Dual Aliovalent Dopants Cu, Mn Engineered Eco‐Friendly QDs for Ultra‐Stable Anti‐Counterfeiting
Bibliographic record
Abstract
Abstract Doping in semiconductor quantum dots (QDs) using optically active dopants tailors their optical, electronic, and magnetic properties beyond what is achieved by controlling size, shape, and composition. Herein, we synergistically modulated the optical properties of eco‐friendly ZnInSe2/ZnSe core/shell QDs by incorporating Cu‐doping and Mn‐alloying into their core and shell to investigate their use in anti‐counterfeiting and information encryption. The engineered “Cu:ZnInSe2/Mn:ZnSe” core/shell QDs exhibit an intense bright orange photoluminescence (PL) emission centered at 606 nm, with better color purity than the undoped and individually doped core/shell QDs. The average PL lifetime is significantly extended to 201 ns, making it relevant for complex encryption and anti‐counterfeiting. PL studies reveal that in Cu:ZnInSe2/Mn:ZnSe, the photophysical emission arises from the Cu state via radiative transition from the Mn 4T1 state. Integration of Cu:ZnInSe2/Mn:ZnSe core/shell QDs into poly(methyl methacrylate) (PMMA) serves as versatile smart concealed luminescent inks for both writing and printing patterns. The features of these printed patterns using Cu:ZnInSe2/Mn:ZnSe core/shell QDs persisted after 10 weeks of water‐soaking and retained 70 % of PL emission intensity at 170 °C, demonstrating excellent thermal stability. This work provides an efficient approach to enhance both the emission and the stability of eco‐friendly QDs via dopant engineering for fluorescence anti‐counterfeiting applications.
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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".