Bibliographic record
Abstract
In this chapter we provide the fundamentals of solar cell operations. We outline basic physical principles, summarize a model based on equivalent circuit and discuss ways to increase efficiency of single-junction solar cell by employing multijunction structures or by creating intermediate bands. General introductory books describing solar cells are [1], [2] and [3]. Anderson [1] and Rabl [2] provide a practical background in solar-energy conversion including techniques for estimating the solar radiation incident upon a collector, fundamentals of optics for solar collectors, discussion of types of concentrators and economical analysis, among others. Moeller [3] concentrates on fundamental, physical and material aspects of semiconductors for photovoltaic energy conversion. Introduction World consumption of electric energy circa 2009 was around 12–13 TW [4]. This energy was created by several methods. Let us look briefly at two of them: nuclear power and solar energy. Assuming that a single nuclear plant produces 1 GW of power, creation of 10 TW of power would require 10 000 nuclear power plants. This huge number will certainly create social problems. Also, the uranium needed for those plants could be diminished in less than 20 years. As far as the solar energy is concerned, the Sun supplies more solar energy on Earth in one hour than we use globally in one year. To make another comparison, a football field covered with silicon solar cells with an efficiency around 17% at 1-Sun illumination produces approximately 500 kW of power.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.055 |
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".