Hydrogen@Home is a research project that uses Internet-connected computers to do research in Hydrogen Production. Our project is in a conceptual developement phase called "Alpha" Phase, you can participate by downloading and running a free program on your computer. Visit the project here - http://hydrogenathome.org/

Have you seen all the media attention paid to hydrogen fuel cell vehicles? Instead of emitting Greenhouse Gases such as CO₂, Hydrogen Fuel Cell Vehicles release water vapor. Tank-to-wheel efficiency of a fuel cell vehicle approaches 45%, compared to diesel vehicles which are 22%.

 

Fuel cell technologies work by reacting chemicals in a manner that forces electrons to travel through a circuit. Hydrogen fuel cells work by reacting Hydrogen with Oxygen which results in water vapor and energy. Many people believe fuels cells are the solution to Global Climate Change, given the abundance of hydrogen in the form of water.

Extracting hydrogen from water is an expensive process using electrolysis, the process of passing electrical current in water. This just exports the problem to the power plant. Furthermore, extracting hydrogen from hydrocarbons such as methane, also result in unwanted emissions. Both fossil fuels and nuclear power have their respective problems.

An alternative strategy for energy security is decentralizing the energy infrastructure to the local level. Developing technologies that are accessible to resource deprived communities in developing countries. Most importantly, this energy technology should be environmentally friendly and affordable.

We could go through a litany describing all potential renewable energy technologies. Among the major categories are solar, wind and geothermal. Despite years of study, none of these technologies have managed to compete on the same scale as fossil fuels or nuclear power.

Biological systems have the singular ability to self-optimize, replenish and require minimal resources. Provide a pool of algae with water, light and a good balance of nutrients and they will thrive in temperate climates. In fact, there are even reports of algae thriving under Arctic Sea Ice! So many of our medicines are manufactured through biological systems: antibiotics and vaccines. Devices used to grow these medicines and other biologically fabricated materials, are frequently called bioreactors.

In the late 1990s, UC Berkeley scientist Anastasios Melis experimented on algae cultures and discovered sulfur deprived algae can produce hydrogen through photosynthesis. Dr Melis discovered the enzyme hydrogenase is responsible for catalyzing this reaction and that this enzyme looses this function in the presence of oxygen. Algae producing this hydrogen are exhausted as oxygen quickly accumulates and causes damage to the hydrogenase.

Any technology analysis of a Hydrogen economy must address the following issues: Production, Application, Storage, Transport and Assess the environmental impact (PASTA). Since the US National Highway Traffic Safety Administration set the Corporate Average Fuel Economy using an acronym called CAFE, it seems fitting that we can reduce this analysis to an acronym called PASTA.

In 2004, the DOE published the "Updated Cost Analysis of Photo biological Hydrogen Production from Chlamydomonas reinhardtii Green Algae", to project the economic viability of biological hydrogen production. Their 2004 analysis estimated it would cost $13.53 USD per kilogram which is roughly the energy equivalent to a gallon or 4 liters of petrol.

Recent developments suggest that the above cost analysis is less relevant because it does not take into account improved efficiency of light conversion through genetic mutations. In 2007, Dr Melis reported "~15% utilization efficiency of absorbed light energy was achieved", by mutating the genes responsible for Chlorophyll Antennae structures. Naturally occuring algae only achieve 0.2% efficiency.

 

Despite all this good news the question remains, how can we mitigate oxidative damage to Hydrogenase? Is it possible to computationally model biological hydrogen production and use these models to mitigate this oxidation? Hydrogen@Home is developing such models.

In the short term Hydrogen@Home is interested in computational research on biological hydrogen production, particularly how this could scale economically. Hydrogen@Home is actively pursing collaborative relationships with several academic institutions, government agencies and corporations.

Our broad vision for Hydrogen@Home is to analyze aspects of renewable hydrogen technology described by PASTA. Computationally modeling hydrogen production is a difficult task requiring many theoretical considerations. In the short term, we intend to build accurate models of these biochemical interactions. Long term, we seek an experimental design that will screen chemical data for new catalysts (organic or inorganic) useful in hydrogen production. As a way to learn about BOINC and computational chemistry, several open source molecular modeling tools have been employed.

In order to determine coordinates of likely binding sites between molecules, Hydrogen@Home employs Molecular Docking simulations. Molecular Docking is a type of simulation best suited for identifying potential drugs for diseases as demonstrated by FightAids@Home where researchers try to find molecules that disrupt the normal function of HIV Protease. Mark Somers developed a molecular simulation user interface on Leiden Classical, which inspired me to implement a user interface that facilitates custom docking simulations. This way users can submit their own Docking Experiment and possibly replicate experiments performed by FightAids@Home. Potentially, such interfaces could end up in science classrooms.

