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Record W4387191107 · doi:10.1016/j.apsusc.2023.158579

Microwave-assisted green synthesis of monodispersed carbon micro-spheres and their antibacterial activity

2023· article· en· W4387191107 on OpenAlexaff
Nada Abdulwali, Joshua van der Zalm, Antony R. Thiruppathi, A. T. Khaleel, Aicheng Chen

Bibliographic record

VenueApplied Surface Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCarbonizationHydrothermal carbonizationX-ray photoelectron spectroscopyRaman spectroscopyAntibacterial activityAscorbic acidCatalysisMaterials scienceCarbon fibersChemical engineeringSurface modificationFourier transform infrared spectroscopyNuclear chemistryNanotechnologyChemistryScanning electron microscopeOrganic chemistryComposite numberBacteria

Abstract

fetched live from OpenAlex

Well-dispersed mono-carbon microspheres were synthesized via the hydrothermal carbonization of ascorbic acid (AA) using a microwave reactor without adding any catalyst. The synthesized materials were characterized by various microscopic and spectroscopic techniques (e.g., SEM, TEM, XRD, Raman, XPS, EDX, and FTIR spectroscopy), showing that the formed carbon spheres (CSs) exhibited uniform morphology and surface structure with abundant oxygen functional groups. The dimension of the formed CSs could be easily tuned by altering the AA concentration, reaction time, and temperature. A mechanism for the formation of the CSs was proposed, and their antibacterial activities were also investigated against two bacterial strains, showing the inhibition rate of 94% for E. coli and 100% for S. aureus . This biocidal activity was achieved with the pure CSs produced from the inexpensive AA (Vitamin C) via a rapid green process without any further surface modification or doping, thus promising for myriad surface coating, medical and environmental applications.

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 score0.023
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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