Diffusion, viscosity, and cycle times for high aspect ratio glass nanoimprinting
Bibliographic record
Abstract
Antireflective (AR) surfaces are critical developments for electronic displays, solar panels, LED/OLED lighting, and lenses. Biomimetic moth-eye nanostructures offer significant reductions in glass reflection across the visible and near-IR spectrum and are self-cleaning due to their hydrophobic nature. However, current AR glass surfaces rely on clean-room-based manufacturing processes, which are costly, have limited throughput, and restrict the patternable surface area.Thermal nanoimprinting has emerged as a promising alternative for large-scale, cost-effective AR patterning on glass. However, the literature pertaining to high transition temperature glass nanoimprinting is limited to shallow aspect ratios due mainly to interdiffusion between the mould and substrate. Furthermore, the fundamental relationship between temperature, pressure, and feature-scale on glass flow remains insufficiently explored.To study high aspect ratio glass nanoimprinting, high-temperature glass nanoimprinting equipment and moulds were developed. Experiments were conducted on borosilicate and soda-lime glass using various moulds with large aspect ratios and were compared to numerical models to elucidate the effect of the process parameters on the cycle time, repeatability and interdiffusion between mould and substrate.Findings indicate that as the mould feature scale decreases, higher temperatures are required to ensure adequate material flow and pattern replication. However, at elevated temperatures, diffusion rates increase, leading to higher fracture risks and interfacial degradation. Comparative analysis between borosilicate and soda-lime glass highlights key material-dependent factors that influence the process.By integrating experimental data with numerical modeling, this study offers insights into the underlying physics governing glass nanoimprinting. The findings contribute to the development of process optimization strategies that can facilitate industrial-scale production. Future work will focus on extending the model to various glasses, ensuring scalable high throughput glass nanoimprinting for diverse glass compositions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".