MXenes in healthcare: transformative applications and challenges in medical diagnostics and therapeutics
Bibliographic record
Abstract
MXenes, a novel class of two-dimensional transition metal carbides, exhibit exceptional physicochemical properties that make them highly promising for biomedical applications. Their application has been explored in bioinstrumentation, tissue engineering, and infectious disease management. In bioinstrumentation, MXenes enhance the sensitivity and response time of wearable sensors, including piezoresistive, electrochemical, and electrophysiological sensors. They also function effectively as contrast agents in MRI and CT imaging for cancer diagnostics and therapy. In tissue engineering, MXenes contribute to both hard and soft tissue regeneration, playing a key role in neural, cardiac, skin and bone repair. Additionally, they offer innovative solutions in combating infectious and inflammatory diseases by facilitating antimicrobial surfaces and immune modulation. Despite their potential, several challenges hinder the clinical translation of MXene-based technologies. Issues related to synthesis, scalability, biocompatibility, and long-term safety must be addressed to ensure their practical implementation in medical applications. This review provides a comprehensive overview of MXenes in next-generation medical diagnostics, including the role they play in wearable sensors and imaging contrast agents. It further explores their applications in tissue engineering and infectious disease management, highlighting their antimicrobial and immunomodulatory properties. Finally, we discuss the key barriers to clinical translation and propose strategies for overcoming these limitations. This review aims to bridge current advancements with future opportunities for integration of MXenes in healthcare.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".