Dataset Information

Molecular Weight Enables Fine-Tuning the Thermal and Dielectric Properties of Polymethacrylates Bearing Sulfonyl and Nitrile Groups as Dipolar Entities.

ABSTRACT: In this work, polymethacrylates containing sulfonyl and nitrile functional groups were successfully prepared by conventional radical polymerization and reversible addition-fragmentation chain-transfer polymerization (RAFT). The thermal and dielectric properties were evaluated, for the first time, considering differences in their molecular weights and dispersity values. Variations of the aforementioned properties do not seem to substantially affect the polarized state of these materials, defined in terms of the parameters ?'_r, ?"_r and tan (?). However, the earlier appearance of dissipative phenomena on the temperature scale for materials with lower molecular weights or broader molecular weight distributions, narrows the range of working temperatures in which they exhibit high dielectric constants along with low loss factors. Notwithstanding the above, as all polymers showed, at room temperature, ?'_r values above 9 and loss factors below 0.02, presenting higher dielectric performance when compared to conventional polymer materials, they could be considered as good candidates for energy storage applications.

SUBMITTER: Bonardd S

PROVIDER: S-EPMC7863962 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Molecular Weight Enables Fine-Tuning the Thermal and Dielectric Properties of Polymethacrylates Bearing Sulfonyl and Nitrile Groups as Dipolar Entities.

Bonardd Sebastian S Saldías Cesar C Leiva Ángel Á Díaz Díaz David D Kortaberria Galder G

Polymers 20210120 3

In this work, polymethacrylates containing sulfonyl and nitrile functional groups were successfully prepared by conventional radical polymerization and reversible addition-fragmentation chain-transfer polymerization (RAFT). The thermal and dielectric properties were evaluated, for the first time, considering differences in their molecular weights and dispersity values. Variations of the aforementioned properties do not seem to substantially affect the polarized state of these materials, defined ...[more]

PMID: 33498200

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Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

Molecular Weight Enables Fine-Tuning the Thermal and Dielectric Properties of Polymethacrylates Bearing Sulfonyl and Nitrile Groups as Dipolar Entities.

Publications

Molecular Weight Enables Fine-Tuning the Thermal and Dielectric Properties of Polymethacrylates Bearing Sulfonyl and Nitrile Groups as Dipolar Entities.

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