Are Substitutes to Cd-Based Quantum Dots in Displays More Sustainable, Effective, and Cost Competitive? An Alternatives Assessment Approach
Bibliographic record
Abstract
Light emissive organics and inorganic nanoparticles are substance classes competing for applications in displays in the form of organic light emitting diodes (OLEDs) and quantum LEDs (QLEDs), respectively. Upcoming substance classes, perovskites and Q-OLED displays, also contain novel nanomaterials and organics for these applications. However, the safety and viability of these emissive substances is difficult to assess quickly and broadly because of their complexity, their inherently different structures, and their rapid evolution in the literature. We propose the use of an alternatives assessment focusing on hazard, cost, and performance, so as to compare these possible substitute substances, with incombent cadmium-containing quantum dots. This assessment type is used in industry and government to inform chemical substitution. It uses available information, while pointing out important data gaps for decision-making. The cost assessment highlights competitiveness of OLEDs because of the low amounts needed in their application for display, but performance assessments do not identify a preferred alternative. The hazard results indicate that there is no clear alternative either, with each novel nanomaterial or organic substance having different negative aspects. These results identify the need for a low-hazard high-performing alternative substance, and the assessment provides a framework for researchers to evaluate their own novel substances.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".