MXenes as emerging materials to repair electroactive tissues and organs
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
Nanomaterials with electroactive properties have taken a big leap for tissue repair and regeneration due to their unique physiochemical properties and biocompatibility. MXenes, an emerging class of electroactive materials have generated considerable interest for their biomedical applications from bench to bedside. Recently, the application of these two-dimensional wonder materials have been extensively investigated in the areas of biosensors, bioimaging and repair of electroactive organs, owing to their outstanding electromechanical properties, photothermal capabilities, hydrophilicity, and flexibility. The currently available data reports that there is significant potential to employ MXene nanomaterials for repair, regeneration and functioning of electroactive tissues and organs such as brain, spinal cord, heart, bone, skeletal muscle and skin. The current review is the first report that compiles the most recent advances in the application of MXenes in bioelectronics and the development of biomimetic scaffolds for repair, regeneration and functioning of electroactive tissues and organs including heart, nervous system, skin, bone and skeletal muscle. The content in this article focuses on unique features of MXenes, synthesis process, with emphasis on MXene-based electroactive tissue engineering constructs, biosensors and wearable biointerfaces. Additionally, a section on the future of MXenes is presented with a focus on the clinical applications of MXenes.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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