Recent advances in 2D MXene-based heterostructures for gas sensing: mechanisms and applications in environmental and biomedical fields
Bibliographic record
Abstract
MXene in 2011, significant progress has been made in using MXenes as chemiresistive sensors. Their layered structure, abundant surface groups, hydrophilicity, tunable conductivity, and excellent thermal properties make MXenes ideal for low-power, flexible, room temperature gas sensors, fostering scalable and reproducible applications in portable devices. This review evaluates the latest advancements in MXene-based gas sensors, beginning with an overview of the elemental compositions, structures, and typical fabrication process of MXenes. We subsequently examine their applications in gas sensing domains, evaluating the proposed mechanisms for detecting common volatile organic compounds such as acetone, formaldehyde, ethanol, ammonia, and nitrogen oxides. To set this apart from similar reviews, our focus centered on the mechanistic interactions between MXene sensing materials and analytes (particularly for chemiresistive gas sensors), leveraging the distinct functionalities of MXene chemistries, which can be finely tuned for specific applications. Ultimately, we examine the current limitations and prospective research avenues concerning the utilization of MXenes in environmental and biomedical applications.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".