Enzyme Immobilization by Inkjet Printing on Reagentless Biosensors for Electrochemical Phosphate Detection
Bibliographic record
Abstract
Enzyme-based biosensors commonly utilize the drop-casting method for their surface modification. However, the drawbacks of this technique, such as low reproducibility, coffee ring effects, and challenges in mass production, hinder its application. To overcome these limitations, we propose a novel surface functionalization strategy of enzyme crosslinking via inkjet printing for reagentless enzyme-based biosensors. This method includes printing three functional layers onto a screen-printed electrode: the enzyme layer, crosslinking layer, and protective layer. Nanomaterials and substrates are preloaded together during our inkjet printing. Inkjet-printed electrodes feature a uniform enzyme deposition, ensuring high reproducibility and superior electrochemical performance compared to traditional drop-casted ones. The resultant biosensors display high sensitivity, as well as a broad linear response in the physiological range of the serum phosphate. This enzyme crosslinking method has the potential to extend into various enzyme-based biosensors through altering functional layer components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".