Upconversion Lanthanide‐Based 2D Metal‐Organic Frameworks for Multimode Information Encryption
Bibliographic record
Abstract
Abstract Luminescent 2D metal‐organic frameworks (MOFs) are a class of metal‐organic framework materials expanded in a 2D plane, which have a wide range of applications in fields of optoelectronic devices, sensors, and information storage due to their unique layered structure and excellent optical properties. Currently, research on luminescent 2D MOFs mainly focuses on down‐shifting luminescence, while the exploration of upconversion luminescence remains in its early stages. Herein, a novel 2D Yb‐PMA MOFs were synthesized, and red upconversion luminescence at 660 nm under 980 nm excitation was successfully achieved by introducing Ho 3+ as the luminescence center and using Yb 3+ as the sensitizer. In addition, multimode emitting MOFs with both upconversion and down‐shifting luminescence were further constructed by codoping Tb 3+ or Eu 3+ . Inspired by the layered structure of 2D materials, multilayer stacked 2D MOFs composites were prepared by ultrasonic exfoliation method and upconversion luminescence was realized for the first time by interfacial energy transfer between different components. This strategy not only expands the optical modulation of luminescent 2D MOFs, but also provides new ideas for the construction of multifunctional luminescent materials. The upconversion/down‐shifting luminescent lanthanide‐based 2D MOFs designed in this work show good potential for application in the field of information encryption.
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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) | 0.002 | 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".