Chemical bath process for highly efficient Cd-free chalcopyrite thin-film-based solar cells
Bibliographic record
Abstract
The highest efficiency for Cu(Ga,In)Se 2 (CIGS) thin-film-based solar cells has been achieved with CdS buffer layers prepared by a solution growth method known as the chemical bath deposition (CBD). With the aim of developing Cd-free chalcopyrite-based thin-film solar cells, we describe the basic concepts involved in the CBD technique. The recipes developed in our laboratory for the heterogeneous deposition of good-quality thin films of ZnO, ZnSe, and MnS are presented. In view of device optimization, the initial formation of chemical-bath-deposited ZnSe thin films on Cu(Ga,In)(S,Se) 2 (CIGSS) and the subsequent development of the ZnSe/CIGSS heterojunctions were investigated by X-ray photoelectron spectroscopy (XPS). The good surface coverage was controlled by measuring changes in the valence-band electronic structure as well as changes in the In4d, Zn3d core lines. From these measurements, the growth rate was found to be around 3.6 nm/min. The valence band and the conduction band-offsets ΔE V and ΔE C between the layers were determined to be 0.60 and 1.27 eV, respectively for the CIGSS/ZnSe interface. The energy-band diagram is discussed in connection with the band-offsets detemined from XPS data. A ZnSe thickness below 10 nm has been found to be optimum for achieving a homogeneous and compact buffer layer on CIGSS with a total area efficiency of 13.7%.PACS No.: 42.70
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".