Modelling and optimizing the gas-lift effect system for texturing silicon
Bibliographic record
Abstract
Modelling and simulation techniques were used to understand the fluid flow patterns inside the silicon texturing system operating under the gas-lift effect (GLE) for solar cell applications. Experiments were performed which confirmed the validity of the simulation model and the simulated results. It was determined that due to the original non-optimal inlet, the fluid flow pattern and the non-uniform fluid velocity distribution inside the system resulted in the generation of lower velocity regions on the surface of the textured silicon. The simulation tool validated the correlation of the lower fluid velocity with the reduced surface coverage, uniformity and subsequent less optimal surface reflectivity. Various inlet designs were modelled and evaluated for optimal performance. The best-case inlet design was fabricated and tested resulting in the validation of the simulation work and significant improvement in the GLE texturing system performance. With the new inlet design, as the fluid velocity reaches and goes beyond some critical value (in this case 0.047 m s −1 ) in areas that had shown lower velocity in the original inlet design (mainly close to the inlet and side walls), the observed morphology and surface reflectivity improve significantly with values very similar to those of the higher velocity areas.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".