Graphene oxide and carbon dots as broad-spectrum antimicrobial agents – a minireview
Bibliographic record
Abstract
Due to the increasing global population, growing contamination of water and air, and wide spread of infectious diseases, antibiotics are extensively used as a major antibacterial drug. However, many microbes have developed resistance to antibiotics through mutation over time. As an alternative to antibiotics, antimicrobial nanomaterials have attracted great attention due to their advantageous properties and unique mechanisms of action toward microbes. They inhibit bacterial growth and destroy cells through complex mechanisms, making it difficult for bacteria to develop drug resistance, though some health concerns related to biocompatibility remain for practical applications. Among various antibacterial nanomaterials, carbon-based materials, especially graphene oxide (GO) and carbon dots (C-Dots), are promising candidates due to the ease of production and functionalization, high dispersibility in aqueous media, and promising biocompatibility. The antibacterial properties of these nanomaterials can be easily adjusted by surface modification. They are promising materials for future applications against multidrug-resistant bacteria based on their strong capacity in disruption of microbial membranes. Though many studies have reported excellent antibacterial activity of carbon nanomaterials, their impact on the environment and living organisms is of concern due to the accumulatory and cytotoxic effects. In this review, we discuss antimicrobial applications of the functional carbon nanomaterials (GO and C-Dots), their antibacterial mechanisms, factors affecting antibacterial activity, and concerns regarding cytotoxicity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".