Bibliographic record
Abstract
An electrophoretic deposition (EPD) method has been developed for the deposition of nanostructured zinc oxide (ZnO) and TiO 2 films. It was shown that the stabilization and charging of the nanoparticles in suspensions can be achieved using organic molecules, such as dopamine and alizarin yellow (AY) dye, which were adsorbed on the oxide nanoparticles. Fourier‐transform infrared spectroscopy investigations showed that the adsorption mechanism is based on the complexation of metal ions at the surfaces of oxide nanoparticles. Cationic dopamine additive was used for the formation of deposits by cathodic EPD. The adsorption of anionic AY on the oxide nanoparticles resulted in charge reversal and enabled the formation of anodic deposits. The method enabled the codeposition of ZnO and TiO 2 and the formation of composite films. It was shown that the deposition yield and deposit composition can be controlled by the variation of deposition time and suspension composition. The deposits were studied using X‐ray diffraction and electron microscopy. The deposition mechanism and kinetics of deposition are discussed.
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.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".