Solid state metathesis synthesis for ZnO-based \nmaterials towards applications in light-emitting \ndiodes and ultra-violet-sensing devices
Bibliographic record
Abstract
Solid state metathesis (SSM) has gained much attention for the synthesis of ZnObased \nsemiconductors. SSM is a synthesis method that avoids organic solvents, high \ntemperature calcination, and it is simple and fast. Co-doped ZnO has already been \nsynthesized by several other methods (co-precipitation method, sol-gel). We wanted \nto use SSM to make Co-doped ZnO, in which Zn²⁺ ions are replaced by Co²⁺ ions. \nThe thesis work goal was to prepare Co-doped ZnO to use it in light emitting diodes \nas a red light emitter. Raman spectra of the attempted Co-doped ZnO confirms the \nformation Co(OH)₂ as a secondary phase, which in turns converts into Co₃O₄ during \nhigh temperature calcination. There were no characteristic peaks in the visible region \nof UV-Vis spectra that would correspond to the emission of red light. \nStoichiometric ZnO films are good candidates for use in various sensing devices. \nThe frequency-dependent UV response of SSM-produced ZnO films were studied under \nAC conditions. After storing in the dark for several days, the UV responses were \nstudied by electrochemical impedance spectroscopy (EIS). The resulting data allow \ndetermination, for each individual film, what range of frequencies are appropriate for \nuse in UV sensing.
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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".