Light induced silver and copper plating on silver screen-printed contacts of silicon solar cells
Bibliographic record
Abstract
Abstract In this paper we present the results of solar cell’s screen printed contacts thickening by light induced plating (LIP) of Cu and Ag. Light-induced and light-assisted plating techniques are compared. Plating bath current versus illumination intensity, the potentials of metallic anode, front and back surfaces of the solar cell were measured and analyzed for all possible plating modes. The current-voltage behaviour of cells under illumination in a plating bath for the cases of electrically insulated and non-insulated back-surface of a cell is analyzed and discussed. Finally, the exact values of current/voltage/illumination intensity for optimal depositions and a general recipe for their determination for different plating materials are given. The quality of deposited layers, state of contact-line and anti-reflective coating surface were controlled after plating by cross-section and planar-view SEM observations. The electrical properties of plated contacts and their effect on overall efficiency of solar cell were investigated by measuring contact-line resistance and dark- and light I–V solar cell measurements. The absence of short-circuits was verified by imaging with an infra-red camera.
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) | 0.000 | 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) | 0.001 | 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".