Robust, Conformal ZnO Coatings on Fabrics via Atmospheric‐Pressure Spatial Atomic Layer Deposition with In‐Situ Thickness Control**
Bibliographic record
Abstract
Abstract Zinc oxide (ZnO) is a promising material for functionalization of textiles. It can add a range of functionalities, including UV protection, antimicrobial activity, flame retardancy, hydrophobicity and electrical conductivity. Commercialization of ZnO – coated textiles is still limited due to the cost and challenges related to their manufacture. Moreover, making robust coatings on textiles and measuring their thickness is also challenging. In this work, atmospheric‐pressure spatial atomic layer deposition (AP‐SALD) systems are utilized for the first time to coat synthetic spun‐bond polypropylene (PP) and natural cotton fabrics with ZnO. The coatings are found to be conformal and uniform, forming complete shells around the fabric fibers. The growth rate is measured to be ∼0.24 nm/cycle using an in‐situ reflectance setup and Virtual Interface (VI) model, which enable precise control of the coating thickness. The coatings are shown to provide UV‐protection and render cotton fabric hydrophobic. No damage is observed after washing, linear abrasion, adhesion, twisting and bending tests, indicating that the coatings are robust. Aerosol‐penetration tests indicate the coatings do not impact the filtering efficiency of fabrics used in N95 respirators. The results are encouraging for industrialization of the AP‐SALD technique for functional textiles.
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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 | 0.000 | 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".