Fabrication and laser patterning of polystyrene optical oxygen sensor films for lab-on-a-chip applications
Bibliographic record
Abstract
We present a novel and simple method for patterning oxygen-sensitive polystyrene thin films and demonstrate its potential for integration with microfluidic lab-on-a-chip devices. Optical oxygen sensing films composed of polystyrene with an embedded luminescent oxygen-sensitive dye present a convenient option for the measurement of oxygen levels in microfluidic and lab-on-a-chip devices; however, patterning and integrating the films with poly(dimethylsiloxane) (PDMS) microfluidic devices has proven difficult due to a residue after dry etch patterning that inhibits subsequent PDMS bonding. Our new method uses mask-less laser ablation by a commercial laser ablation system to define the outline of the structures and subsequent bulk film removal by aqueous lift-off. Because the bulk film is peeled or lifted off of the substrate rather than etched, the process is compatible with standard PDMS plasma bonding. We used ToF-SIMS analysis to investigate how laser ablation facilitates this fabrication process as well as why dry etching polystyrene inhibits PDMS plasma bonding. The results of this analysis showed evidence of chemical species formed during the laser ablation and dry etching processes that can produce these effects. Our new method's mask-less nature, simplicity, speed, and compatibility with PDMS bonding make it ideally suited for single-use lab-on-a-chip applications. To demonstrate the method's compatibility with PDMS microfluidics, we also present a demonstration of the sensors' integration into a microfluidic oxygen gradient generator device.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".