A Study on the Ability of Nanomaterials to Adsorb NO and SO2 from Combustion Gases and the Effectiveness of Their Separation.

Climate neutrality for the year 2050 is the goal assumed at the level of the EU27+UK. As Romania is no exception, it has assumed the gradual mitigation of pollution generated by the energy sector, and by 2030, according to 'Fit for 55', the share of energy from renewable sources must reach 42.5% from total energy consumption. For the rest of the energy produced from traditional sources, natural gas and/or coal, modern technologies will be used to retain the gaseous noxes. Even if they are not greenhouse gases, NO and SO2, generated from fossil fuel combustion, cause negative effects on the environment and biodiversity. The adsorption capacity of different materials, three nanomaterials developed in-house and three commercial adsorbents, both for NO and SO2, was tackled through gas chromatography, elemental analysis, and Fourier-transform infrared spectroscopy. Fe-BTC has proven to be an excellent material for separation efficiency and adsorption capacity under studied conditions, and is shown to be versatile both in the case of NO (80.00 cm3/g) and SO2 (63.07 cm3/g). All the developed nanomaterials generated superior results in comparison to the commercial adsorbents. The increase in pressure enhanced the performance of the absorption process, while temperature showed an opposite influence, by blocking the active centers on the surface.

Selectivity of MOFs and Silica Nanoparticles in CO2 Capture from Flue Gases.

Until reaching climate neutrality by attaining the EU 2050 level, the current levels of CO2 must be mitigated through the research and development of resilient technologies. This research explored potential approaches to lower CO2 emissions resulting from combustion fossil fuels in power plant furnaces. Different nanomaterials (MOFs versus silica nanoparticles) were used in this context to compare their effectiveness to mitigate GHG emissions. Porous materials known as metal-organic frameworks (MOFs) are frequently employed in sustainable CO2 management for selective adsorption and separation. Understanding the underlying mechanism is difficult due to their textural characteristics, the presence of functional groups and the variation in technological parameters (temperature and pressure) during CO2-selective adsorption. A silica-based nanomaterial was also employed in comparison. To systematically map CO2 adsorption as a function of the textural and compositional features of the nanomaterials and the process parameters set to a column-reactor system (CRS), 160 data points were collected for the current investigation. Different scenarios, as a function of P (bar) or as a function of T (K), were designed based on assumptions, 1 and 5 vs. 1-10 (bar) and 313.15 and 373.15 vs. 313.15-423.15 (K), where the regression analyses through Pearson coefficients of 0.92-0.95, coefficients of determination of 0.87-0.90 and p-values < 0.05, on predictive and on-site laboratory data, confirmed the performances of the CRS.

Eco-Friendly Alternative Disposal through the Pyrolysis Process of Meat and Bone Meal.

The capitalization of agri-food waste is essential for the sustainability of a circular economy. This work focuses on a solution to eliminate such waste, meat and bone meal (MBM), which is produced in large quantities by the food industry and is prohibited for use as animal feed under the European directives. Therefore, with the focus of converting waste to energy, the catalytic pyrolysis of MBM in the presence of mesoporous silica nanocatalysts (SBA-3 and SBA-16 materials and metallic derivates) was investigated in a home-made reactor for the production of renewable energy. The mesoporous silica materials were synthesized using relatively simple methods and then characterized in order to determine their morpho-structural characteristics. The MBM pyrolysis behavior under different experimental conditions was examined in detail, both in the presence and absence of the new catalysts. The resulting MBM-based pyrolysis products, MBMPYOILs and MBMPYGASs, were also assessed as potential alternative fuels, highlighting comparable energy values to conventional fuels. The outcomes of this investigation offer a potential pathway to the clean production of gas and oil, thus promoting the high-grade utilization of MBM waste.

Genomics and Machine Learning for Taxonomy Consensus: The Mycobacterium tuberculosis Complex Paradigm.

Infra-species taxonomy is a prerequisite to compare features such as virulence in different pathogen lineages. Mycobacterium tuberculosis complex taxonomy has rapidly evolved in the last 20 years through intensive clinical isolation, advances in sequencing and in the description of fast-evolving loci (CRISPR and MIRU-VNTR). On-line tools to describe new isolates have been set up based on known diversity either on CRISPRs (also known as spoligotypes) or on MIRU-VNTR profiles. The underlying taxonomies are largely concordant but use different names and offer different depths. The objectives of this study were 1) to explicit the consensus that exists between the alternative taxonomies, and 2) to provide an on-line tool to ease classification of new isolates. Genotyping (24-VNTR, 43-spacers spoligotypes, IS6110-RFLP) was undertaken for 3,454 clinical isolates from the Netherlands (2004-2008). The resulting database was enlarged with African isolates to include most human tuberculosis diversity. Assignations were obtained using TB-Lineage, MIRU-VNTRPlus, SITVITWEB and an algorithm from Borile et al. By identifying the recurrent concordances between the alternative taxonomies, we proposed a consensus including 22 sublineages. Original and consensus assignations of the all isolates from the database were subsequently implemented into an ensemble learning approach based on Machine Learning tool Weka to derive a classification scheme. All assignations were reproduced with very good sensibilities and specificities. When applied to independent datasets, it was able to suggest new sublineages such as pseudo-Beijing. This Lineage Prediction tool, efficient on 15-MIRU, 24-VNTR and spoligotype data is available on the web interface "TBminer." Another section of this website helps summarizing key molecular epidemiological data, easing tuberculosis surveillance. Altogether, we successfully used Machine Learning on a large dataset to set up and make available the first consensual taxonomy for human Mycobacterium tuberculosis complex. Additional developments using SNPs will help stabilizing it.