Production of biohydrogen

Yin, Yanan; Wang, Jianlong

doi:10.1016/b978-0-12-824116-5.00002-7

Cited by 10 publications

(2 citation statements)

References 246 publications

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“…Among different types of hydrogenases, the [FeFe] hydrogenase is more efficient in hydrogen production than the other types. Therefore, Hyd is at the core of biological production of H 2 , which is a cost-effective and carbon-free strategy to produce fuels [4]. Among the organisms that express the enzyme, Chlorella vulgaris 211/11P Cvu, is an interesting microalga strain that presents a promising candidate for the production of biofuels at an industrial scale [5], due to its rapid growth in fresh water.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases

Botticelli,

La Penna,

Minicozzi

et al. 2024

IJMS

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The advent of deep learning algorithms for protein folding opened a new era in the ability of predicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning predictions is mandatory. We use the example of [FeFe] hydrogenases, enzymes of unicellular algae promising for biotechnology applications to illustrate this situation. [FeFe] hydrogenase is an iron–sulfur protein that catalyzes the chemical reduction of protons dissolved in liquid water into molecular hydrogen as a gas. Hydrogen production efficiency and cell sensitivity to dioxygen are important parameters to optimize the industrial applications of biological hydrogen production. Both parameters are related to the organization of iron–sulfur clusters within protein domains. In this work, we propose possible three-dimensional structures of Chlorella vulgaris 211/11P [FeFe] hydrogenase, the sequence of which was extracted from the recently published genome of the given strain. Initial structural models are built using: (i) the deep learning algorithm AlphaFold; (ii) the homology modeling server SwissModel; (iii) a manual construction based on the best known bacterial crystal structure. Missing iron–sulfur clusters are included and microsecond-long molecular dynamics of initial structures embedded into the water solution environment were performed. Multiple-walkers metadynamics was also used to enhance the sampling of structures encompassing both functional and non-functional organizations of iron–sulfur clusters. The resulting structural model provided by deep learning is consistent with functional [FeFe] hydrogenase characterized by peculiar interactions between cofactors and the protein matrix.

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Section: Introductionmentioning

confidence: 99%

Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases

Botticelli,

La Penna,

Minicozzi

et al. 2024

IJMS

View full text Add to dashboard Cite

show abstract

“…H 2 can also be generated through photo-fermentation (PF) and dark fermentation (DF) using organic compounds as the starting feedstock. For photo-fermentation, specialized photosynthetic organisms (e.g., purple non-sulfur bacteria) use light energy to convert volatile fatty acids such as acetate, butyrate, lactate among others to H 2 and CO 2 without oxygen (Yin and Wang, 2022), whereas for dark fermentation, H 2 is typically produced from carbohydrates (e.g., glucose, or more commonly known as "sugars") anaerobically without light input (Kamran, 2021). Two-stage processes integrating PF and DF have also been explored.…”

mentioning

confidence: 99%

Biohydrogen: prospects for industrial utilization and energy resiliency in rural communities

Mandalika,

Chou,

Decker

2024

Front. Ind. Microbiol.

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Biohydrogen (bioH2) production in rural regions of the United States leveraged from existing biomass waste streams serves two extant needs: rural energy resiliency and decarbonization of heavy industry, including the production of ammonia and other H2-dependent nitrogenous products. We consider bioH2 production using two different strategies: (1) dark fermentation (DF) and (2) anaerobic digestion followed by steam methane reforming of the biogas (AD-SMR). Production of bioH2 from biomass waste streams is a potentially ‘greener’ pathway in comparison to natural gas-steam methane reforming (NG-SMR), especially as fugitive emissions from these wastes are avoided. It also provides a decarbonizing potential not found in water-splitting technologies. Based on literature on DF and AD of crop residues, woody biomass residues from forestry wastes, and wastewaters containing fats, oils, and grease (FOG), we outline scenarios for bioH2 production and displacement of fossil fuel derived methane. Finally, we compare the costs and carbon intensity (CI) of bioH2 production with those of other H2 production pathways.

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Hydrogen Sensing with Palladium‐Based Materials: Mechanisms, Challenges, and Opportunities

Toksha,

Gupta,

Rahaman

2024

Chemistry An Asian Journal

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Palladium morphologies are prominently used in Hydrogen gas sensing applications owing to their unique characteristics and properties. In this review article, Palladium nanoparticles, thin films, and alloys were designated as the scope of Palladium morphologies. The aim of this review article is to explore Hydrogen sensing using Palladium, focusing on the recent advancements in the field. The principles underlying Hydrogen sensing mechanisms with Palladium are discussed initially, highlighting the unique properties of Palladium that make it a promising material for this purpose. Special attention is given to the surface interactions and structural modifications that influence the sensitivity and selectivity of Palladium‐based sensors. The study also addresses key challenges and recent innovations in the field which contribute to the enhancement of Palladium‐based Hydrogen sensing capabilities. The current state of research is critically examined to identify gaps in knowledge and future research directions are highlighted. The prospects and challenges associated with the use of Palladium for Hydrogen sensing, emphasizing its pivotal role in advancing sensor technologies for Hydrogen detection are also discussed.

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Production of biohydrogen

Cited by 10 publications

References 246 publications

Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases

Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases

Biohydrogen: prospects for industrial utilization and energy resiliency in rural communities

Hydrogen Sensing with Palladium‐Based Materials: Mechanisms, Challenges, and Opportunities

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