Meta-analyses of the evolution of MXene synthesis for bioengineering and artificial intelligence-driven applications
Bibliographic record
Abstract
The year 2011 marked a breakthrough in material science innovation with the discovery of MXenes, an emerging family of two-dimensional transition metal-based nanomaterials. Owing to their distinctive properties, MXenes have rapidly surfaced as transformative materials, particularly in energy-storage and nanomedicine. In this review, we systematically explore the evolution of MXene synthesis, from its discovery to current advancements, focussing on their bioengineering applications, through a meta-analytic and bibliometric lens. We discuss synthesis methods, ranging from hydrofluoric acid (HF)-based etching to non-HF approaches, along with post-synthesis processes like intercalation, delamination and surface functionalization, that tailor MXene properties for biomedical therapeutics. We also overview key microscopy, spectroscopy and diffraction-based characterization methods, to understand their structure and functionality. Additionally, discussion on artificial intelligence (AI)-driven innovations highlights the significant shift in material science. By connecting synthesis methods with resulting characteristics and meta-analyses trends, this review emphasizes MXenes' transformative potential in regenerative therapeutics and diagnostics.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".