CdTe in thin film photovoltaic cells: Interventions to protect drinking water in production and end-of-life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it