Future of photovoltaic materials with emphasis on resource availability, economic geology, criticality, and market size/growth
Bibliographic record
Abstract
The reduction of greenhouse gas emissions depends largely on the availability of clean energy. To harness solar energy, photovoltaic (PV) materials (solar-grade silicon, germanium, gallium, indium, tellurium, selenium, and arsenic) must be available at a reasonable cost. Markets for these critical and specialty materials do not exceed 200,000 tonnes per year; however, they are subject to fast growth rates. Except for solar-grade silicon, PV materials are by-products of base and precious metal extraction. This is motivated in part by environmental and workplace regulations and the need to purify the main commodity to users’ specifications. Given favorable market conditions, any PV material can be derived from more than one deposit type. For example, germanium can be recovered as a by-product from bauxite, Mississippi Valley-type, clastic-dominated sediment-hosted zinc-lead, Kipushi-type, Apex-type, and other deposit types. The raw materials required to produce metallurgical-grade silicon (MG-Si), mainly quartzites, are available on all continents. The process is energy intensive, so the availability of abundant, inexpensive, and “clean” power is one of the key parameters in selecting future silicon metal plant sites. MG-Si is the starting material for the production of solar-grade silicon. Although no shortages of PV materials due to a lack of raw materials are expected in the short term, those linked to bottlenecks, geopolitical economic considerations, armed conflicts, natural hazards outside of human control, or commercialization of new technology are possible. The advent of the “circular economy” cannot eliminate the need to increase mine, smelter, and refinery production of PV materials.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".