A Paper Strip-Based Photoinduced Electrogenerated Chemiluminescence Platform with CTF/PMo12 Heterojunction-Sensitive Glutamic Acid Detection
Bibliographic record
Abstract
Glutamic acid (GA), a crucial neurotransmitter, plays a significant role in brain function, muscle health, and various neurological disorders. Elevated GA levels have been associated with conditions such as stroke, Parkinson’s disease, epilepsy, and Alzheimer’s disease. Accurate detection of GA levels is paramount for both diagnostic and research purposes. Herein, we develop a photoinduced electrogenerated chemiluminescence (PECL) test strip fiber paper coated with a covalent triazine framework (CTF)/H 3 PMo 12 O 40 polyoxometalate (PMo12) heterojunction. Under blue-light irradiation, photogenerated carriers were separated by an electric field, π-electron delocalization, and polarization, leaving holes on the surface of PMo12 to drive ECL reactions based on luminol oxidation, enabling the detection of GA. This detection resulted in a PECL signal, and fluorometry information, including RGB values, was captured by using a smartphone. The PECL platform enabled the sensitive detection of GA as a neurotransmitter within a linear range of 0.05–1.7 mM, with a low limit of detection (LOD) of 15 nM. The results validated the safety and feasibility of this approach to detect GA in biological samples with satisfactory selectivity against other molecules. The combination of PECL with a smartphone reader presents an exciting avenue for the development of test strips on-site for visual real-time monitoring. This integration offers a high-throughput approach suitable for applications in point-of-care testing and disease monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".