25 research outputs found

    Hydrogenase Inhibition by O2: Density Functional Theory/Molecular Mechanics Investigation

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    [Fe-Fe]-hydrogenases are enzymes that reversibly catalyze the reduction of protons to molecular hydrogen, which occurs in anaerobic media. In living systems, [Fe-Fe]-hydrogenases shift the reversible reaction towards H2 formation. The [Fe-Fe]-hydrogenase H-cluster is the active site, which contains two iron atoms (Fep-Fed, i.e., proximal and distal iron). Because most experimental and theoretical investigations confirm that the structure of di-iron air inhibited species is FepII-FedII-O-O-H-, O2 has to be prevented from binding to Fed in all di-iron subcluster oxidation states in order to retain a catalytically active enzyme. By understanding the catalytic processes of metalloenzymes, researches are enabled to produce an excellent source of fuel and energy storage (H2) for the future, which is clean and highly energetic when reacted with oxygen. H-cluster oxidation in gas phase, and in aqueous enzyme phase, has been investigated by means of quantum mechanics (QM) and combined quantum mechanics-molecular mechanics (QM/MM). The inhibitory process occurs at the coordination site, distal iron (Fed), of the catalytic H-cluster. The processes involved in the H-cluster oxidative pathways are O2 binding, e-transfer, protonation, and H2O removal. We found that oxygen binding is non-spontaneous in gas phase, and spontaneous for aqueous enzyme phase where both Fe atoms have oxidation state II however, it is spontaneous for the partially oxidized and reduced clusters in both phases. Hence, in the protein environment the O2-inhibited H-cluster is obtained by means of exergonic reaction pathways. A unifying endeavor has been carried out for the purpose of understanding the thermodynamic results vis-a-vis several other performed electronic structural methods, such as frontier molecular orbitals (FMO), natural bond orbital partial charges (NBO), and H-cluster geometrical analysis. Since hydrogenases become O2 inactivated, residue mutations were carried out in order to make them O2 resistant. Residue mutations consist of delet

    Residue Mutations in [Fe-Fe]-Hydrogenase Impedes O 2 Binding: A QM/MM Investigation

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    [Fe-Fe]-hydrogenases are enzymes that reversibly catalyze the reaction of protons and electrons to molecular hydrogen, which occurs in anaerobic media. In living systems, [Fe-Fe]-hydrogenases are mostly used for H(2) production. The [Fe-Fe]-hydrogenase H-cluster is the active site, which contains two iron atoms. The latest theoretical investigations1,2 advocate that the structure of di-iron air inhibited species are either Fe(p) (II)-Fe(d) (II)-O-H(-), or Fe(p) (II)-Fe(d) (II)-O-O-H, thus O(2) has to be prevented from binding to Fe(d) in all di-iron subcluster oxidation states in order to retain a catalytically active enzyme. By performing residue mutations on [Fe-Fe]-hydrogenases, we were able to weaken O(2) binding to distal iron (Fe(d)) of Desulfovibrio desulfuricans hydrogenase (DdH). Individual residue deletions were carried out in the 8 A apoenzyme layer radial outward from Fe(d) to determine what residue substitutions should be made to weaken O(2) binding. Residue deletions and substitutions were performed for three di-iron subcluster oxidation states, Fe(p) (II)-Fe(d) (II), Fe(p) (II)-Fe(d) (I), and Fe(p) (I)-Fe(d) (I) of [Fe-Fe]-hydrogenase. Two deletions (DeltaThr(152) and DeltaSer(202)) were found most effective in weakening O(2) binding to Fe(d) in Fe(p) (II)-Fe(d) (I) hydrogenase (DeltaG(QM/MM) = +5.4 kcal/mol). An increase in Gibbs\u27 energy (+2.2 kcal/mol and +4.4 kcal/mol) has also been found for Fe(p) (II)-Fe(d) (II), and Fe(p) (I)-Fe(d) (I) hydrogenase respectively. pi-backdonation considerations for frontier molecular orbital and geometrical analysis corroborate the Gibbs\u27s energy results

    Inactivation of [Fe-Fe]-Hydrogenase by O2. Thermodynamics and Frontier Molecular Orbitals Analyses

