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Record W4413425508 · doi:10.1093/oxfmat/itaf016

Systematic control of silver nanoparticle size and shape for enhanced biocompatibility and antibacterial activity against <i>Staphylococcus aureus</i> and <i>Escherichia coli</i>

2025· article· en· W4413425508 on OpenAlexaff
Gabriela Carreño, Solange Piñero, M Arias Delgado, Luz S. Quintero, Jorge A. Gutiérrez, Sergio Blanco

Bibliographic record

VenueOxford Open Materials Science · 2025
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of British Columbia
FundersUniversidad Industrial de Santander
KeywordsStaphylococcus aureusBiocompatibilityEscherichia coliSilver nanoparticleAntibacterial activityMicrobiologyNanoparticleChemistryNanotechnologyMaterials scienceBiologyBacteriaBiochemistry

Abstract

fetched live from OpenAlex

Abstract The antimicrobial activity of silver nanoparticles (AgNPs) is strongly influenced by their size and shape. This study investigates the impact of synthesis conditions on the morphology and antibacterial properties of AgNPs against Staphylococcus aureus and Escherichia coli. Six synthesis routes were tested using sodium borohydride, ascorbic acid, and sodium citrate as reducing agents, yielding nanoparticles with diverse structures, including spherical, ellipsoidal, truncated cubes, polyhedral, and elongated bars. Morphological differences were confirmed by TEM and UV-Vis spectroscopy. The overall results showed that quasi-spherical AgNPs synthesized using sodium borohydride at low temperatures and ascorbic acid at room temperature yielded the best MIC and % hemolysis, 2.75 E−3 ng/ml and 5.49 E−4 ng/ml, respectively, likely due to greater surface reactivity. Hemolysis assays suggested that nanoparticles produced at lower temperatures exhibited reduced cytotoxicity. These results highlight the importance of controlling synthesis parameters to optimize the antimicrobial effectiveness and biocompatibility of AgNPs for biomedical applications, particularly against bacteria that have a negative impact on human health.

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)0.001
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 score0.016
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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