MXenes Antibacterial Properties and Applications: A Review and Perspective
Bibliographic record
Abstract
The mutations of bacteria due to the excessive use of antibiotics, and generation of antibiotic-resistant bacteria have made the development of new antibacterial compounds a necessity. MXenes have emerged as biocompatible transition metal carbide structures with extensive biomedical applications. This is related to the MXenes' unique combination of properties, including multifarious elemental compositions, 2D-layered structure, large surface area, abundant surface terminations, and excellent photothermal and photoelectronic properties. The focus of this review is the antibacterial application of MXenes, which has attracted the attention of researchers since 2016. A quick overview of the synthesis strategies of MXenes is provided and then summarizes the effect of various factors (including structural properties, optical properties, surface charges, flake size, and dispersibility) on the biocidal activity of MXenes. The main mechanisms for deactivating bacteria by MXenes are discussed in detail including rupturing of the bacterial membrane by sharp edges of MXenes nanoflakes, generating the reactive oxygen species (ROS), and photothermal deactivating of bacteria. Hybridization of MXenes with other organic and inorganic materials can result in materials with improved biocidal activities for different applications such as wound dressings and water purification. Finally, the challenges and perspectives of MXene nanomaterials as biocidal agents are presented.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".