Photosensitization of Molecular Oxygen on Graphene Oxide for Ultrasensitive Signal Amplification
Bibliographic record
Abstract
Abstract H 2 O 2 and horseradish peroxidase (HRP) are commonly used together in bioassays. HRP is required to accelerate the reaction between a chromogenic substrate (e.g., 3,3′,5,5′‐tetramethylbenzidine, TMB) and H 2 O 2 , and thus amplifies the signal. Herein, molecular oxygen for enzyme‐free and H 2 O 2 ‐free oxidation is explored, still using the same colorimetric reaction. Restricted by spin selection rules, the ground‐state triplet oxygen needs to be converted to the singlet state to oxidize TMB. Phloxine B (PB) is used as the photosensitizer because of its excellent performance and safety. Under green light irradiation, each PB has a turnover of approximately 51 TMB molecules in 20 min, making PB a “molecular enzyme mimic” for signal amplification. With its small size, multiple PB molecules are loaded on a graphene oxide nanosheet to design a modified enzyme‐linked immunosorbance (ELISA) assay (termed photosensitization immunosorbent assay, PISA), improving the 1:1 enzyme/target ratio to n :1. PISA is more sensitive for carcinoembryonic antigen than a commercial ELISA kit, and successfully measures the antigen in the serum of multiple cancer patients. This simple and green method of oxidation coupled with the small size of the photosensitizer and graphene oxide may enable many other applications in biosensor development, smart materials, and energy harvesting.
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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) | 0.000 | 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".