Metagenomic analysis of lichen-associated bacterial community profiling in Roccella montagnei.

A lichen is a composite organism formed of algae or cyanobacteria that live in a mutually advantageous symbiotic relationship with the filaments (hyphae) of fungus. Three lichen samples were obtained from diverse sites at a terrestrial habitat located in Coimbatore and coastal habitats located in Kanyakumari and Nagapattinam districts of Tamil Nadu. Amplification and sequencing of 16S rRNA V3-V4 regions were used for metagenomic study. Aside from the Next-Generation Sequencing data (NGS), distinct types of lichen microbiome profiles were clearly revealed. The bacterial diversity in the lichen genera of Roccella montagnei growing in coastal and terrestrial environments was further investigated using common and unique operational taxonomic units (OTUs) and the QIIME pipeline (1.9.1). Using similarity clustering, the heat map analysis depicts the abundance information of chosen OTUs as well as the similarity and difference between OTUs and lichen samples. Using multiple methods, the alpha and beta diversity analysis revealed that there were differences in all of the samples. However, UPGMA tree inference of comparable bacterial community in coastal habitat lichen samples compared to terrestrial habitat validates their evolutionary lineage. As a result, the bacterial population associated with corticolous lichen is dependent on geographic locations, growth substrate, and climatic circumstances of similar lichen genera produced in different habitats and tree substrates.

Alchemical Predictions for Computational Catalysis: Potential and Limitations.

Kohn-Sham density functional theory (DFT) is the workhorse method for calculating adsorbate binding energies relevant for catalysis. Unfortunately, this method is too computationally expensive to methodically and broadly search through catalyst candidate space. Here, we assess the promise of computational alchemy, a perturbation theory approach that allows for predictions of binding energies thousands of times faster than DFT. We first benchmark the binding energy predictions of oxygen reduction reaction intermediates on alloys of Pt, Pd, and Ni using alchemy against predictions from DFT. Far faster alchemical estimates yield binding energies within 0.1 eV of DFT values in many cases. We also identify distinct cases where alchemy performs significantly worse, indicating areas where modeling improvements are needed. Our results suggest that computational alchemy is a very promising tool that warrants further consideration for high-throughput screening of heterogeneous catalysts.

Structural and Substituent Group Effects on Multielectron Standard Reduction Potentials of Aromatic N-Heterocycles.

Aromatic N-heterocycles have been used in electrochemical CO2 reduction, but their precise role is not yet fully understood. We used first-principles quantum chemistry to determine how the molecular sizes and substituent groups of these molecules affect their standard redox potentials involving various proton and electron transfers. We then use that data to generate molecular Pourbaix diagrams to find the electrochemical conditions at which the aromatic N-heterocycle molecules could participate in multiproton and electron shuttling in accordance with the Sabatier principle. While one-electron standard redox potentials for aromatic N-heterocycles can vary significantly with molecule size and the presence of substituent groups, the two-electron and two-proton standard redox potentials depend much less on structural modifications and substituent groups. This indicates that a wide variety of aromatic N-heterocycles can participate in proton, electron, and/or hydride shuttling under suitable electrochemical conditions.