Fabrication of Conductive Nanomaterial Patterns on Polymeric Substrates Using Laser and Adhesive Tape
Bibliographic record
Abstract
This study introduces a technique for fabricating flexible devices using a combination of spraying carbon nanotubes (CNTs), laser patterning, and adhesive tape. The method involves depositing CNTs on polypropylene substrate by spray coating and utilizing selective laser treatment to create durable and embedded conductive patterns. This approach addresses the common issue of poor adhesion of conductive material to substrates in flexible electronics by partially melting the polymer to enhance CNTs integration. The fabricated sensors exhibit excellent durability and retain performance after multiple bending cycles. Chronoamperometry tests demonstrate the ability of the electrochemical sensors thus fabricated to detect hydrogen peroxide (H2O2) in buffer solutions, with a detection range from 0.1 to 900 ppm. The use of selective laser treatment and adhesive tape enables the removal of unpatterned areas and ensures high precision and flexibility in the design. This straightforward and versatile method can be applied to various conductive materials and polymers, and it can offer significant potential for advancements in flexible sensor technology and other electronic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".