Synthesis and applications of double metal MXenes: A review
Bibliographic record
Abstract
MXenes are known as a new type of two-dimensional layered materials that are composed of carbide, nitride, or carbonitride of transition metals. In the recent discovery of a new class of MXenes, two transition metals occupy the metal site, called double transition metal MXenes (DTM). These multilayer composites are of interest due to their attractive features such as high ion transport, extensive surface area, and biocompatibility. Some computational methods are used to predict the properties and performance of bimetallic carbonitrides. The most important feature of this category of materials is the stability and amount of formation energy, which directly affects the choice of material in various applications. Density functional theory (DFT) calculations are very beneficial to estimate the thermodynamic stability of DTM MXenes. Of course, proper surface modification with stable terminals is needed to overcome the limitations of DTM MXenes. In this review, the electrochemical, metallic, and magnetic properties of DTM MXene have been presented first. In the following, preparation methods are summarized according to the latest published findings. Then, various applications including hydrogen evolution reactions, anode materials in lithium and sodium batteries, nanomagnetic materials, and special applications have been investigated. Finally, more challenges, prospects, and suggestions for the development of two-dimensional DTM MXenes have been presented.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".