Effect of curing parameters and configuration on the efficacy of ultraviolet light curing self-adhesive masks used for abrasive jet micro-machining and microfluidic device fabrication
Bibliographic record
Abstract
Soft lithography techniques has been used widely in the past decade to fabricate microfluidic chips used in biomedical applications. Abrasive jet machining (AJM) has been used to fabricate similar chips using particle erosion mechanisms. This thesis proposes a new technique using a UV light sensitive self-adhesive mask (RapidMask) and AJM to fabricate a three dimensional flow focusing microfluidic chip where the depth of the channel is allowed to vary along the channel length. A detailed characterization of the effect of curing parameters of a UV light curing self-adhesive mask on the resulting feature resolution is reported. Instead of relying on the manufacturer recommended curing parameters which were vaguely described for specific UV curing units, it was found that measured energy density could be used to quantify a recommended cure that is independent of the curing unit. The best achievable pattern on borosilicate glass using RM and AJM was found and reported along with the erosion rates of uncured, cured RM during AJM. A new methodology was introduced to use multiple layers of the RM in order to increase the achievable feature aspect ratio. The results of the RM curing and multiple layer investigation were then used to fabricate a three dimensional flow focusing chip with a varying depth at the focusing junction. The chip was then sealed and tested to demonstrate its capabilities and potential in healthcare and biomedical applications. To the best knowledge of the author, this thesis is the first to report using a double layer RM to fabricate a microfluidic chip using AJM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".