Fluorescence Sensor Array for Metal Ion Detection Based on Various Coordination Chemistries: General Performance and Potential Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A sensor array containing 9 cross-reactive sensing fluorescent elements with different affinity and selectivity to 10 metal cations (Ca(2+), Mg(2+), Cd(2+), Hg(2+), Co(2+), Zn(2+), Cu(2+), Ni(2+), Al(3+), Ga(3+)) is described. The discriminatory capacity of the array was tested at different ranges of pH and at different cation concentrations using linear discriminant analysis (LDA). Qualitative identification of cations can be determined with over 96% of accuracy in a concentration range covering 3 orders of a magnitude (5-5000 microM). Quantitative analysis can be achieved with over 90% accuracy in the concentration range between 10 and 5000 microM. The array performance was also tested in identification of nine different mineral water brands utilizing their various electrolyte compositions and their Ca(2+), Mg(2+), and Zn(2+) levels. LDA cross-validation routine shows 100% correct classification for all trials. Preliminary results suggest that similar arrays could be used in testing of the consistency of the purification and manufacturing process of purified and mineral waters.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it