Molecular docking simulations are only part of the hydrogen modeling odyssey. Molecular Dynamics is a similar modeling approach except it models the behavior of molecules over a length of time. As we have learned more about molecular modeling, we understand that Hybrid QM/MM Molecular Dynamics simulations provide the best potential for modeling enzyme interactions on a reasonable timescale. Hybrid QM/MM applies computationally expensive quantum theory over a limited area and computationally less expensive classical physics to a larger area of the simulation. Classical physics alone, is not effective in modeling these enzyme catalyzed chemical reactions, applying quantum theory to a limited area makes it computationally feasible.

Are these simulations feasible for distributed computing? Before we can effectively design these Molecular Dynamics simulations, we must determine the best experimental methods; What computational methods are reasonable and accurately predict outcomes that match reality. We should also define how small these molecular dynamics calculations can be reduced for distribution over the Internet.

In general, CPU and memory requirements are:

Molecular Mechanical methods	~N2
Semiempirical Quantum Chemical methods	~N2
Ab initio Quantum Chemical methods	~N4

http://anusf.anu.edu.au/%7Evvv900/qm-mm.html

There are four theoretical assumptions that we can apply independently to any molecular dynamics simulations: ab initio, Density Functional Theory (DFT), Semi-Empirical and Classical Molecular Mechanics. We could design ab initio simulations that applies pure quantum theory and is computationally expensive. The computational cost of DFT can vary depending on theoretical considerations, they model the complete electronic structure of molecules. Semi-empirical QM methods are computationally more efficient on orders of magnitude than DFT or ab initio methods because they use some predefined parameters called Molecular Force Fields; however, these force fields can be difficult to derive and do not always transfer from one experiment to another. Finally Molecular Mechanics is the simplest method of modeling molecular interactions and has many applications.

Example: One Energy Evaluation for a Peptide with 126 Atoms:

METHOD	CPU TIME Seconds	MEMORY KB
QUANTUM CHEMICAL*	273.0	4889
MOLECULAR MECHANICAL	0.15	58

*Semi-empirical PM3 method http://anusf.anu.edu.au/%7Evvv900/qm-mm.html

If you consider the above semi-empirical an N² and ab initio N⁴, an equivalent ab initio would take approximately 1 day and 23 GB of memory. Perhaps with a multi core processor, these calculations can be achieved quicker.

Is it possible to spread the computations and memory over the internet? Standard desktop computers cannot provide the same performance we expect from supercomputers. We can reduce our computations by minimizing the number of atoms and distributing short time intervals of analysis. If we have a large batch of variables to examine, distributed computing could be useful because its doing so many different experiments in parallel, automating MD analysis is problematic.

Molecular dynamics can be used to interpret empirical data or predict outcomes. These simulations provide data that characterizes free energy change and coordinates. We would like to determine the reaction mechanisms for hydrogen production or the Minimum Energy Path (MEP) for a reaction. There are several theoretical approaches to predicting MEP. One of the most frequently cited methods is called Nudge Elastic Band (NEB) method, which predicts reaction paths that provide smooth energy curves.

Once we have an accurate computer model that corresponds to reality, we can begin introducing variables in computer simulations and compare these predictions to experiments. If this methodology proves effective, we scale these experiments to a new level and explore many variables such as different enzymes, co-enzymes and alternative reaction pathways. If these methods are effective in modeling hydrogen production, we can reasonably assume they would be effective in modeling all aspects of PASTA. There is reason to believe fuel cell technology can achieve 90% tank-to-wheel efficiency using enzymes that facilitate proton transport. In this scenario, vehicles could travel twice as far on one kg of Hydrogen.

Besides modeling the actual enzyme catalyzed reaction, another less computationally demanding approach is identifying proteins with structural homology to Hydrogenase and vectoring their genes into the algae in a way that they are delivered to the same reaction site. This should be a relative easy approach and it could be implemented as one group of workunits run on Hydrogen@Home.

Another approach to mitigating oxidative damage is finding mechanisms to transport oxygen away from the Hydrogenase. Biology has developed ways to transport oxygen as demonstrated by hemoglobin and myoglobin. It is unclear whether a transport protein can capture the high energy oxygens emerging from PSII; moreover, whether these proteins are soluble in the cynobacteria's thylakoid membrane. If we study obligate anaerobes, microorganisms that cannot survive in Oxygen, we may find ways to create anoxic environments.

Any modeling technique for modeling biochemical behaviors must rely on benchmark comparisons. These models can be powerful if we choose the right theoretical considerations. If we our models don't match experimental observations, we should go back and pick different sets of parameters. As a research project, we are developing collaborative relationships with laboratories interested in this research topic. With these relationships in place, we will be much more effective in our experimental design.

Visit the project here - http://hydrogenathome.org/

 

< Prev