Direct Photolithographic Deposition of Color‐Coded Anti‐Counterfeit Patterns with Titania Encapsulated Upconverting Nanoparticles
Bibliographic record
Abstract
Abstract Creating security labels as anti‐counterfeit measures can require multi‐step methods, clean room processing, and high‐cost equipment. Some labels also have a limited applicability due to the ease of creating a counterfeit. Herein, a photochemical metal‐organic deposition (PMOD) based approach that enables creation of high‐resolution luminescent patterns that retain nanoparticles in transparent metal oxide films is reported. This low‐cost, photoresist‐free process creates high‐resolution patterns of metal oxides without requiring processes such as etching or lift‐off. Upconverting nanoparticles (UCNPs) with tunable red/green or blue emission are prepared by doping Yb3+/Er3+ and Yb3+/Tm3+ into β‐NaYF4 hosts, respectively. Luminescent inks are prepared by suspending UCNPs in solutions with titanium di‐n‐butoxide bis(2‐ethylhexanoate). Customizable luminescent patterns are prepared by casting inks onto substrates, followed by exposure to ultraviolet light through photomasks. Photodecomposition of the titanium precursor yields amorphous oxide films encapsulating the UCNPs. Security labels are prepared by selectively patterning luminescent inks using PMOD. Distinct patterns of red‐green‐blue (RGB) luminescence are discernible only upon excitation with a near‐infrared (NIR) laser. These customizable, anti‐counterfeit labels exhibit the merits of low‐cost, high‐throughput, and simple manufacturing techniques. Yet, the versatility of customizing their emissive properties suggests a practical application as an anti‐counterfeiting measure.
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 | 0.000 |
| 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".