A Flexible Electrochemical Biosensor Based on NdNiO<sub>3</sub> Nanotubes for Ascorbic Acid Detection
Bibliographic record
Abstract
Flexible and wearable electrochemical biosensors are considered a non-invasive tool for monitoring biological substances, thus attracting attention due to the simple assembly, fast response, ultra-sensitivity, and low cost. Among the substances detected by electrochemical methods, ascorbic acid (AA) stands out, as its presence in the body promotes adequate physiological functions of the immune, central nervous, and circulatory systems, thus preventing and treating various diseases. Herein, this work focuses on the use of nanostructured NdNiO 3 compounds in an alternative flexible biosensor for AA detection by electrochemical sensing. Here, 1D nanostructures of NdNiO 3 were obtained by wet pore filling of a mesoporous aluminum oxide template. To the best of our knowledge, no electrochemical biosensors using NdNiO 3 nanotubes supported onto GO flexible electrodes have been reported for AA or other bioanalyte detection. Next, an electrochemical biosensor for AA was built using a laser-induced graphene (GO) electrode and two different NdNiO 3 (NNO) nanotubes, one with an external diameter of 20 nm (NNO20) and the other with 100 nm (NNO100). The size effect and Ni 3+ /Ni 2+ ratio influence on the sensing properties can be verified through electrochemical characterization. The GO/NNO20 and GO/NNO100 biosensors presented a detection range of 30 to 1100 μmol L –1, but the minimum detectable limit (3.8 μmol L –1 ) and sensitivity (0.031 μA μM –1 cm –2 ) are significantly better for the GO/NNO100 device. These outstanding results make both devices competitive with other AA devices listed in the literature. We also simulated the biosensors’ real application and verified that these biosensors could detect AA in synthetic sweat and under application of mechanical deformations. Thus, the GO/NNO biosensors showed a promising alternative to the development of real-time monitoring, POC devices, and flexible wearable electrochemical devices to use in AA detection. Future works should address the potential to detect other bioanalytes.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".