Thermal degradation and flame retardancy of nylon 6/aluminum methylmethoxy phosphonate composites.

An aluminum methylmethoxyphosphonate (AlPo)-based flame retardant (FR) was synthesized. Thermal degradation and flame retardancy of nylon 6 (PA6)/AlPo composites were examined and compared with PA6/commercial aluminum diethylphosphinate (AlPi) composites. The PA6/AlPo composite achieved a V-0 rating at 20 wt% loading during the UL-94 test, and it exhibited the formation of a charred layer that protected the polymer from burning and reduced the release of gases during the combustion of PA6. AlPo demonstrated exceptional performance in gaseous and condensed phases in the PA6 matrix, whereas AlPi only worked in the gaseous phase. The differences between the thermal degradation mechanisms and flame retardancies of AlPi and AlPo were investigated via Fourier-transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and cone calorimetry. A suitable degradation mechanism was proposed to aid the development of flame retardants in the future.

Spin-Selective Hole-Exciton Coupling in a V-Doped WSe2 Ferromagnetic Semiconductor at Room Temperature.

While valley polarization with strong Zeeman splitting is the most prominent characteristic of two-dimensional (2D) transition metal dichalcogenide (TMD) semiconductors under magnetic fields, enhancement of the Zeeman splitting has been demonstrated by incorporating magnetic dopants into the host materials. Unlike Fe, Mn, and Co, V is a distinctive dopant for ferromagnetic semiconducting properties at room temperature with large Zeeman shifting of band edges. Nevertheless, little known is the excitons interacting with spin-polarized carriers in V-doped TMDs. Here, we report anomalous circularly polarized photoluminescence (CPL) in a V-doped WSe2 monolayer at room temperature. Excitons couple to V-induced spin-polarized holes to generate spin-selective positive trions, leading to differences in the populations of neutral excitons and trions between left and right CPL. Using transient absorption spectroscopy, we elucidate the origin of excitons and trions that are inherently distinct for defect-mediated and impurity-mediated trions. Ferromagnetic characteristics are further confirmed by the significant Zeeman splitting of nanodiamonds deposited on the V-doped WSe2 monolayer.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors.

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.

BACKGROUND: Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms. RESULTS: We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo(+) and EpicCapo(+REF). Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo(+) and EpicCapo(+REF) outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo(+REF) was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments. CONCLUSIONS: Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo(+REF) is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.