A Tool, an App and a Field: Fluorescent PET Sensors, Blood Electrolyte Analysis and Molecular Logic as Products of Supramolecular Photoscience from Northern Ireland and Sri Lanka
Bibliographic record
Abstract
Abstract The general tool of fluorescent PET (photoinduced electron transfer) sensors/switches – a molecular design principle with engineering features – is outlined, with the aid of frontier orbital energy diagrams. Fluorophores such as anthracene, 1,3‐diaryl‐Δ2‐pyrazolines and 4‐amino‐1,8‐naphthalimides are employed within this system, alongside receptors such as amines, carboxylates, crown ethers and amino acids. This tool appealed to a multinational corporation for building a medical analyzer for electrolytes such as Na+, K+, Ca2+ and gases like CO2, which became a commercially successful application. Finally, the tool was a springboard for chemistry to cross into computer science. The field of molecular logic can elucidate how molecules inside us handle information. Molecular examples of the simplest logic gates such as YES, NOT, OR, AND are described. A case of a human‐level computation – visual edge detection – is also included.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".