Formation and Characterization of Carbon-Radical Precursors in Char Steam Gasification

Highly reactive radicals play an important role in high-temperature gasification processes. However, the effect of radicals on gasification has not been systematically investigated. In the present study, the formation of carbon-radical precursors using atomic radicals such as OH, O, and H and molecules such as H₂ and O₂ was characterized, and the effect of the precursors on the adsorption step of steam char gasification was studied using quantum chemistry methods. The results revealed that the radicals can be chemisorbed exothermically on char active sites, and the following order of reactivity was observed: O > H₂ > H > OH > O₂. Moreover, hydrogen bonds are formed between steam molecules and carbon-radical complexes. Steam molecule adsorption onto carbon-O and carbon-OH complexes is easier than adsorption onto clean carbon surfaces. Alternatively, adsorption on carbon-O₂, carbon-H₂, and carbon-H complexes is at the same level with that of clean carbon surfaces; thus, OH and O radicals accelerate the physical adsorption of steam onto the char surface, H radical and O₂ and H₂ molecules do not have a significant effect on adsorption

Co-pyrolysis of Mixed Plastics and Cellulose: An Interaction Study by Py-GC×GC/MS

Understanding of the interaction between cellulose and various plastics is crucial for designing waste-to-energy processes. In this work, co-pyrolysis of polystyrene (PS) and cellulose was performed in a Py-GC×GC/MS system at 450–600 °C with ratios 70:30, 50:50, and 30:70. Polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET) were then added to the mixture with different ratios. It was found that co-pyrolysis of PS and cellulose promotes the formation of aromatic products with a large increase in the yield of ethylbenzene as compared to the calculated value from individual feedstock. This indicates interactions between cellulose and PS pyrolysis products. Observations from experiments including more than one type of plastics indicate that the interactions between different plastics are more pronounced than the interaction between plastics and cellulose

Evaluation of Engineered Biochar-Based Catalysts for Syngas Production in a Biomass Pyrolysis and Catalytic Reforming Process

Biochar, originating from biomass pyrolysis, has been proven a promising catalyst for tar cracking/reforming with great coke resistance. This work aims to evaluate various engineered biochar-based catalysts on syngas production in a biomass pyrolysis and catalytic reforming process without feeding extra steam. The tested engineered biochar catalysts include physical- and chemical-activated, nitrogen-doped, and nickel-doped biochars. The results illustrated that the syngas yields were comparable when using biochar and activated biochar as catalysts. A relatively high specific surface area (SSA) and a hierarchical porous structure are beneficial for syngas and hydrogen production. A 2 h physical-activated biochar catalyst induced the syngas with the highest H2/CO ratio (1.5). The use of N-doped biochar decreased the syngas yield sharply due to the collapse of the pore structure but obtained syngas with the highest LHVgas (18.5MJ/Nm3). The use of Ni-doped biochar facilitated high syngas and hydrogen yields (78.2 wt % and 26 mmol H2/g-biomass) and improved gas energy conversion efficiency (73%). Its stability and durability test showed a slight decrease in performance after a three-time repetitive use. A future experiment with a longer time is suggested to determine when the catalyst will finally deactivate and how to reduce the catalyst deterioration

Time course of the Mn(II)-oxidizing activities and the cell growth of several isolates from A-, B-, and C-layer soils.

<p>The cells were grown in liquor K medium for 144(OD₆₀₀) and the concentration of Mn oxides were determined according to the procedures described in the “Materials and Methods” section. (○) Optical density of the cells (at 600 nm); (▪) Concentration of Mn oxides; (□) pH. A: Non-Mn(II)-oxidizing <i>E. coli</i> JM109 (as the negative control). <b>B</b>: A86. <b>C</b>: A101. <b>D</b>: B84. <b>E</b>: C19. <b>F</b>: C13.</p

DGGE analysis of bacterial 16S rRNA V3 genes amplified from the total community DNA of the untreated soil (represented by A, B, and C labeled in lanes) and Mn (II)/carbon-rich complex medium-enriched soil (represented by 0, 1, and 10 mM labeled in lanes; from different depths; 0, 1, and 10 mM are the Mn(II) concentration).

