Dataset Information

Higher-Order Structure in Amorphous Poly(ethylene terephthalate)/Graphene Nanocomposites and Its Correlation with Bulk Mechanical Properties.

ABSTRACT: Graphene of two different aspect ratios, A f, was melt mixed with poly(ethylene terephthalate) (PET) to form amorphous PET/graphene composites with less than 5% crystallinity. The higher-order structure and mechanical properties of poly(ethylene terephthalate) (PET) in these composites were investigated using techniques such as differential scanning calorimetry and dynamic mechanical analysis, whereas transmission electron microscopy, melt rheology, and electrical conductivity were used to study the graphene dispersion. A decrease in heat capacity changes, ?C p, of PET in nanocomposites at the glass transition temperature, T g, without T g change suggests that a rigid amorphous fraction (RAF) of PET was formed at the PET/graphene interface. The stiffening effect of graphene below 1 wt % loading is quite small in the glassy state region and independent of the A f of graphene. Above 2 wt %, graphene forms a mechanical percolation network with the RAF of PET and the PET chains are geometrically restricted by the incorporation of graphene with a high A f, resulting in an unexpectedly higher modulus of nanocomposites both below and above T g.

SUBMITTER: Aoyama S

PROVIDER: S-EPMC6648142 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Higher-Order Structure in Amorphous Poly(ethylene terephthalate)/Graphene Nanocomposites and Its Correlation with Bulk Mechanical Properties.

Aoyama Shigeru S Ismail Issam I Park Yong Tae YT Macosko Christopher W CW Ougizawa Toshiaki T

ACS omega 20190115 1

Graphene of two different aspect ratios, <i>A</i> <sub>f</sub>, was melt mixed with poly(ethylene terephthalate) (PET) to form amorphous PET/graphene composites with less than 5% crystallinity. The higher-order structure and mechanical properties of poly(ethylene terephthalate) (PET) in these composites were investigated using techniques such as differential scanning calorimetry and dynamic mechanical analysis, whereas transmission electron microscopy, melt rheology, and electrical conductivity ...[more]

PMID: 31459396

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Project description:Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.

Dataset Information

Higher-Order Structure in Amorphous Poly(ethylene terephthalate)/Graphene Nanocomposites and Its Correlation with Bulk Mechanical Properties.

Publications

Higher-Order Structure in Amorphous Poly(ethylene terephthalate)/Graphene Nanocomposites and Its Correlation with Bulk Mechanical Properties.

Similar Datasets

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