Micro “Hyper‐Channels” on Laser‐Refined Cellulose Structures
Bibliographic record
Abstract
Abstract Controlled liquid transportation is widely applied in both academia and industry. However, liquid transport applications are limited by parameters such as driving forces, precision, and velocity. Herein, a simple laser‐refining technology is presented to produce micro “hyper‐channels”. A cellulose substrate is rendered hydrophobic through silanization and refined with a laser to produce both hierarchical nanostructures and a wettability contrast simultaneously. Such a method enables faster (“hyper”‐channel) aqueous liquid transportation (≈25X, 50 mm s−1) compared to conventional methods. Complex patterns can be readily produced at different scales with spatial resolution as low as 50 µm. This technique also controls the refining depth on the thin paper substrate. Shallow channels can be fabricated on thin paper substrates that enable fluidic channel‐crossover without liquid mixing. With certain parameters, the technique creates “portals” through the substrate, allowing trans‐dimensional liquid transportation between two layers of a single sheet of substrate. The fluid throughput can be increased, while also permitting fluidic channel crossover without liquid mixing. By introducing multiple portals, the controlled fluid can transfer trans‐dimensionally several times, enabling further fluidic complexity. The real‐life utility of the method is demonstrated by creating a trans‐dimensional microfluidic device for colorimetric detection.
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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".