Patterning MXene RFID Antennas via Surface Hydrophobicity Modulation
Bibliographic record
Abstract
Abstract MXenes are an emerging class of 2D transition metal carbides and nitrides, known for their mechanical, chemical, and electrical properties, which make them suitable for electromagnetic applications such as antennas and radio frequency identification (RFID) devices. This research demonstrates that MXene‐based RFID antennas can be patterned by modifying the hydrophobicity of a hydrophilic paper substrate. A Ti3C2Tx MXene colloid with a concentration of 32 mg g−1 with a conductivity of (≈10 000 S cm−1) is used to fabricate conductive traces of RFID antennas through dip‐coating, using a superhydrophobic layer patterning technique. Ink spreading is minimized by controlling the water repellency of the surfaces and taking advantage of the inherent hydrophilicity of MXene, resulting in improved pattern fidelity. The versatility of the proposed patterning method is demonstrated through the fabrication of three different RFID antenna tags, including dipole, meander, and T‐matched antennas, designed to operate at ultrahigh frequency (UHF) (800–920 MHz). The method also enabled impedance matching for dipole and meander‐shaped RFIDs to 50 Ω, achieving ≈ 97% efficiency compared to copper‐based counterparts fabricated using subtractive methods. This approach enables well‐defined, self‐confined deposition and offers a scalable process for MXene conductive traces and microstrip lines patterning.
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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) | 0.001 | 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".