The influence of process parameters of screen-printed invasive plant paper electrodes on cyclic voltammetry
Bibliographic record
Abstract
Abstract Using disposable screen-printed electrodes is a convenient way of monitoring environmental pollution, production process control etc. Usually, commercially available screen-printed electrodes are used, but more and more studies are being carried out in the field of printing electrodes on thinner, low-cost and versatile substrates, including paper. In the present research, the comparison of screen-printed electrodes printed on different paper-based materials is presented. Two unique and innovative invasive plant-based papers made from (1) Japanese knotweed, (2) Canadian and Giant goldenrod and (3) commercially available cardboard were used as a printing material for the electrodes. The selected paper substrates were characterized, and screen-printed electrodes were printed. The influence of substrates’ properties and pre- or post-treatment of the screen-printed electrodes on the electrochemical behaviour is thoroughly analyzed. The results indicate that the printing substrate (roughness) had the most significant influence on the cyclic voltammetry response. Comparing pre- and post-treatment of screen-printed electrodes, it was shown that grinding influenced the electrochemical activity significantly, while corona discharge does not have as significant influence. Besides, it was shown that the invasive plant-based papers are viable alternatives to commercially available papers and can be used as low-cost and eco-friendly alternatives for disposable screen-printed electrodes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".