Bibliographic record
Abstract
Maintaining clarity and avoiding the accumulation of water and dirt on optically transparent surfaces such as US military vehicle windshields, viewports, periscope optical head windows, and electronic equipment cover glasses are critical to providing a high level of visibility, improved survivability, and much-needed safety for warfighters in the field. Through a combination of physical vapor deposition techniques and the exploitation of metastable phase separation in low-alkali borosilicate, a novel technology was developed for the fabrication of optically transparent, porous nanostructured silica thin film coatings that are strongly bonded to glass platforms. The nanotextured films, initially structurally superhydrophilic, exhibit superior superhydrophobicity, hence antisoiling ability, following a simple but robust modification in surface chemistry. The surfaces yield water droplet contact angles as high as 172°. Moreover, the nanostructured nature of these coatings provides increased light scattering in the UV regime and reduced reflectivity (i.e., enhanced transmission) over a broad range of the visible spectrum. In addition to these functionalities, the coatings exhibit superior mechanical resistance to abrasion and are thermally stable to temperatures approaching 500°C. The overall process technology relies on industry standard equipment and inherently scalable manufacturing processes and demands only nontoxic, naturally abundant, and inexpensive base materials. Such coatings, applied to the optical components of current and future combat equipment and military vehicles will provide a significant strategic advantage for warfighters. The inherent self-cleaning properties of such superhydrophobic coatings will also mitigate biofouling of optical windows exposed to high-humidity conditions and can help decrease repair/replacement costs, reduce maintenance, and increase readiness by limiting equipment downtime.
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".