Heterogeneous Photocatalysis of Metronidazole in Aquatic Samples

Metronidazole (MET) is a commonly detected contaminant in the environment. The compound is classified as poorly biodegradable and highly soluble in water. Heterogeneous photocatalysis is the most promoted water purification method due to the possibility of using sunlight and small amounts of a catalyst needed for the process. The aim of this study was to select conditions for photocatalytic removal of metronidazole from aquatic samples. The effect of catalyst type, mass, and irradiance intensity on the efficiency of metronidazole removal was determined. For this purpose, TiO2, ZnO, ZrO2, WO3, PbS, and their mixtures in a mass ratio of 1:1 were used. In this study, the transformation products formed were identified, and the mineralization degree of compound was determined. The efficiency of metronidazole removal depending on the type of catalyst was in the range of 50–95%. The highest MET conversion (95%) combined with a high degree of mineralization (70.3%) was obtained by using a mixture of 12.5 g TiO2–P25 + PbS (1:1; v/v) and running the process for 60 min at an irradiance of 1000 W m−2. Four MET degradation products were identified by untargeted analysis, formed by the rearrangement of the metronidazole and the C-C bond breaking.

Temperature-Dependent Growth of 36 Inner Nanotubes inside Nickelocene, Cobaltocene and Ferrocene-Filled Single-Walled Carbon Nanotubes

We have investigated the effects of temperature, diameter and metal catalyst type on the growth of inner nanotubes inside metallocene-filled single-walled carbon nanotubes (SWCNTs). The effects on the yield of different chiralities of inner nanotubes were scrutinized by multifrequency Raman spectroscopy. The investigated diameters range from ~0.7 to 1.3 nm and comprise 36 distinct chiralities. For all three investigated metals (Ni, Co, Fe), there is a linear correlation of growth temperature with nanotube diameter. The common slope for these metals is found to be 40.5 °C/Å. The temperature difference between the largest and the smallest diameter tubes amounts to ~230 °C for all three precursors. The growth temperatures are offset by 34 °C from Ni to Co and another 28 °C from Co to Fe. The quantified correlations of temperature, diameter and metal catalyst type provide the basis for engineering the diameter-specific growth of nanotubes.

Sustainable Exploitation of Residual Cynara cardunculus L. to Levulinic Acid and n-Butyl Levulinate

Hydrolysis and butanolysis of lignocellulosic biomass are efficient routes to produce two valuable bio-based platform chemicals, levulinic acid and n-butyl levulinate, which find increasing applications in the field of biofuels and for the synthesis of intermediates for chemical and pharmaceutical industries, food additives, surfactants, solvents and polymers. In this research, the acid-catalyzed hydrolysis of the waste residue of Cynara cardunculus L. (cardoon), remaining after seed removal for oil exploitation, was investigated. The cardoon residue was employed as-received and after a steam-explosion treatment which causes an enrichment in cellulose. The effects of the main reaction parameters, such as catalyst type and loading, reaction time, temperature and heating methodology, on the hydrolysis process were assessed. Levulinic acid molar yields up to about 50 mol % with levulinic acid concentrations of 62.1 g/L were reached. Moreover, the one-pot butanolysis of the steam-exploded cardoon with the bio-alcohol n-butanol was investigated, demonstrating the direct production of n-butyl levulinate with good yield, up to 42.5 mol %. These results demonstrate that such residual biomass represent a promising feedstock for the sustainable production of levulinic acid and n-butyl levulinate, opening the way to the complete exploitation of this crop.

A Review on Machine Learning Application in Biodiesel Production Studies

The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

Catalytic ethanolysis of fructan-rich and starchy expired food into biofuel 5-ethoxymethylfurfural

Catalytic systems were investigated for the ethanolysis of expired food into 5-ethoxymethylfurfural (5-EMF). Fructan-rich expired food (expired probiotics beverage powder, onion powder, garlic powder, and burdock tea), and starchy expired food (expired steamed buns and egg yolk battercake) were tested as starting substrates. Optimization of the reaction conditions included varying the catalyst type, temperature, catalyst loading, and reaction time to maximize 5-EMF yield. Several co-solvents were added to evaluate their impact on the generation of 5-EMF. The selected expired foods produced 5-EMF yields ranging from 0.2 mol% to 68 mol%. The 5-EMF yield from fructan-rich expired food was more noticeably affected by the content of the non-carbohydrate part than that from starchy expired food. The effect of co-solvents on conversion efficiency was closely related to the catalyst type but was not strongly correlated with the feedstock used. This study provides a facile way to produce biofuel chemicals from carbohydrate-rich expired food.