Geiger Mode Single Photon Counting: A Laboratory Experiment Exploring Delayed Fluorescence in Plants
Bibliographic record
Abstract
The time-resolved detection of very low intensity light emission has become an essential capability in many areas of science including molecular biology, fluorimetry, DNA sequencing, virus detection, nanoparticle research, and optical materials development. Among the most basic techniques for the detection of rapidly fluctuating low-intensity light is photon counting. Despite its extensive applications in the physical and biological sciences and engineering, photon counting techniques have traditionally been left out of undergraduate curricula due to the prohibitive cost of the equipment and the complexity of its operation. However, the recent development of the low-cost silicon photomultiplier device, a solid-state single photon avalanche diode detector, has enabled the availability of easy-to-operate, low voltage, advanced timing performance, and highly sensitive photon counting systems well within the budget of undergraduate teaching laboratories. In this contribution, we provide a strategy to introduce undergraduate interdisciplinary chemistry and physics students to silicon-photomultiplier-based photon counting through the interesting phenomenon of delayed fluorescence from photosystem II in plants. This experiment is perhaps best suited for an upper-level undergraduate laboratory and should stimulate the interest of students across a wide variety of disciplines, from physical chemistry to molecular biophysics to photonics instrumental analysis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".