CdTe in thin film photovoltaic cells: Interventions to protect drinking water in production and end-of-life
Bibliographic record
Abstract
Solar energy harvesting is a crucial technology in the transition away from fossil fuels. However, in order to make a renewable energy source truly sustainable, it is necessary to understand and mitigate broader impacts. At the Water-Energy Nexus lies the question of trade-offs between energy sources in terms of their water footprint, through water use or water contamination. The purpose of this work is to analyze CdTe thin film photovoltaic cells to evaluate interventions that can prevent contamination of drinking water. We focus on drinking water because of its relevance to the United Nation’s Sustainable Development Goal 6: clean water and sanitation. Thin-film PV cells use CdTe as a semiconductor material because of its advantageous band gap and high solar absorption efficiency. However, CdTe as well as cadmium and tellurium species can be toxic to aquatic and terrestrial ecosystems and pose serious health hazards to humans when present in drinking water. We propose a multiple criteria decision analysis (MCDA) that can be used by business leaders and politicians to aid in decision-making in regards to new interventions to protect drinking water. In this article we use a case study to demonstrate the use of the MCDA framework. The interventions analyzed in this review are regulation of recycling and disposal, bioreactors, and dye-sensitized solar cells. Protecting water supplies while increasing access to reliable electricity through low-cost solar is a critical path to meeting the UN Sustainable Development Goals as this renewable energy technology evolves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".