Effect of preparation method of palygorskite-supported Fe and Ni catalysts on catalytic cracking of biomass tar

In this study, the effect of catalyst preparation and additive precursors on the catalytic decomposition of biomass using palygorskite-supported Fe and Ni catalysts was investigated. The catalysts were characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM). It is concluded that the most active additive precursor was Fe(NO3)3·9H2O. As for the catalyst preparation method, co-precipitation had superiority over incipient wetness impregnation at low Fe loadings

Kinetic study of goethite dehydration and the effect of aluminium substitution on the dehydrate

Goethite and Al-substituted goethite were synthesized and were characterized using XRD and XRF. The kinetic study of goethite dehydrate was investigated by TG and DTG at different heating rates (2, 5, 10, 15, 20 ◦C/min) and the effect of Al substitution for Fe on dehydrate was studied. The results showed that two types of absorbed water with the same Ed values of 3.4, 6.2 kJ/mol were confirmed on goethite and Alsubstituted goethite. Three types of hydroxyl units were proved, one being on the surface and the other two being in the structure of goethite. The substitution of Al for Fe in the structure of goethite decreases the desorption rate of hydroxyl, increases the dehydroxylation temperature, broadens the desorption peaks in DTG curves, and improves the Ed values from 19.4, 20.4, 26.1 kJ/mol to 21.6, 30, 33.6 kJ/mol when Al substitution comes to 9.1%

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167

Catalytic cracking of tars derived from rice hull gasification over goethite and palygorskite

Palygorskite (P), goethite (G), and hydrothermally synthesized goethite (HG) were used as supports for Fe and Ni. The catalytic activity of these materials was investigated involving in P, G and HG (supported Fe and Ni) for catalytic decomposition of biomass tar derived from rice hull gasification. The materials were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), and transmission electron microscopy (TEM) with an energy dispersive X-ray (EDS). The catalytic activity of P for removal of tar was significantly better than that of G and HG. However, the activity of G with 6 mass% Ni labeled as Ni6/G (tar conversion 94.6%), which was equal to Fe6Ni6/P (94.4%), was better than Ni6/P (64.4%) and Ni6/HG (46.7%). When the loading of Ni (mass%) was 6 mass% on G, tar conversion had the best value (94.6%) and yield of gaseous products reached 486.9, 167.8 and 22.2 mL/(g·tar) for H2, CO, CH4, respectively. The catalytic activity of goethite supported Ni was better in improving tar conversion and improving increased yield of H2, CO, CH4, which was attributed to the existence of Al/Fe substitution of goethit

Effect of palygorskite clay on pyrolysis of rape straw: An in situ catalysis study

Biomass tar restricts the wide application and development of biomass gasification technology. In the present paper, palygorskite, a natural magnesium-containing clay mineral, was investigated for catalytic pyrolysis of rape straw in situ and compared with the dolomite researched widely. The two types of natural minerals were characterized with XRD and BET. The results showed that combustible gas derived from the pyrolysis increased with an increase in gasification temperature. The Hconversion and Cconversion increased to 44.7% and 31% for the addition of palygorskite and increased to 41.3% and 31.3% for the addition of dolomite at the gasification temperature of 800 °C, compared with 15.1% and 5.6% without addition of the two types of material. It indicated that more biomass was converted into combustible gases implying the decrease in biomass tar under the function of palygorskite or dolomite and palygorskite had a slightly better efficiency than that of dolomite in the experimental conditions

Catalytic cracking of tar derived from rice hull gasification over palygorskite-supported Fe and Ni

The catalytic performance of Fe–Ni/PG (PG: palygorskite) catalysts pre-calcined and reduced at 500 ◦C for catalytic decomposition of tar derived through rice hull gasification was investigated. The materials were characterized by using X-ray diffraction, hydrogen temperature reduction, and transmission electron microscopy. The results showed that ferrites with spinel structure ((Fe, Ni)3O4) were formed during preparation of bimetallic systems during calcination and reduction of the precursors (Fe–Ni/PG catalysts) and NiO metal oxide particles were formed over Fe6–Ni9/PG catalyst. The obtained experimental data showed that Fe–Ni/PG catalysts had greater catalytic activity than natural PG. Tar removal using Fe6–Ni9/PG catalyst was as high as Fe10–Ni6/PG catalyst (99.5%). Fe6–Ni9/PG showed greater catalytic activity with greater H2 yield and showed stronger resistance to carbon deposition, attributed to the presence of NiO nanoparticles. Thus, the addition of nickel and iron oxides played an important role in catalytic cracking of rice hull biomass tar