<p><b>A</b>: DGGE profile of the original soil from A-, B-, and C-horizon soils; <b>B</b>, <b>C</b>, and <b>D</b>: DGGE profile of Mn(II)-enriched soil from A-, B-, and C-horizon soils, respectively. Denaturant gradients of 50% to 70%, 45% to 65%, 45% to 55%, and 40% to 50% were used for the optimal separation of the products for <b>A</b>, <b>B</b>, <b>C</b>, and <b>D</b>, respectively. The numbers on the gels are the bands that were excised and sequenced corresponded to the list in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073778#pone.0073778.s006" target="_blank">Table S1</a>.</p

XRD patterns (A, B, and C) and LBB tests (D) of Mn oxides from different depths of soils and Mn oxides from different Mn(II)-oxidizing bacteria.

<p>The experiments were performed using dried powdered Mn oxide samples. In A, B, and C, the red dashed lines indicate the overlapping peaks; In C, a HEPES buffer and a synthesized rhodochrosite sample were used as the negative controls.</p

Phylogenetic relationship between the 16S rRNA gene sequences from the soil isolates with high Mn(II)-oxidizing activities (labeled with “□”) and their closest GenBank sequences with 16S rRNA gene from the known Mn(II)-oxidizing bacterial strains (labeled with “▪”) reported previously.

<p>The GenBank accession numbers of these sequences are shown in brackets. Bootstrap values ≥50% with 1,000 replicates are indicated at the branch points.</p

SEM images of the mixture of bacteria and Mn oxides as well as EDX spectra of the selected areas.

<p>In SEM, A101, B84, and C92 were the isolates from the A-, B-, and C-layer substrata, respectively, illustrating the formation of Mn oxide aggregates; C13 represents an SEM image of an isolate having no capability to produce Mn oxide aggregates. Two scanning areas for EDX analysis in the SEM images of A101, B84, and C92 were indicated by a, b, and arrows, respectively. A single scan was indicated for C13.</p

The Analysis of a Novel Computational Thinking Test in a First Year Undergraduate Computer Science Course

In Ireland, Computer Science is not yet a state examination subject. In recent years, steps to include it have been taken - it was introduced as a Leaving Certificate subject in the academic year of 2018-19 on a pilot basis and will be examined for the first time in 2020 (O’Brien, 2017). Prior to this, the only Computer Science course offered at second level was a Junior Certificate Coding short course (NCCA, 2017). Research shows that an early introduction to computing is an advantage for students. It can build confidence in dealing with complexity and with open-ended problems (Yevseyeva &Towhidnejad, 2012). Problem-solving skills can be extended and transferred as reported by Koh et al. (2013) and students’ analytical skills can be improved according to Lishinski et al. (2016) and Van Dyne and Braun (2014). It has been shown by Webb and Rosson (2013) that students’ self-efficacy for computational problem solving, abstraction, debugging and terminology can be in-creased. It has also been found that teaching Computational Thinking can provide a better understanding of how programming is about solving a problem (not just coding) and that it can improve female students’ attitudes and confidence towards programming (Davies, 2008). One especially interesting finding is that exposure to Computational Thinking can be used as an early indicator and predictor of academic success since Computational Thinking scores have been found to correlate strongly with general academic achievement by Haddad and Kalaani (2015). This paper examines first year undergraduate Computer Science students who took a novel test to assess their Computational Thinking skills and in addition a survey gathering their views on Computer Science and Computational Thinking. This survey was administered twice within the academic year and comparisons are drawn on the changes between these survey results

Additional file 1: of Cross-border spread, lineage displacement and evolutionary rate estimation of rabies virus in Yunnan Province, China

Figure S1. Raw data of RABV N gene sequences in each location in each year in Southeast Asia. Figure S2. RABV N gene sequences in each area in each year. The color spectrum shows the number of sequences from each year and area, from green (low numbers of sequences) to dark red (high numbers of sequences). The data set contained 452 RABV sequences. Figure S3. Histogram showing the temporal trend of the expected number of RABV introductions from North and South China into Yunnan. Figure S4. Plot showing the number of sequences that cover each position in the RABV genome. The coverage low around position 4945 corresponds to a string of six guanines that is only 5 guanines long in many strains. Table S1. Sequences used in this study. Table S2. Time-annotated (near) full genome sequences used for estimating evolutionary rates. Table S3. The sampling time distribution of the final dataset. Table S4. Subsampled sequences used in phylogeographical analysis. (DOCX 332 kb