Facile Fabrication of Hybrid Copper–Fiber Conductive Features with Enhanced Durability and Ultralow Sheet Resistance for Low‐Cost High‐Performance Paper‐Based Electronics
Bibliographic record
Abstract
The accelerating arrival of the Internet of Things (IoT) era creates a rapidly growing demand for paper‐based electronics due to their low cost, light weight, flexibility, and environmental friendliness. However, manufacturing high quality circuits with ultralow sheet resistance on cellulose paper remains a challenge. Here, a method is proposed to easily fabricate hybrid copper–fiber highly conductive features on low‐cost cellulose paper with strong adhesion and enhanced bending durability. A functional coating for fast surface modification of cellulose paper via an in situ cross‐linking mechanism between pyridine and epoxy groups is developed to enhance copper–fiber adhesion and protect paper in alkaline electroless deposition bath. Thanks to the unique porous structure of cellulose paper, the electroless copper deposition occurs in a 3D manner in the inkjet‐printed area, and forms a flexible copper–fiber hybrid structure ≈90 µm thick with sheet resistances as low as 0.00544 Ω sq−1. To demonstrate its potential applications in the IoT industry, a functional battery‐free circuit and a high‐performance planar antenna for radio frequency identification are fabricated and tested using the proposed method.
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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".