Direct Laser Writing of Poly(furfuryl Alcohol)/Graphene Oxide Electrodes for Electrochemical Determination of Ascorbic Acid
Bibliographic record
Abstract
Abstract Due to the considerable importance of preventing and treating diseases, efficient detection methods are required to monitor levels of ascorbic acid (AA) in beverages, foods, dietary supplements, and biological fluids. In this work, an efficient, easy handling, low cost, and simple fabrication process for non‐enzymatic electrochemical sensors was fabricated through the carbonization of a graphene oxide filled biomass‐derived polymer poly(furfuryl alcohol) (PFA/GO), as sustainable alternative, using a high throughput CO2 laser‐scribing process. The laser power was found to determine the physicochemical properties of the resulting graphene‐like electrodes. As an electrochemical sensor, devices presented a detection limit of 1.0 μmol cm2 L−1 with good reproducibility towards AA oxidation. For real sample measurements, recovery rates between 97 and 113 % were found in commercial vitamin‐C tablet. Analysis of AA in synthetic sweat presented good intra‐electrode reproducibility and limit of detection of 1.3 μmol cm2 L−1.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".