Wafer‐Scale Transfer of MXene Films with Enhanced Device Performance via 2D Liquid Intercalation
Bibliographic record
Abstract
Abstract Wafer‐scale transfer processes of 2D materials significantly expand their application space in scalable microelectronic devices with excellent and tunable properties through van der Waals (vdW) stacking. Unlike many 2D materials, wafer‐scale transfer of MXene films for vdW contact engineering has not yet been reported. With their rich surface chemistry and tunable properties, the transfer of MXenes can enable enormous possibilities in electronic devices using interface engineering. Taking advantage of the MXene hydrophilic surface, a straightforward, green, and fast process for the transfer of MXene films at the wafer scale (4‐inch) is developed. Uniform vdW stacking of several types of large‐area heterojunctions including MXene/MXene (Ti3C2Tx, Nb2CTx, and V2CTx), MXene/MoS2, and MXene/Au is further demonstrated. Multilayer support is applied to minimize damage or deformation in the transfer process of patterned Ti3C2Tx film. It allows us to fabricate thin film transistors and manipulate the MXene/MoS2 interface through the intercalation of various 2D liquids. Particularly noteworthy is the significant enhancement of the interfacial carrier transfer efficiency by ≈2 orders of magnitude using hydrogen iodide (HI) intercalation. This finding indicates a wide range of possibilities for interface engineering by transferring MXene films and employing liquid‐assisted interfacial intercalation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".