Dual Reversible Network Nanoarchitectonics for Ultrafast Light-Controlled Healable and Tough Polydimethylsiloxane-Based Composite Elastomers

It is highly desirable to develop polydimethylsiloxane (PDMS) elastomers with high self-healing efficiency and excellent mechanical properties. However, most self-healable materials reported to date still take several hours to self-heal and improving the self-healing property often comes at the expense of mechanical properties. Herein, a simple design strategy of dual reversible network nanoarchitectonics is reported for constructing ultrafast light-controlled healable (40 s) and tough (≈7.2 MJ m–3) PDMS-based composite elastomers. The rupture reconstruction of dynamic bonds and the reinforcement effect of carbon nanotubes (10 wt %) endowed our composite elastomer with excellent fracture toughness that originated from a good yield strength (≈1.1 MPa) and stretchability (≈882%). Moreover, carbon nanotubes can quickly and directly heat the damaged area of the composite to achieve its ultrafast repair with the assistance of dynamic polymer/filler interfacial interaction, greatly shortening the self-healing time (12 h). The self-healing performance is superior to that of reported self-healable PDMS-based materials. This novel strategy and the as-prepared supramolecular elastomer can inspire further various practical applications, such as remote anti-icing/deicing materials

Accurate Prediction of Immunogenic T-Cell Epitopes from Epitope Sequences Using the Genetic Algorithm-Based Ensemble Learning

<div><p>Background</p><p>T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.</p><p>Methods</p><p>In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set.</p><p>Results</p><p>Two datasets named ‘IMMA2’ and ‘PAAQD’ are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant.</p><p>Conclusions</p><p>The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128194#pone.0128194.s001" target="_blank">S1 File</a>.</p></div

Dual Reversible Network Nanoarchitectonics for Ultrafast Light-Controlled Healable and Tough Polydimethylsiloxane-Based Composite Elastomers

It is highly desirable to develop polydimethylsiloxane (PDMS) elastomers with high self-healing efficiency and excellent mechanical properties. However, most self-healable materials reported to date still take several hours to self-heal and improving the self-healing property often comes at the expense of mechanical properties. Herein, a simple design strategy of dual reversible network nanoarchitectonics is reported for constructing ultrafast light-controlled healable (40 s) and tough (≈7.2 MJ m–3) PDMS-based composite elastomers. The rupture reconstruction of dynamic bonds and the reinforcement effect of carbon nanotubes (10 wt %) endowed our composite elastomer with excellent fracture toughness that originated from a good yield strength (≈1.1 MPa) and stretchability (≈882%). Moreover, carbon nanotubes can quickly and directly heat the damaged area of the composite to achieve its ultrafast repair with the assistance of dynamic polymer/filler interfacial interaction, greatly shortening the self-healing time (12 h). The self-healing performance is superior to that of reported self-healable PDMS-based materials. This novel strategy and the as-prepared supramolecular elastomer can inspire further various practical applications, such as remote anti-icing/deicing materials

The average performances of models merging different feature vectors, evaluated by 20 independent runs of the 10-CV.

<p>The average performances of models merging different feature vectors, evaluated by 20 independent runs of the 10-CV.</p

The average performances of GA-based ensemble method on benchmark datasets, evaluated by 20 runs of 10-CV.

<p>The average performances of GA-based ensemble method on benchmark datasets, evaluated by 20 runs of 10-CV.</p

Soft, Tough, and Thermally Conductive Elastomer Composites by Constructing a Curled Conformation

Filled elastomer composites have gained significant attention due to their ability to undergo large-strain reversible deformations with minimal force. However, achieving the desired functionality, such as high thermal conductivity, often requires ultrahigh filler loadings (above 50%). Unfortunately, excessive filler loading compromises the softness and toughness of the composites due to the prevalence of trapped entanglements. To address this challenge, a simple solvent-thermal design strategy is reported to optimize the balance among Young’s modulus, stretchability, and toughness in highly filled elastomer composites. This is realized by the curled conformation formed by the disentangling of the excessively entangled polymer chains and by better mixing of the BN filler and the polymer matrix. The released trapped entanglement can effectively reduce the Young’s modulus (2.80 MPa) of the C-PDMS/60 wt % BN elastomer composites, and the strong unfolding and stretching ability of the curled conformation also endows it with excellent stretchability (∼492%), thus achieving high toughness (∼2.80 MJ m–3). Additionally, the better mixing ability allows the C-PDMS/60 wt % BN elastomer composites to be compounded with the high BN filler loading (60 wt %), thus achieving high thermal conductivity (1.65 W m–1 K–1). The comprehensive performance of the C-PDMS/60 wt % BN demonstrates remarkable advancements in highly filled elastomer composites. Leveraging these favorable characteristics, the curled PDMS/BN elastomer composites can serve as effective thermal interface materials for efficient heat dissipation and hold great potential for applications in the field of flexible electronics

The average performances of different individual feature-based models, evaluated on IMMA2 by 20 independent runs of the 10-CV.

<p>The average performances of different individual feature-based models, evaluated on IMMA2 by 20 independent runs of the 10-CV.</p

The average AUC scores of individual feature-based models using different values for λ, evaluated on IMMA2 by 20 independent runs of the 10-CV.

<p>The average AUC scores of individual feature-based models using different values for λ, evaluated on IMMA2 by 20 independent runs of the 10-CV.</p

The statistics of improvements over benchmark methods (significance level 0.05).

<p>*N.A. means data not available.</p><p>The statistics of improvements over benchmark methods (significance level 0.05).</p