Photo-luminescent quantum dots used for security identification
Bibliographic record
Abstract
Information retrieval is critical in security technologies such as those for status identification and documentation authentication. Ideally, coding materials should be difficult to locate, impossible to counterfeit, and easy to process. This presentation addresses a novel information retrieval technology with these ideal features of its coding materials: the photo-luminescent (PL) quantum-dots (QD) synthesized via wet-chemistry approaches. As compared to traditional PL materials, they exhibit emission with narrower full width at half maximum, greater brightness, and higher photo-stability; also, their PL wavelength can be easily and accurately tuned via their size, structure, and composition. Due to such a feasible tune-ability, mainly, QDs have demonstrated enormous potential applications in security and defense. When QDs are excited, they can provide coded information with their PL wavelength and intensity. If the coding wavelengths from the QD PL are designed as the Fraunhofer lines, i.e. black lines in solar spectrum, the retrieval system can extract the useful information even under sunshine covering areas. Multi-photon excitation (MPE) technologies can further extend applications of QDs to multi-layer information extraction. For an info-label of 2-millimeter in depth, a MPE system with the depth resolution less than one micro-meter can thus achieve 2 GB resolutions, when a coding material exhibiting 6 PL wavelengths with 10 intensity levels. In general, transparent thin-film coating of QDs can be applied to various substrates, such as documents, fingernails, and military helmets and vehicles. Moreover, QD based security information can be easily destroyed by preset expiration in the presence of timing agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".