Graphene oxide and carbon dots as broad-spectrum antimicrobial agents – a minireview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it