Bifunctional Gas Diffusion Electrode Enables In Situ Separation and Conversion of CO2 to Ethylene from Dilute Stream

The requirement of concentrated carbon dioxide (CO₂ ) feedstock significantly limits the economic feasibility of electrochemical CO₂ reduction (eCO₂ R) which often involves multiple intermediate processes, including CO₂ capture, energy-intensive regeneration, compression, and transportation. Herein, a bifunctional gas diffusion electrode (BGDE) for separation and eCO₂ R from a low-concentration CO₂ stream is reported. The BGDE is demonstrated for the selective production of ethylene (C₂ H₄ ) by combining high-density-polyethylene-derived porous carbon (HPC) as a physisorbent with polycrystalline copper as a conversion catalyst. The BGDE shows substantial tolerance to 10 vol% CO₂ exhibiting a Faradaic efficiency of ≈45% toward C₂ H₄ at a current density of 80 mA cm^-2 , outperforming previous reports that utilized such partial pressure (P_CO2 = 0.1 atm and above) and unaltered polycrystalline copper. Molecular dynamics simulation and mixed gas permeability assessment reveal that such selective performance is ensured by high CO₂ uptake of the microporous HPC as well as continuous desorption owing to the molecular diffusion and concentration gradient created by the binary flow of CO₂ and nitrogen (CO₂ |N₂ ) within the sorbent boundary. Based on detailed techno-economic analysis, it is concluded that this in situ process can be economically compelling by precluding the C₂ H₄ production cost associated with the energy-intensive intermediate steps of the conventional decoupled process.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.

Sustainable valorization of asphaltenes via flash joule heating

The refining process of petroleum crude oil generates asphaltenes, which poses complicated problems during the production of cleaner fuels. Following refining, asphaltenes are typically combusted for reuse as fuel or discarded into tailing ponds and landfills, leading to economic and environmental disruption. Here, we show that low-value asphaltenes can be converted into a high-value carbon allotrope, asphaltene-derived flash graphene (AFG), via the flash joule heating (FJH) process. After successful conversion, we develop nanocomposites by dispersing AFG into a polymer effectively, which have superior mechanical, thermal, and corrosion-resistant properties compared to the bare polymer. In addition, the life cycle and technoeconomic analysis show that the FJH process leads to reduced environmental impact compared to the traditional processing of asphaltene and lower production cost compared to other FJH precursors. Thus, our work suggests an alternative pathway to the existing asphaltene processing that directs toward a higher value stream while sequestering downstream emissions from the processing.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Evaluation of antioxidant and chemopreventive effect of Cocos nucifera L

This study revealed the antioxidant and anticancer activities of the ethanolic extract of the tender fruits of Cocos nucifera. In antioxidant screening, IC50 value was found to be 7.71μg/mlfor ascorbic acid and44.67 μg/ml for C. nucifera fruits. The phenolic content, total flavonoid and total tannin content were 537.89 mg GAE/100 gm, 40.69mg of QE/100 gm and 44.61 mg of GAE/100 gm of dry powder of C. nucifera respectively. In DMBA and croton oil-induced skin cancer in model mice,the extract significantly decreased the number, size, yield and burden of tumor when compared with carcinogenic control. The extract increased the level of natural antioxidants like GSH, SOD and Catalase. Moreover, substantial decrease in SGPT, SGOT (liver activity marker) was observed at different doses ofthe crude extract. Lipid profile of mice treated with C. nucifera was brought back to normal level to some extent when compared to carcinogenic control. In conclusion,C. nucifera fruits have antioxidant activity as well as preventive role on cancer initiation and propagation without major toxicity.Jahangirnagar University J. Biol. Sci. 6(2): 47-58, 2017 (December)