Sensitivity-Enhanced CMOS Phase Luminometry System Using Xerogel-Based Sensors
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
We present the design and implementation of a phase luminometry sensor system with improved and tunable detection sensitivity achieved using a complementary metal-oxide semiconductor (CMOS) integrated circuit. We use sol-gel derived xerogel thin films as an immobilization media to house oxygen (O2) responsive luminescent molecules. The sensor operates on the principal of phase luminometry wherein a sinusoidal modulation signal is used to excite the luminophores encapsulated in the porous xerogel films and the corresponding phase shift of the emission signals is monitored. The phase shift is directly related to excited state lifetimes of the luminophores which in turn are related to the concentration of the target analyte species present in the vicinity of the luminophores. The CMOS IC, which consists of a 16 times 16 high-gain phototransistor array, current-to-voltage converter, amplifier and tunable phase shift detector, consumes an average power of 14 mW with 5-V power supply operating at a 38-kHz modulation frequency. The output of the IC is a dc voltage that corresponds to the detected luminescence phase shift with respect to the excitation signal. As a prototype, we demonstrate an oxygen sensor system by encapsulating the luminophore tris(4,7-diphenyl-1,10-phenanthroline)ruthenium(II) within the xerogel matrices. The sensor system showed a fast response on the order of few seconds and we obtained a detection sensitivity of 118 mV per 1% change in O2 concentration. The system demonstrates a novel concept to tune and improve the detection sensitivity for specific concentrations of the target analyte in many biomedical monitoring applications.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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