Surface characterization using chemical force microscopy and the flow performance of modified polydimethylsiloxane for microfluidic device applications
Bibliographic record
Abstract
The widespread interest in micro total analysis systems has resulted in efforts to develop devices in cheaper polymer materials such as polydimethylsiloxane (PDMS) as an alternative to expensive glass and silicon devices. We describe the oxidation of the PDMS surface to form ionizable groups using a discharge from a Tesla coil and subsequent chemical modification to augment electroosmotic flow (EOF) within the microfluidic devices. The flow performance of oxidized, amine-modified and unmodified PDMS materials has been determined and directly compared to conventional glass devices. Exact PDMS replicas of glass substrates were prepared using a novel two step micromolding protocol. Chemical force microscopy has been utilized to monitor and measure the efficacy of surface modification yielding information about the acid/base properties of the modified and unmodified surfaces. Results with different substrate materials correlates well with expected flow modifications as a result of surface modification. Oxidized PDMS devices were found to support faster EOF (twice that of native PDMS) similar to glass while those derivatized with 3-aminopropyl triethoxysilane (APTES) showed slower flow rates compared to native PDMS substrates as a result of masking surface charge. Results demonstrate that the surface of PDMS microdevices can be manipulated to control EOF characteristics using a facile surface derivatization methodology allowing surfaces to be tailored for specific microfluidic applications and characterized with chemical force microscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".