Hybrid polymer fabrication process for electro-enzymatic glucose sensor
Bibliographic record
Abstract
We present a novel self-aligned and hybrid polymer fabrication process for an electro-enzymatic glucose sensor. The self-aligned fabrication process is performed using polydimethylsiloxane (PDMS) as a process substrate material, SU-8 as a sensor structural material, and gold as an electrode material. PDMS has many advantages as a process substrate over conventional substrates such as bare silicon or glass. During the fabrication process, SU-8 has good adhesion to the PDMS. However, after completion of all fabrication steps, the SU-8 based sensors can be easily peeled-off from the PDMS. The PDMS is prepared on a glass handle wafer, and is reusable for many process cycles. Such an SU-8 release technique from a PDMS substrate has never been proposed before. The novel process is employed to realize a glucose sensor with active and reference gold electrodes that are sandwiched between two SU-8 layers with contact pad openings and the active area opening to the top SU-8 layer. The enzyme glucose oxidase is immobilized within the confined active area opening to provide an active electrode sensing surface. After successful fabrication using the hybrid process, the overall thickness of the sensors is measured between 166.15 μm and 210.15 μm. The sensor area and the electrode area are 2mm x 3mm and 2mm x 2mm respectively. The resulting glucose sensors are mechanically flexible. A linear response is observed for the glucose sensors, typically between 50mg/dl and 600mg/dl glucose concentrations.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".