Achieving Higher Strength and Sensitivity toward UV Light in Multifunctional Composites by Controlling the Thickness of Nanolayer on the Surface of Glass Fiber
Bibliographic record
Abstract
The interphase between fiber and matrix plays an essential role in the performance of composites. Therefore, the ability to design or modify the interphase is a key technology needed to manufacture stronger and smarter composite. Recently, depositing nanomaterials onto the surface of the fiber has become a promising approach to optimize the interphase and composites. But, the modified composites have not reached the highest strength yet, because the determining parameters, such as thickness of the nanolayer, are hardly controlled by the mentioned methods in reported works. Here, we deposit conformal ZnO nanolayer with various thicknesses onto the surfaces of glass fibers via the atomic layer deposition (ALD) method and a tremendous enhancement of interfacial shear strength of composites is achieved. Importantly, a critical thickness of ZnO nanolayer is obtained for the first time, giving rise to a maximal relative enhancement in the interfacial strength, which is more than 200% of the control fiber. In addition, the single modified fiber exhibits a potential application as a flexible, transparent, in situ UV detector in composites. And, we find the UV-sensitivity also shows a strong correlation with the thickness of ZnO. To reveal the dependence of UV-sensitivity on thickness, a depletion thickness is estimated by a proposed model which is an essential guide to design the detectors with higher sensitivity. Consequently, such precise tailoring of the interphase offers an advanced way to improve and to flexibly control various macroscopic properties of multifunctional composites of the next generation.
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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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".