Rippling Colloidal Polyelectrolyte Complex for Customized Fingerprints with High Tactile Perception
Bibliographic record
Abstract
Fingerprints possess wide applications in personal identification, tactile perception, access control, and anti-counterfeiting. However, latent fingerprints are usually left on touched surfaces, leading to the leakage of personal information. Furthermore, tactile perception greatly decreases when fingerprints are covered by gloves. Customized fingerprints are developed to solve these issues, but it is a challenge to develop fingerprints with various customized patterns using traditional techniques due to their requiring special templates, materials, or instruments. Inspired by ripples on the lake, blowing air is used to generate surface waves on a colloidal polyelectrolyte complex, leading to vertical stratification and the accumulation of particles near the top of the film layer. As water rapidly evaporates, the viscosity of these particles significantly increases and the wave is solidified, forming fingerprint patterns. These customized fingerprints integrate functions of grasping objects, personal identification without leaving latent fingerprints and tactile perception enhancement, which can be applied in information security, anti-counterfeiting, tactile sensors, and biological engineering.
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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 | 0.001 | 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".