Manufacturing cost modeling for flexible organic solar cells
Bibliographic record
Abstract
Solar energy is an abundant source of renewable energy. With increasing demand for energy generation to meet rising energy needs, there is immense interest in electricity generation from solar power using photovoltaic cells (PV). While conventional silicon PVs (Si-PVs) dominate the current solar PV market, wide adoption is limited mainly due to the high cost of silicon and related processing. In contrast, emerging technologies such as organic material based PVs can be fabricated as thin flexible sheets using conventional printing techniques, and have the potential of saving significant materials and costs as well as reducing environmental impact. Despite having limitations in power conversion efficiencies, OPVs have the potential to displace traditional Si-PVs and enable new market applications, and it is therefore worthwhile to understand their production economics. This paper presents a technical-economic cost model (TCM) analysis based on three manufacturing processes defined by IDME Technologies Corporation (IDME). The TCM was used to investigate the manufacturing cost of scaling up production of OPVs to three different annual production volumes, and to make recommendations for production scale-up. The findings suggest that an automated semi-continuous process is the most suitable manufacturing process for the widest range of production volumes in a cost-effective manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".