Evaluation of GaN and InGaN semiconductors as potentiometric anion selective electrodes / by Rozalina Dimitrova.
Bibliographic record
Abstract
"Ion selective electrodes (ISEs) are chemical sensors primarily used for in situ analysis and monitoring of air, water, and land. Despite their easy fabrication, low cost, and simple usage, ISEs still suffer from the sensitivity of their response to temperature variations, solution turbidity, interferences from other ions in solution, drift of the electrode potential, membrane fouling, and short lifetime. As a result, investigating new materials to develop ISEs that can address some of these limitations is a worthwhile and challenging topic of research. The excellent mechanical, thermal and chemical stability of gallium nitride (GaN) and indium gallium nitride (InGaN) semiconductors, coupled with their resistance to corrosion and low toxicity if dissolved, are some of the properties that make these materials prime candidates for a variety of sensor applications, particularly at high temperatures and in harsh environments. This thesis evaluates the potential of these two semiconductor materials for replacing conventional ISE membranes with a solid-state semiconductor surface/solution interface. The benefits gained from this novel design of the ISE sensing element are assessed.
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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) | 0.002 | 0.001 |
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".