Polyhydroxyalkanoate (PHA) produced by bacteria is a marine biodegradable polymer which is a potential substitute for the petroleum-based polymers. Metabolic engineering is a powerful approach for the development of platforms for microbial cell factories. This review introduces our metabolic engineering of the model methanol-utilizing bacterium Methylorubrum extorquens aiming at biosynthesis of practical PHA from sustainable methanol. The wild-type strain AM1 synthesizes a homopolymer of C₄ monomer unit ((R)-3-hydroxybutyrate)) with low cellular content using methanol substrate, whereas the engineered strain, with replaced PHA synthase and modified metabolic pathways for C₁-assimilation and PHA synthesis, synthesizes PHA terpolymer composed of C₄-C₆ monomer units from methanol. The cellular content of the terpolymer was 4-times higher than that of the C₄-homopolymer of the parent strain. However, enforcement of PHA biosynthesis was accompanied by reduction of methylotrophic growth, probably due to competition between the pathways for PHA biosynthesis and C₁-assimilation. Interestingly, this trade-off was overcome by adding lanthanum ion into the culture medium.

Machine learning has been successfully implemented in the estimation of reservoir fluid properties, competing with the empirical correlations used in this field. One of the most commonly used modeling schemes is the artificial neural network, which is known for its black-box problem. This study offers a different modeling approach that overcomes this limitation. The model provides accurate estimations and facilitates a deeper understanding of the key input parameters and their importance to the estimated results. It uses a boosted decision tree regression (BDTR) predictive modeling scheme to estimate the bubble point pressure (P_b) and oil formation volume factor at the bubble point pressure (B_ob) as a function of oil and gas specific gravities, solution gas-oil ratio, and reservoir temperature. The built BDTR model exhibits higher accuracy and performance than previous machine learning models and the most commonly used empirical correlations for estimating P_b and B_ob. The results indicate the higher efficacy of the developed model integrated with an imputation pre-processing step compared with the most commonly used empirical correlations in estimating P_b. This model brings significant predictive capability and versatility to datasets with multiple missing input features.

Fischer-Tropsch synthesis (FTS) using carbon dioxide (CO₂) as a reactant (CO₂-FTS) is a potential solution for carbon neutral societies to utilize atmospheric CO₂ as a carbon feedstock. The addition of potassium to Co-based catalysts is effective in promoting carbon chain growth to form liquid hydrocarbons (C₅₊) in CO₂-FTS, and hindering methane formation. However, the effects of potassium have not yet been clarified. The role of potassium in SiO₂-supported Co-based catalysts was investigated by FTIR, XPS, and STEM methods in terms of location and interaction. Potassium appears to maintain the oxidized state of the cobalt surface under reducing conditions, and the oxidized cobalt surface provides a weakly basic site for CO₂ adsorption. The maximum liquid product selectivity (53 %) was obtained for a reactant with a low H₂/CO₂ ratio (H₂/CO₂ = 1) using K–Co/SiO₂. The liquid products of K–Co/SiO₂ include C₅₊ and oxygenates, which consist of valuable alcohols and acetic acid.

We apologize for the following error which occurred in Vol. 63, No. 4 (July issue), pages 184-195.