Cold Gas Sprayed Semiconductor-Based Electrodes for the Photooxidation of Water
Bibliographic record
Abstract
Photoassisted water splitting for hydrogen generation requires the development of low cost, but highly efficient photoelectrodes. For the industrial hydrogen production with photoelectrochemical cells suitable catalysts and feasible photoelectrode preparation processes still need to be identified. This contribution explores the potential of cold gas spraying (CGS) for the production of photoanodes employing semiconductors for the water oxidation reaction (OER). Conventional large area coating techniques usually employ wet chemical methods with subsequent calcination steps to obtain enhanced binding between the catalyst particles and the substrate. In the cold gas spraying process, particles are accelerated to high velocities by a pressurized gas. The nitrogen used as process gas is preheated and then expanded in a De Laval type nozzle. On impact with the substrate, the particles deform and break up and thus can build an efficient interface to the back contact. For a first demonstration TiO2 aggregates delivered by EVONIK were probed for the preparation of TiO2 photoelectrodes. In photoelectrochemical experiments these cold gas sprayed TiO2 photoelectrodes showed seven times higher photocurrents in the photooxidation of water (at 1.23V(NHE)) than reference electrodes prepared by the established doctor blade technique. In systematic experiments it was observed that with increasing gas temperature in the coating process the obtained photocurrent is enhanced. The better performance can be mainly attributed to an improved bonding of the TiO2 particles to the substrate (back contact) due to their increased impact energy. Although the bulk characteristics of the particles remained unchanged incident photon to current efficiency (IPCE) and surface photovoltage measurements reveal the formation of surface-localized interband transitions at the TiO2 surface (1.2, 2 and 2.4 eV) during the CGS process. In order to study the influence of these defects on the photoreaction performance surface modification by plasma treatment is employed. For this structure-activity correlation analysis by Raman, XRD and XPS is performed. From these initial experiments the preparation is extended to other potential metal oxides (e.g. WO3, Fe2O3 and BiVO4) for the photooxidation of water. Besides the relevant particle-substrate bonding and defect chemistry of the semiconductor layer due to the process, morphology investigation by SEM, gas sorption and 3D topography are considered for the optimization of the coating technique.
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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) | 0.001 | 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".