Rapid prototyping of paper-based electronics by robotic printing and micromanipulation
Bibliographic record
Abstract
The fabrication techniques of paper-based electronics have been widely explored in the past two decades, contributing to the wide application of these promising devices in a variety of fields. In this paper, we report a new rapid prototyping technique for constructing paper-based field-effect transistor (FET) biosensors integrating rolled-up semiconductor microtubes. Leveraging robotic printing and micromanipulation techniques, silver ink electrodes are printed on paper and pre-synthesized microtubes are transferred onto a pair of printed electrodes to form a complete FET. Compared with existing fabrication techniques of paper-based electronics that purely rely on printing, the proposed technique is more versatile in that it can rapidly integrate high-performance semiconductor microtubes onto paper substrates. To improve the printing uniformity, a time-shift mechanism is proposed to compensate the ink under-and over-dispersion at the beginning and end of the printing process, respectively. To realize automated microtube pick-up and transfer, image processing and motion control algorithms are developed to detect positions of the end-effector (a glass needle) and the microtube, and to control motions of the glass needle with minimized human intervention. We demonstrate the effectiveness of the technique by fabricating the designed paper-based devices using bimetallic microtubes and measure their current-voltage characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".