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 (PCO2 = 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.

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.