Patterning of Nano and Micromaterials on Polymer Substrates Using Spraying, Selective Laser Treatment, and Adhesive Delamination for Sensing Applications
Bibliographic record
Abstract
Abstract Integrating conductive nanomaterials with polymer substrates in a scalable and low‐cost way is crucial for developing flexible electronics. This work presents a scalable process for integrating high‐quality nanomaterials with polymer films to fabricate flexible electrical devices by combining simple yet effective techniques. Various conductive patterns on polymer substrates are successfully created by utilizing a combination of nanomaterial spraying, laser treatment, and adhesive delamination. The laser treatment embeds the sprayed nanoparticles onto the polymer surface by partially melting the polymer and significantly enhancing their adhesion selectively in places where it traces a path. This method incorporates single‐walled carbon nanotubes, graphene, and molybdenum disulfide onto polymers such as polypropylene, polyvinylidene fluoride, and nylon, achieving a minimum line width of 350 µm. The versatility of this technique is demonstrated by fabricating a range of devices, including microheaters, temperature sensors, chemiresistive sensors, and electrochemical sensors. The fabricated devices exhibit excellent durability and stable performance, addressing the limitations of integrating nanomaterials into polymer films. Additionally, this method allows for precise control of conductivity and pattern complexity, making it suitable for various applications. This work contributes to the advancement of flexible electronics, providing a scalable and adaptable method for creating high‐performance devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".