We have developed an evolutionary algorithm (EA) for the global minimum search of molecular clusters. The EA is able to discover all the putative global minima of water clusters up to (H(2)O)(20) and benzene clusters up to (C(6)H(6))(30). Then, the EA was applied to search for the global minima structures of (C(6)H(6))(n)(+) with n = 2-20, some of which were theoretically studied for the first time. Our results for n = 2-6 are consistent with previous theoretical work that uses a similar interaction potential. Excluding the very symmetric global minimum structure for n = 9, the growth pattern of (C(6)H(6))(n)(+) with n ≥ 7 involves the (C(6)H(6))(2)(+) dimer motif, which is placed off-center in the cluster. Such observation indicates that potentials commonly used in the literature for (C(6)H(6))(n)(+) cannot reproduce the icosahedral-type packing suggested by the available experimental data.

Statistical experimental designs were used to develop a medium based on corn steep liquor (CSL) and other low-cost nutrient sources for high-performance very high gravity (VHG) ethanol fermentations by Saccharomyces cerevisiae. The critical nutrients were initially selected according to a Plackett-Burman design and the optimized medium composition (44.3 g/L CSL; 2.3 g/L urea; 3.8 g/L MgSO₄·7H₂O; 0.03 g/L CuSO₄·5H₂O) for maximum ethanol production by the laboratory strain CEN.PK 113-7D was obtained by response surface methodology, based on a three-level four-factor Box-Behnken design. The optimization process resulted in significantly enhanced final ethanol titre, productivity and yeast viability in batch VHG fermentations (up to 330 g/L glucose) with CEN.PK113-7D and with industrial strain PE-2, which is used for bio-ethanol production in Brazil. Strain PE-2 was able to produce 18.6±0.5% (v/v) ethanol with a corresponding productivity of 2.4±0.1g/L/h. This study provides valuable insights into cost-effective nutritional supplementation of industrial fuel ethanol VHG fermentations.

The search of robust microorganisms is essential to design sustainable processes of second generation bioethanol. Yeast strains isolated from industrial environments are generally recognised to present an increased stress tolerance but no specific information is available on their tolerance towards inhibitors that come from the pretreatment of lignocellulosic materials. In this work, a strategy for the selection of different yeasts using hydrothermal hydrolysate from Eucalyptus globulus wood, containing different concentrations of inhibitors, was developed. Ten Saccharomyces cerevisiae and four Kluyveromyces marxianus strains isolated from industrial environments and four laboratory background strains were evaluated. Interestingly, a correlation between final ethanol titer and percentage of furfural detoxification was observed. The results presented here highlight industrial distillery environments as a remarkable source of efficient yeast strains for lignocellulosic fermentation processes. Selected strains were able to resourcefully degrade furfural and HMF inhibitors, producing 0.8g ethanol/Lh corresponding to 94% of the theoretical yield.

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Diversity is a key issue to consider when designing evolutionary approaches for difficult optimization problems. In this paper, we address the development of an effective hybrid algorithm for cluster geometry optimization. The proposed approach combines a steady-state evolutionary algorithm and a straightforward local method that uses derivative information to guide search into the nearest local optimum. The optimization method incorporates a mechanism to ensure that the diversity of the population does not drop below a pre-specified threshold. Three alternative distance measures to estimate the dissimilarity between solutions are evaluated. Results show that diversity is crucial to increase the effectiveness of the hybrid evolutionary algorithm, as it enables it to discover all putative global optima for Morse clusters up to 80 atoms. A comprehensive analysis is presented to gain insight about the most important strengths and weaknesses of the proposed approach. The study shows why distance measures that consider structural information for estimating the dissimilarity between solutions are more suited to this problem than those that take into account fitness values. A detailed explanation for this differentiation is provided.

The application and physiological background of two industrial Saccharomyces cerevisiae strains, isolated from harsh industrial environments, were studied in Very High Gravity (VHG) bio-ethanol fermentations. VHG laboratory fermentations, mimicking industrially relevant conditions, were performed with PE-2 and CA1185 industrial strains and the CEN.PK113-7D laboratory strain. The industrial isolates produced remarkable high ethanol titres (>19%, v/v) and accumulated an increased content of sterols (2 to 5-fold), glycogen (2 to 4-fold) and trehalose (1.1-fold), relatively to laboratory strain. For laboratory and industrial strains, a sharp decrease in the viability and trehalose concentration was observed above 90 g l⁻¹ and 140 g l⁻¹ ethanol, respectively. PE-2 and CA1185 industrial strains presented important physiological differences relatively to CEN.PK113-7D strain and showed to be more prepared to cope with VHG stresses. The identification of a critical ethanol concentration above which viability and trehalose concentration decrease significantly is of great importance to guide VHG process engineering strategies. This study contributes to the improvement of VHG processes by identifying yeast isolates and gathering yeast physiological information during the intensified fermentation process, which, besides elucidating important differences between these industrial and laboratory strains, can drive further process optimization.

An optimized very high gravity (VHG) glucose medium supplemented with low cost nutrient sources was used to evaluate bio-ethanol production by 11 Saccharomyces cerevisiae strains. The industrial strains PE-2 and CA1185 exhibited the best overall fermentation performance, producing an ethanol titre of 19.2% (v/v) corresponding to a batch productivity of 2.5 g l(-1) h(-1), while the best laboratory strain (CEN.PK 113-7D) produced 17.5% (v/v) ethanol with a productivity of 1.7 g l(-1) h(-1). The results presented here emphasize the biodiversity found within S. cerevisiae species and that naturally adapted strains, such as PE-2 and CA1185, are likely to play a key role in facilitating the transition from laboratory technological breakthroughs to industrial-scale bio-ethanol fermentations.

We propose a two-step genetic algorithm (GA) to fit potential energy curves to both ab initio and spectroscopic data. In the first step, the GA is applied to fit only the ab initio points; the parameters of the potential so obtained are then used in the second-step GA optimization, where both ab initio and spectroscopic data are included in the fitting procedure. We have tested this methodology for the extended-Rydberg function, but it can be applied to other functions providing they are sufficiently flexible to fit the data. The results for NaLi and Ar2 diatomic molecules show that the present method provides an efficient way to obtain diatomic potentials with spectroscopic accuracy.