ArticleACS omega2020
Estimating Nanoscale Surface Roughness of Polyethylene Terephthalate Fibers.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Research and Analysis on Enhancement of Surface Flashover Performance of Epoxy Resin Based on Dielectric Barrier Discharge Plasma Fluorination Modification.Nanomaterials (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitation of surface roughness is difficult, if subtle, but significant differences cause an uncommon variance. We used atomic force microscopy to measure the surface roughness of polyethylene terephthalate (PET) fibers before and after a 30 s plasma treatment of 300 W. Samples were measured multiple times at different locations, in four scan sizes. The surface roughness was expressed in terms of nine roughness parameters. Despite the large number of data, simple statistics was not able to detect significant differences in roughness before and after plasma treatment. A factorial analysis of variance (ANOVA) of the normalized data and a sum of ranking differences analysis using four types of data preprocessing and their factorial ANOVA confirmed that (i) the plasma treatment had roughened the PET fiber surface; (ii) the roughness increases with the scanned area in the measured range; and (iii) what the best roughness parameters are in discriminating between surfaces before and after treatment. Although the compared roughness estimators were on different scales, a roughness estimation of the nanoscale surfaces was feasible, where other methods fail. The presented methodology can be applied widely and unambiguously for highly different method comparison tasks.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.