Sustainable Triplet‐State Engineering in Cotton‐Derived Carbon Dots: Mg‐Based Matrices Enable Multicolor Room‐Temperature Phosphorescence
Bibliographic record
Abstract
Abstract Sustainable production of color‐tunable, room‐temperature phosphorescent (RTP) carbon dots (CDs) from abundant biomass sources presents significant scientific and technological challenges. Here, a facile strategy is presented for fabricating multicolor RTP CDs through controlled thermal treatment of natural cotton in Mg(NO 3 ) 2 ·6H 2 O. By precisely controlling the calcination temperature at 300–500 °C, three distinct RTP materials are obtained: yellow‐emitting CDs@Mg(NO 3 ) 2 /Mg 3 (OH) 4 (NO 3 ) 2 ‐300, cyan‐emitting CDs@MgO‐400, and blue‐emitting CDs@MgO‐500. Systematic investigations reveal that the emission color is governed by the relative contributions of carbon core states and surface functional groups, which can be modulated by the calcination temperature. The rigid Mg‐based matrices provide spatial confinement and form covalent/hydrogen bonds with CDs, enabling efficient intersystem crossing and suppressing non‐radiative decay pathways. The resulting materials exhibit exceptional RTP performance, including long lifetimes of up to 483 ms, high phosphorescence quantum yields reaching 12.4%, and remarkable stability under various conditions. Leveraging their unique excitation‐dependent emission and time‐resolved decay characteristics, sophisticated applications of these materials are demonstrated in multilevel data encryption, advanced anti‐counterfeiting, and dynamic password systems. This work not only provides fundamental insights into triplet‐state engineering of CDs but also establishes a sustainable platform for designing next‐generation optical materials with potential applications in security, displays, and beyond.
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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".