Near‐Infrared, Heavy Metal‐Free Colloidal “Giant” Core/Shell Quantum Dots
Bibliographic record
Abstract
Abstract “Giant” core/shell quantum dots (g‐QDs) are a promising class of materials for future optoelectronic technologies due to their superior chemical‐ and photostability compared to bare QDs and core/thin shell QDs. However, inadequate light absorption in the visible and near‐infrared (NIR) region and frequent use of toxic heavy metals (e.g., Cd and Pb) are still major challenges for most g‐QDs (e.g., CdSe/CdS) synthesized to date. The synthesis of NIR, heavy metal‐free, Zn‐treated spherical CuInSe2/CuInS2 g‐QDs is reported using the sequential cation exchange method. These g‐QDs exhibit tunable NIR optical absorption and photoluminescence (PL) properties. Transient fluorescence spectroscopy shows prolonged lifetime with increasing shell thickness, indicating the formation of quasi type‐II band alignment, which is further confirmed by simulations. As a proof‐of‐concept, as‐synthesized g‐QDs are used to sensitize TiO2 as a photoanode in a photoelectrochemical (PEC) cell, demonstrating an efficient and stable PEC system. These results pave the way toward synthesizing NIR heavy metal‐free g‐QDs, which are very promising components of future optoelectronic technologies.
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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) | 0.000 | 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 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".