Study of the Enzyme Activity Change due to Inkjet Printing for Biosensor Fabrication
Bibliographic record
Abstract
Enzymes, the most commonly used biosensing element, have a great influence on the performance of biosensors. Recently, drop-on-demand (DOD) printing technique has been widely employed for the fabrication of biosensors due to its merits of noncontact, less waste, and rapid deposition. However, enzyme printing studies were rarely conducted on the effect of printing parameters from the aspect of the pressure wave propagation mechanism. This study investigated the effects of pressure wave propagation on enzyme activity from the aspects of wave superposition, wave amplitude, resulting mechanical stress, and protein conformation change using pyruvate oxidase as the model enzyme. We found that the mechanical stress increased the activity of pyruvate oxidase during the inkjet printing process. A shear rate of 3 × 10 5 s –1 enhanced the activity by 14.10%. The enhancement mechanism was investigated, and the mechanical activation or mild proteolysis was found to change the conformation of pyruvate oxidase and improve its activity. This study is fundamental to understand the effect of both printing mechanism and induced mechanical stress on the properties of biomolecules and plays an important role in modulating the activity of other enzyme-based inks, which is crucial for the development of biosensors.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".