Fabricating a Dielectric Coating for an Improved Electrokinetic Micropump
Bibliographic record
Abstract
This research proposes a method of fabricating a dielectric coating over the microelectrodes of an alternating current electrothermal (ACET) device to create a barrier between the biofluid and microelectrodes, eliminating the risk of electrolysis and creating a more effective device. Strontium titanate (STO) is proposed as a potential dielectric material for ACET devices, as it has a high dielectric constant compared to other materials, allowing for a higher device flow rate. This work examines various parameters used for the radio-frequency sputtering (rf-sputtering) of STO, and how these parameters affect the deposited film properties and the microelectrodes being coated. It was found during initial experimentation that the rf-sputtering technique used to deposit STO thin-films tends to etch away at the electrodes due to high energy oxygen ions. Literature is scarce on the topic but provides some guidance on modifications to the initial sputtering parameters. Through additional experiments, the following observations were made: a high oxygen injection (~30%) is required to ensure the STO film is nonconductive, sputtering the slides at 90° significantly reduces etching, a lower RF power reduces etching (but has not been found to eliminate it) and decreasing the bias power appears to reduce both etching and deposition rates. This work shows that a dielectric coating could be deposited over ACET electrodes with further work to optimize the parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".