Flexible MXene–hydrogel mechatronics for next-generation energy storage applications: A review
Bibliographic record
Abstract
The rapid advancement of wearable and epidermal electronics has driven the demand for compact, flexible, and high-performance energy storage systems. This review examines the integration of two-dimensional (2D) MXenes with hydrogel matrices for multifunctional mechatronic applications in micro-energy storage systems designed for next-generation wearable devices. The review identifies three core research domains: (1) synthesis and structural tailoring of MXene-hydrogel composites, (2) optimization of electrochemical and mechanical behaviors for microbatteries and mirco-supercapacitors, and (3) design of shape-conforming, self-healing, and multifunctional energy devices. From these domains, key challenges are highlighted, including material degradation, environmental sensitivity, and manufacturing scalability. The review further identifies critical knowledge gaps in ion transport dynamics, surface chemistry control, and mechanical adaptability, and proposes future research directions to address these gaps. Overall, this work provides a comprehensive understanding of MXene-hydrogel mechatronics and outlines their potential to enable flexible, autonomous, and high-efficiency next-generation energy storage systems.
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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 | 0.000 |
| 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".