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Utilizing CO2 in an electro-chemical process and synthesizing value-added chemicals are amongst the few viable and scalable pathways in carbon capture and utilization technologies. CO2 electro-reduction is also counted as one of the main options entailing less fossil fuel consumption and as a future electrical energy storage strategy. The current study aims at developing a new electrochemical platform to produce low-carbon e-biofuel through multifunctional electrosynthesis and integrated co-valorisation of biomass feedstocks with captured CO2. In this approach, CO2 is reduced at the cathode to produce drop-in fuels (e.g., methanol) while value-added chemicals (e.g., selective oxidation of alcohols, aldehydes, carboxylic acids and amines/amides) are produced at the anode. In this work, a numerical model of a continuous-flow design considering various anodic and cathodic reactions was built to determine the most techno-economically feasible configurations from the aspects of energy efficiency, environment impact and economical values. The reactor design was then optimized via parametric analysis.

High mass loading (ca. 30 mg/cm2) electrodes were prepared with carbon recovered from catalytic methane cracking (MC). As-fabricated supercapacitors displayed 74% of capacitance retention from 6 mA/cm2 to 60 mA/cm2 and a Ragone plot’s slope of −7 Wh/kW (compared to 42% and −31 Wh/kW, respectively, for high mass loading devices fabricated with commercial carbon). The high-rate capability of the MC-recovered carbon is attributed to the presence of carbon black and carbon nanotubes produced during the reaction, which likely increased the electronic and ionic conductivity within the electrode. These results suggest that the by-product of this hydrogen generation route might be a suitable active material for supercapacitors.

Background: In recent years, microarray datasets have been used to store information about human genes and methods used to express the genes in order to successfully diagnose cancer disease in the early stages. However, most of the microarray datasets typically contain thousands of redundant, irrelevant, and noisy genes, which raises a great challenge for effectively applying the machine learning algorithms to these high-dimensional microarray datasets. Methods: To address this challenge, this paper introduces a proposed hybrid filter and differential evolution-based feature selection to choose only the most influential genes or features of high-dimensional microarray datasets to improve cancer diagnoses and classification. The proposed approach is a two-phase hybrid feature selection model constructed using selecting the top-ranked features by some popular filter feature selection methods and then further identifying the most optimal features conducted by differential evolution (DE) optimization. Accordingly, some popular machine learning algorithms are trained using the final training microarray datasets with only the best features in order to produce outstanding cancer classification results. Four high-dimensional cancerous microarray datasets were used in this study to evaluate the proposed method, which are Breast, Lung, Central Nervous System (CNS), and Brain cancer datasets. Results: The experimental results demonstrate that the classification accuracy results achieved by the proposed hybrid filter-DE over filter methods increased to 100%, 100%, 93%, and 98% on Brain, CNS, Breast and Lung, respectively. Furthermore, applying the suggested DE-based feature selection contributed to removing around 50% of the features selected by using the filter methods for these four cancerous microarray datasets. The average improvement percentages of accuracy achieved by the proposed methods were up to 42.47%, 57.45%, 16.28% and 43.57% compared to the previous works that are 41.43%, 53.66%, 17.53%, 61.70% on Brain, CNS, Lung and Breast datasets, respectively. Conclusions: Compared to the previous works, the proposed methods accomplished better improvement percentages on Brain and CNS datasets, comparable improvement percentages on Lung dataset, and less improvement percentages on Breast dataset.