Synthesis, Characterization, and Growth Mechanism of n-Type CuInS<sub>2</sub> Colloidal Particles
Bibliographic record
Abstract
We report on the growth of CuInS 2 n-type semiconductive particles, prepared using a modified Czekelius’s colloidal method, as well as their chemical and electrochemical properties. Solid state Raman spectroscopy revealed two crystalline phases: chalcopyrite and the so-called copper−gold phase. Increasing the annealing temperature of the particles favors the formation of the chalcopyrite phase. As shown by XPS, EDX, and ICP-AES, an excess of indium was obtained, which was greater at the surface (CuIn 1.45 S 1.9 at 450 °C) than in the bulk (CuIn 1.04 S 1.74 at 450 °C). UV−visible measurements showed that the n-type CuInS 2 possesses a direct bandgap energy of 1.45 eV. Two organic redox couples in nonaqueous media were used to perform the capacitance measurements carried out by EIS on a CuInS 2 film: 5-mercapto-1-methyltetrazolate (T − )/di-5-(1-methyltetrazole) disulfide (T 2 ) and 5-trifluoromethyl-2-mercapto-1,3,4-thiadiazolate (G − )/5,5′-bis(2-trifluoromethyl-1,3,4-thiadiazole) disulfide (G 2 ). Fermi levels of −3.95 eV and −3.64 eV and majority charge carrier densities of 4.1 × 10 18 and 1.8 × 10 18 cm −3 were determined, respectively, using these redox couples. On the basis of the CuInS 2 /electrolyte energy level diagrams, the G − /G 2 redox couple is expected to lead to a more efficient device (greater photocurrent and photovoltage). In situ Raman spectroscopy measurements showed that the reactivity of copper with hexamethyldisilathiane is faster than with indium. This explains the excess of indium at the surface of the CuInS 2 particles, as well as its n-type semiconductivity.
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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) | 0.000 | 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 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".