Improving the Light Quality of White Light‐Emitting Diodes Using Cellulose Nanocrystal‐Filled Phosphors
Bibliographic record
Abstract
Light‐emitting diode (LED) lighting delivers better performance and reliability, and substantially lowers the total cost of ownership compared with conventional lighting. The most common white LED is generally produced using a blue LED chip and phosphor combination to generate white light. This type of phosphor‐converted white LED can be a great alternative to the more expensive 3 chip RGB (red, green, blue) LED. Herein, cellulose nanocrystals, a wood‐derived biopolymer, are used with phosphor to improve the uniformity of correlated color temperature (CCT) and luminous flux from the white LED. These nanocrystals can scatter light strongly and for an optimized concentration of nanocrystals, it is found to increase the luminous flux of the white LED by over 30% compared with the reference sample without any nanocrystal. The CCT uniformity is also improved from 173.45 K for the reference sample to 59 K for the optimized sample. The chromaticity coordinates are also studied and found to be shifting toward lower correlated color temperatures with increasing cellulose concentrations. Combining these results with low cost, wide availability, and environmental impact, cellulose nanocrystals can play an important role in the future generation of white LEDs.
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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) | 0.000 | 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) | 0.001 | 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".