Three-Dimensional Assembled MXene Architectures in Biomedical Innovations
Bibliographic record
Abstract
MXenes, primarily composed of two-dimensional (2D) transition metal carbides/nitrides, have emerged as a research hot spot, particularly in the field of biomedicine, owing to their notable physicochemical properties, functional termini, and intriguing biocompatibilities. However, they suffer from smaller specific surface areas, poor mechanical properties, and a lack of dimensional control. Previous studies have indicated that constructing 2D MXene nanosheets into a three-dimensional (3D) structure can effectively reduce the level of re-aggregation, which can provide a larger specific surface area and stronger physical structural support for various applications. This work provides a systematic and focused review of the latest developments in 3D-assembled MXene for biomedical applications. This review first presents a comprehensive summary of the widely employed strategies for manufacturing 3D-assembled MXene architectures, such as metal ion-induced assembly, metal–organic framework-assisted assembly, carbon materials-assisted assembly, and polymer-assisted assembly. Furthermore, research attention is also directed toward exploring the structure–property relationships of 3D-assembled MXenes for diagnostic applications, i.e., biosensing and bioimaging, and therapeutic applications, i.e., drug delivery and tumor therapy. Finally, opportunities and challenges of the 3D-assembled MXenes have also been outlined. We strongly expect that 3D-assembled MXenes can offer significant benefits in the context of clinical translations for theranostic applications.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".