Simultaneous Enhancement of Near-Infrared Emission and Dye Photodegradation in a Racemic Aspartic Acid Compound via Metal-Ion Modification
Bibliographic record
Abstract
Abstract Changing functionalities of materials using simple methods is an active area of research, as it is “green” and lowers the developing cost of new products for the enterprises. A new small molecule racemic N,N-dimethyl aspartic acid has been prepared. Its structure is determined by single-crystal X-ray diffraction. It is characterized by FTIR, XPS, 1H NMR, and mass spectroscopy. Its near-infrared luminescence can be enhanced by the combination of metal ions, including Dy3+, Gd3+, Nd3+, Er3+, Sr3+, Y3+, Zn2+, Zr4+, Ho3+, Yb3+, La3+, Pr6+/Pr3+, and Sm3+ ions. An optical chemistry mechanism upon interaction between the sensitizer and activator is proposed. Furthermore, the association of Ca2+, Sr2+, or Zr4+ ions to the molecule enhanced its photodegradation for dyes under white-light irradiation. Specifically, rhodamine 6G can be degraded by the Ca2+-modified molecule; rhodamine B, rhodamine 6G, and fluorescein sodium salt can be degraded by the Sr2+- or Zr4+-modified molecule. This surprising development opens a way in simultaneously increasing NIR luminescence and the ability of dye photodegradation for the investigated molecule.
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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".