Emerging Photovoltaic (PV) Materials for a Low Carbon Economy
Bibliographic record
Abstract
Emerging photovoltaic (PV) technologies have a potential to address the shortcomings of today’s energy market which heavily depends on the use of fossil fuels for electricity generation. We created inventories that offer insights into the environmental impacts and cost of all the materials used in emerging PV technologies, including perovskites, polymers, Cu2ZnSnS4 (CZTS), carbon nanotubes (CNT), and quantum dots. The results show that the CO2 emissions associated with the absorber layers are much less than the CO2 emissions associated with the contact and charge selective layers. The CdS (charge selective layer) and ITO (contact layer) have the highest environmental impacts compared to Al2O3, CuI, CuSCN, MoO3, NiO, poly (3-hexylthiophene-2,5-diyl (P3HT)), phenyl-C61-butyric acid methyl ester (PCBM), poly polystyrene sulfonate (PEDOT:PSS), SnO2, spiro-OMeTAD, and TiO2 (charge selective layers) and Al, Ag, Cu, FTO, Mo, ZnO:In, and ZnO/ZnO:Al (contact layers). The cost assessments show that the organic materials, such as polymer absorbers, CNT, P3HT and spiro-OMeTAD, are the most expensive materials. Inorganic materials would be more preferable to lower the cost of solar cells. All the remaining materials have a potential to be used in the commercial PV market. Finally, we analyzed the cost of PV materials based on their material intensity and CO2 emissions, and concluded that the perovskite absorber will be the most eco-efficient material that has the lowest cost and CO2 emissions.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".