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Full-Text Articles in Engineering
Raising Dielectric Permittivity Mitigates Dopant-Induced Disorder In Conjugated Polymers, Meenakshi Upadhyaya, Michael Lu-Díaz, Subhayan Samanta, Muhammad Abdullah, Keith Dusoe, Kevin R. Kittilstved, Dhandapani Venkataraman, Zlatan Akšamija
Raising Dielectric Permittivity Mitigates Dopant-Induced Disorder In Conjugated Polymers, Meenakshi Upadhyaya, Michael Lu-Díaz, Subhayan Samanta, Muhammad Abdullah, Keith Dusoe, Kevin R. Kittilstved, Dhandapani Venkataraman, Zlatan Akšamija
Electrical and Computer Engineering Faculty Publication Series
Conjugated polymers need to be doped to increase charge carrier density and reach the electrical conductivity necessary for electronic and energy applications. While doping increases carrier density, Coulomb interactions between the dopant molecules and the localized carriers are poorly screened, causing broadening and a heavy tail in the electronic density-of-states (DOS). The authors examine the effects of dopant-induced disorder on two complimentary charge transport properties of semiconducting polymers, the Seebeck coefficient and electrical conductivity, and demonstrate a way to mitigate them. Their simulations, based on a modified Gaussian disorder model with Miller-Abrahams hopping rates, show that dopant-induced broadening of the …
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Faculty of Engineering and Information Sciences - Papers: Part B
The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. …