Off‐Grid Electrogenerated Chemiluminescence with Customized p‐i‐n Photodiodes
Bibliographic record
Abstract
Abstract Electrochemiluminescence (ECL) is the generation of light induced by an electrochemical reaction, driven by electricity. Here, an all‐optical ECL (AO–ECL) system is developped, which triggers ECL by the illumination of electrically autonomous “integrated” photoelectrochemical devices immersed in the electrolyte. Because these systems are made using small and cheap devices, they can be easily prepared and readily used by any laboratories. They are based on commercially available p‐i‐n Si photodiodes (≈1 € unit−1), coupled with well‐established ECL‐active and catalytic materials, directly coated onto the component leads by simple and fast wet processes. Here, a Pt coating (known for its high activity for reduction reactions) and carbon paint (known for its optimal ECL emission properties) are deposited at cathode and anode leads, respectively. In addition to its optimized light absorption properties, using the commercial p‐i‐n Si photodiode eliminates the need for a complicated manufacturing process. It is shown that the device can emit AO–ECL by illumination with polychromatic (simulated sunlight) or monochromatic (near IR) light sources to produce visible photons (425 nm) that can be easily observed by the naked eye or recorded with a smartphone camera. These low‐cost off‐grid AO‐ECL devices open broad opportunities for remote photodetection and portable bioanalytical tools.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".