Review on Emerging Photothermal Cancer Therapy Using Two-Dimensional MXene Nanomaterials
Bibliographic record
Abstract
Nanomaterial-assisted photothermal therapy (PTT) as a non-invasive cancer treatment has emerged as a promising strategy for the localized ablation of tumors while minimizing systemic toxicity. Among the various nanomaterials developed for this purpose, MXenes a class of two-dimensional transition metal carbides and nitrides have gained significant attention due to their strong light absorption, high photothermal energy conversion efficiency, low infrared emissivity, and surface tunability. These properties enable MXenes to serve as efficient photothermal agents capable of generating localized hyperthermia under near-infrared (NIR) light irradiation. This review provides a brief overview of the molecular structure and photothermal properties of MXenes and explains the mechanisms underlying their advantageous photothermal performance compared to other competing materials. It further discusses recent in vivo studies demonstrating complete tumor eradication through MXene-assisted PTT. Finally, future research directions are proposed to address current challenges and advance MXenes as platforms for safe, effective, and non-invasive cancer treatment.
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 | 0.001 | 0.001 |
| 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".