Multidimensional Integration of Reversible Dynamic Information Enables Advanced Anticounterfeiting
Bibliographic record
Abstract
Stimuli-responsive polymers have been widely used to diversify anticounterfeiting technologies. However, creating multilevel anticounterfeiting strategies using these polymers remains challenging due to the compatibility and mutual interference between different responsive signals, resulting in a low information resolution. Herein, we propose a strategy to enhance anticounterfeiting capacity by dimensionally integrating dynamic information and leveraging the orthogonal, non-interfering multistimuli responses of functional molecules. Specifically, distinct stimuli-responsive polymers were integrated into a module matrix, enabling multilevel information storage. At the molecular scale, diverse functional molecules are incorporated into polymer backbones to augment multicolored changes under different external stimuli. From the orthogonal combination of several types of stimuli-responsive polymers, this strategy enables the preparation of anticounterfeiting labels and QR codes with multilevel encryption and decryption capability. Altering the type and sequence of external stimuli allows these labels and QR codes to display dynamic information via a combined color change, thereby expanding the information storage capacity and enhancing anticounterfeiting levels. This multidimensional integration strategy of distinct stimuli-responsive polymers opens opportunities for high-security anticounterfeiting applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".