Linking Surface Fractality and Roughness to Seebeck Response in SWCNT‐Reinforced Polyester Composites

In this study, pure polyester and 2 wt.% single‐walled carbon nanotube (SWCNT)‐reinforced composites were fabricated to elucidate the relationship between surface heterogeneity, fractal morphology, and thermoelectric (TE) performance. Scanning electron microscopy images were digitized and quantitatively analyzed using fractal and statistical approaches to determine fractal dimension, surface coverage ratio, cluster density, and roughness parameters. The incorporation of SWCNTs resulted in a statistically significant increase in fractal dimension from ≈1.84–1.88, accompanied by enhanced surface coverage and cluster density, indicating increased morphological complexity and network formation. Carbon nanotube (CNT) addition also led to higher surface roughness, reflecting the development of a spatially interconnected nanotube structure. TE measurements revealed positive Seebeck coefficients, confirming p‐type conduction behavior. Although the Seebeck coefficient decreased with increasing electrical conductivity in the percolative regime, the power factor improved due to the dominant contribution of enhanced charge transport. These results demonstrate that CNT‐induced surface heterogeneity and fractal network evolution play a decisive role in modulating electrical and thermoelectric performance in polyester‐based composites.

Authors

Institutions

Publication Details

Journal
physica status solidi (a)
Published
2026-04-15
DOI
https://doi.org/10.1002/pssa.70341
Primary Topic
Carbon Nanotubes in Composites
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linking Surface Fractality and Roughness to Seebeck Response in SWCNT‐Reinforced Polyester Composites

Mehmet Bayırlı, Aykut Ilgaz, Feti Gülcü
physica status solidi (a)
Carbon Nanotubes in Composites
article

Linking Surface Fractality and Roughness to Seebeck Response in SWCNT‐Reinforced Polyester Composites

Mehmet Bayırlı, Aykut Ilgaz, Feti Gülcü
article en

Abstract

In this study, pure polyester and 2 wt.% single‐walled carbon nanotube (SWCNT)‐reinforced composites were fabricated to elucidate the relationship between surface heterogeneity, fractal morphology, and thermoelectric (TE) performance. Scanning electron microscopy images were digitized and quantitatively analyzed using fractal and statistical approaches to determine fractal dimension, surface coverage ratio, cluster density, and roughness parameters. The incorporation of SWCNTs resulted in a statistically significant increase in fractal dimension from ≈1.84–1.88, accompanied by enhanced surface coverage and cluster density, indicating increased morphological complexity and network formation. Carbon nanotube (CNT) addition also led to higher surface roughness, reflecting the development of a spatially interconnected nanotube structure. TE measurements revealed positive Seebeck coefficients, confirming p‐type conduction behavior. Although the Seebeck coefficient decreased with increasing electrical conductivity in the percolative regime, the power factor improved due to the dominant contribution of enhanced charge transport. These results demonstrate that CNT‐induced surface heterogeneity and fractal network evolution play a decisive role in modulating electrical and thermoelectric performance in polyester‐based composites.

physica status solidi (a)Vol. 223(8)
Small and Medium Enterprises Development Organization (TR), Hacettepe University (TR)
Openalex Percentile: Top 14%
Carbon Nanotubes in Composites
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.