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Record W4409042431 · doi:10.15586/qas.v17i2.1532

Hydrophobic carbon dots: An overview of the synthesis, purification, cytotoxicity, and potential applications in food safety and analytical chemistry

2025· article· en· W4409042431 on OpenAlexaff
Rahim Molaei, Roghayieh Razavi, Abdullah Khalid Omer, Anita Lotfi Javid, Parya Ezati, Negar Nikfarjam, Loong‐Tak Lim

Bibliographic record

VenueQuality Assurance and Safety of Crops & Foods · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryCytotoxicityCarbon fibersNanotechnologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Hydrophobic carbon dots (HCDs) represent a burgeoning class of nanomaterials distinguished by their unique physicochemical, antimicrobial, and optical properties. These attributes have propelled HCDs to the forefront of research, particularly in the fields such as composite film production, biological imaging, and antibacterial coatings, with broad implications for industries, such as food safety, medicine, catalysis, and sensor technology. This comprehensive review delves into the diverse synthesis methodologies of HCDs, such as chemical oxidation, hydrothermal/solvothermal techniques, pyrolysis, and microwave irradiation. The comparative benefits and challenges of these methods were analyzed critically. This manuscript presents an in-depth exploration of purification methods, hydrophobicity indices, and cytotoxicity by a thorough examination of current literature. In addition, it highlights the innovative applications of HCDs, from advanced chemosensors, the development of stationary phases for chromatography to bioimaging and diagnostics, and the construction of optoelectric devices for food 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.315
Teacher spread0.291 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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