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    The oxidation of H-cluster in gas phase, and in aqueous enzyme phase, has been investigated by means of quantum mechanics (QM) and combined quantum mechanics–molecular mechanics (QM/MM). Several potential reaction pathways (in the above-mentioned chemical environments) have been studied, wherein only the aqueous enzyme phase has been found to lead to an inhibited hydroxylated cluster. Specifically, the inhibitory process occurs at the distal iron (Fed) of the catalytic H-cluster (which isalso the atom involved in H2 synthesis). The processes involved in the H-cluster oxidative pathways are O2 binding, e− transfer, protonation, and H2O removal. We found that oxygen binding is nonspontaneous in gas phase, and spontaneous for aqueous enzyme phase where both Fe atoms have oxidation state II; however, it is spontaneous for the partially oxidized and reduced clusters in both phases. Hence, in the protein environment the hydroxylated H-cluster is obtained by means of completely exergonic reaction pathway starting with proton transfer. A unifying endeavor has been carried out for the purpose of understanding the thermodynamic results vis-à-vis several other performed electronic structural methods, such as frontier molecular orbitals (FMO), natural bond orbital partial charges (NBO), and H-cluster geometrical analysis. An interesting result of the FMO examination (for gas phase) is that an e− is transferred to LUMOα rather than to SOMOβ, which is unexpected because SOMOβ usually resides in a lower energy rather than LUMOα for open-shell clusters

    Normal and inverted hysteresis in perovskite solar cells

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    Hysteretic effects are investigated in perovskite solar cells in the standard FTO/TiO2_2/CH3_3NH3_3PbI3−x_{3-x}Clx_x/spiro-OMeTAD/Au configuration. We report normal (NH) and inverted hysteresis (IH) in the J-V characteristics occurring for the same device structure, the behavior strictly depending on the pre-poling bias. NH typically appears at pre-poling biases larger than the open circuit bias, while pronounced IH occurs for negative bias pre-poling. The transition from NH to IH is marked by a intermediate mixed hysteresis behavior characterized by a crossing point in the J-V characteristics. The measured J-V characteristics are explained quantitatively by the dynamic electrical model (DEM). Furthermore, the influence of the bias scan rate on the NH/IH hysteresis is discussed based on the time evolution of the non-linear polarization. Introducing a three step measurement protocol, which includes stabilization, pre-poling and measurement, we put forward the difficulties and possible solutions for a correct PCE evaluation.Comment: 11 pages, 10 figure

    Measurement of anisotropic volumetric resistivity in lithium ion electrodes

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    Measurements of the electronic conductivity of lithium ion coatings are an important part of electrode development, particularly for thicker electrodes and in high power applications. A resistance measurement system with 46 probes has been used to characterise lithium ion electrodes, with different formulations and coat weights. The results show that the total through plane resistance is dominated by the interface resistance between the coating and the metal foil, rather than the volumetric resistivity of the coating. For coatings containing carbon nano-tubes, the in plane resistivities in the coating and perpendicular directions are different. A finite volume model was developed to help analyse and interpret the resistivity data

    Optimisation of industrially relevant electrode formulations for LFP cathodes in lithium ion cells

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    The electrode formulation has a significant effect on the performance of lithium ion cells. The active material, binder, and conductive carbon all have different roles, and finding the optimum composition can be difficult using an iterative approach. In this study, a design of experiment (DoE) methodology is applied to the optimisation of a cathode based on lithium iron phosphate (LFP). The minimum LFP content in the electrodes is 94 wt%. Seventeen mixes are used to evaluate adhesion, resistivity, and electrochemical performance. The coating adhesion increases with binder content, and the coating conductivity increases with carbon nano-tube content. The best coatings achieve 5C:0.2C capacity ratios above 50%, despite the relatively high coat weight. Models based on just the component mixture do not replicate the discharge capacities at high rates. However, a combined mixture + process model can fit the data, and is used to predict an optimum formulation

    Data of physical and electrochemical characteristics of calendered NMC622 electrodes and lithium-ion cells at pilot-plant battery manufacturing

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    The data reported here was prepared to study the effects of calendering process on NMC622 cathodes using a 3-3-2 full factorial design of experiments. The data set consists of 18 unique combinations of calender roll temperature (85 °C, 120 °C, or 145 °C), electrode porosity (30%, 35%, or 40%), and electrode mass loading (120 g/m² or 180 g/m²). The reported physical characteristics of the electrodes include thickness, coating weight, maximum tensile strength, and density. The electrochemical performances of the electrodes were obtained by testing coin cells. In this context, 54 half-cells were produced, 3 per each calendering experiment to ensure repeatability and reliability of the results. The responses of interest included, charge energy capacity at C/2, C/5, discharge energy capacity at C/20, C/5, C/2, C, 2C, 5C, 10C, gravimetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), volumetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), rate performance (5C:0.2C), area specific impedance (at 10% to 90% state of charge (SoC) in 10 breakpoints), long-term cycling capacity (charge at C/5 for 50 cycles, discharge at C/2 for 50 cycles), long-term cycling degradation (at C/2 during 50 cycles of charge and discharge), and cycling columbic efficiency (50 cycles of C/2 charge and discharge). The details of the experimental design that has led to this data as well as comprehensive statistical analysis, and machine learning-based models can be found in the recently published manuscripts by Hidalgo et al. and Faraji-Niri et al. [1,2]
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