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Deciphering Molecular Mechanisms of Interface Buildup and Stability in Porous Si/Eumelanin Hybrids.

ABSTRACT: Porous Si/eumelanin hybrids are a novel class of organic-inorganic hybrid materials that hold considerable promise for photovoltaic applications. Current progress toward device setup is, however, hindered by photocurrent stability issues, which require a detailed understanding of the mechanisms underlying the buildup and consolidation of the eumelanin-silicon interface. Herein we report an integrated experimental and computational study aimed at probing interface stability via surface modification and eumelanin manipulation, and at modeling the organic-inorganic interface via formation of a 5,6-dihydroxyindole (DHI) tetramer and its adhesion to silicon. The results indicated that mild silicon oxidation increases photocurrent stability via enhancement of the DHI-surface interaction, and that higher oxidation states in DHI oligomers create more favorable conditions for the efficient adhesion of growing eumelanin.

SUBMITTER: Pinna E

PROVIDER: S-EPMC5536055 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Deciphering Molecular Mechanisms of Interface Buildup and Stability in Porous Si/Eumelanin Hybrids.

Pinna Elisa E Melis Claudio C Antidormi Aleandro A Cardia Roberto R Sechi Elisa E Cappellini Giancarlo G d'Ischia Marco M Colombo Luciano L Mula Guido G

International journal of molecular sciences 20170719 7

Porous Si/eumelanin hybrids are a novel class of organic-inorganic hybrid materials that hold considerable promise for photovoltaic applications. Current progress toward device setup is, however, hindered by photocurrent stability issues, which require a detailed understanding of the mechanisms underlying the buildup and consolidation of the eumelanin-silicon interface. Herein we report an integrated experimental and computational study aimed at probing interface stability via surface modificati ...[more]

PMID: 28753933

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Project description:A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

Dataset Information

Deciphering Molecular Mechanisms of Interface Buildup and Stability in Porous Si/Eumelanin Hybrids.

Publications

Deciphering Molecular Mechanisms of Interface Buildup and Stability in Porous Si/Eumelanin Hybrids.

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