronicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

ZnO nanoparticles are synthesized for antibacterial applications by a simple co-precipitation method. X-ray diffraction (XRD) reveals that the synthesized ZnO has hexagonal crystal structure with mean crystallite size of 29 nm. Scanning electron microscopy (SEM) and Energy dispersive x-ray spectroscopy (EDX) shows pure ZnO with uniform morphology. UV–VIS absorption spectroscopy yield an absorption edge in the range 300-400 nm which corresponds to a band gap energy of 3.50 eV. Antibacterial activity of ZnO nanoparticles is tested against gram positive and gram negative bacteria by using agar-well method. These ZnO nanoparticles are found to be strongly antimicrobial as they effectively prevent the growth of many test microorganisms with a small minimum inhibitory concentrations (MIC) ~ 80 – 280 µg/ml

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.247
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2016
Admission routes1
Has abstractyes

Explore more

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