Cold gas spraying A promising technique for photoelectrodesThe example TiO2
Bibliographic record
Abstract
Cold gas spraying CGS is presented as an innovative approach to deposit semiconductor particles onto substrates in order to produce photoelectrodes for electrochemical applications, e.g. the oxygen evolution reaction OER . The spraying technique is characterized by high velocity particles which impact and deposit on a surface at relatively low temperature. Compared to established wet chemical techniques, an increased photoelectrochemical activity is observed due to an enhanced particle to substrate bonding. For closer investigation of the influence of the process parameters on the photoelectrochemical activity, TiO2 electrodes P25 20 by Evonik Industries sprayed with different carrier gases nitrogen, argon, helium are analyzed. Due to different acceleration conditions of the particles in the de Laval nozzle, these carrier gases allow to investigate the influence of the impact energy of the particles on the binding mechanism and thus the resulting photocurrent density in the OER. Photoelectrochemical activities, structural properties as well as the electrode structure are correlated in order to discuss the history of the semiconductor and its photoelectrochemical properties evoked in the CGS process. Surface photovoltage measurements are considered to analyze the charge carrier dynamics in the porous TiO2 film. For the gas carrier nitrogen, beneficial conditions for the particle to particle and particle to substrate coupling are provided due to the sufficient temperature and velocity of the particles
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".