Why does explaining help learning? Insight from an explanation impairment effect
Bibliographic record
Abstract
Luminescent solar concentrators (LSCs) appear as candidates to enhance the performance of photovoltaic (PV) cells and contribute to reduce the size of PV systems, decreasing, therefore, the amount of material needed and thus the cost associated with energy conversion. One way to maximize the device performance is to explore near-infrared (NIR)-emitting centers, resonant with the maximum optical response of the most common Si-based PV cells. Nevertheless, very few examples in the literature demonstrate the feasibility of fabricating LSCs emitting in the NIR region. In this work, NIR-emitting LSCs are reported using silicon 2,3-naphthalocyanine bis(trihexylsilyloxide) (SiNc or NIR775) immobilized in an organic-inorganic tri-ureasil matrix (t-U(5000)). The photophysical properties of the SiNc dye incorporated into the tri-ureasil host closely resembled those of SiNc in tetrahydrofuran solution (an absolute emission quantum yield of ∼0.17 and a fluorescence lifetime of ∼3.6 ns). The LSC coupled to a Si-based PV device revealed an optical conversion efficiency of ∼1.5%, which is among the largest values known in the literature for NIR-emitting LSCs. The LSCs were posteriorly coupled to a Si-based commercial PV cell, and the synergy between the t-U(5000) and SiNc molecules enabled an effective increase in the external quantum efficiency of PV cells, exceeding 20% in the SiNc absorption region.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".