Complexity Of Reaction Mechanism Research Articles

Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO2 emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly correlate the AAC mix design parameters to their performances through an artificial intelligence approach. To be specific, 145 fresh property data and 193 mechanical strength data were collected from laboratory tests on 52 AAC mixtures, which were used as inputs for the machine learning algorithm. Five independent random forest (RF) models were established, which are able to predict fresh and hardened properties (in terms of compressive strength, slump values, static/dynamic yield stress, and plastic viscosity) of AAC with equivalent accuracy reported in the literature. Moreover, an inverse optimization was performed on the RF model obtained to reduce the sodium silicate dosages, which may further mitigate the environmental impact of producing AAC. The present RF model gives practical information on AAC mix design cases.

Carbon Dioxide Reduction: A Bioinspired Catalysis Approach.

While developed in a number of directions, bioinspired catalysis has been explored only very recently for CO2 reduction, a challenging reaction of prime importance in the context of the energetic transition to be built up. This approach is particularly relevant because nature teaches us that CO2 reduction is possible, with low overpotentials, high rates, and large selectivity, and gives us unique clues to design and discover new interesting molecular catalysts. Indeed, on the basis of our relatively advanced understanding of the structures and mechanisms of the active sites of fascinating metalloenzymes such as formate dehydrogenases (FDHs) and CO dehydrogenases (CODHs), it is possible to design original, active, selective, and stable molecular catalysts using the bioinspired approach. These metalloenzymes use fascinating metal centers: in FDHs, a Mo(W) mononuclear ion is coordinated by four sulfur atoms provided by a specific organic ligand, molybdopterin (MPT), containing a pyranopterin heterocycle (composed of a pyran ring fused with a pterin unit) and two sulfhydryl groups for metal chelation; in CODHs, catalytic activity depends on either a unique nickel-iron-sulfur cluster or a dinuclear Mo-Cu complex in which the Mo ion is chelated by an MPT ligand. As a consequence, the novel class of catalysts, designed by bioinspiration, consists of mononuclear Mo, W, and Ni and as well as dinuclear Mo-Cu and Ni-Fe complexes in which the metal ions are coordinated by sulfur ligands, more specifically, dithiolene chelates mimicking the natural MPT cofactor. In general, their activity is evaluated in electrochemical systems (cyclic voltammetry and bulk electrolysis) or in photochemical systems (in the presence of a photosensitizer and a sacrificial electron donor) in solution. This research is multidisciplinary because it implies detailed biochemical, functional, and structural characterization of the inspiring enzymes together with synthetic organic and organometallic chemistry and molecular catalysis studies. The most important achievements in this direction, starting from the first report of a catalytically active biomimetic bis-dithiolene-Mo complex in 2015, are discussed in this Account, highlighting the challenging issues associated with synthesis of such sophisticated ligands and molecular catalysts as well as the complexity of reaction mechanisms. While the very first active biomimetic catalysts require further improvement, in terms of performance, they set the stage in which molecular chemistry and enzymology can synergistically cooperate for a better understanding of why nature has selected these sites and for developing highly active catalysts.

Effects of thermochemical modelling and surface catalyticity on an Earth re-entry vehicle

A numerical study has been conducted to assess the effects of thermochemical modelling and surface catalyticity on the design of a crew return vehicle re-entering from low Earth orbit. The effects of: complexity of chemical models, kinetics of reactions, vibrational relaxation, and wall reaction mechanism on vehicle aerothermodynamics and aerodynamics performances, and on some flowfield features, are highlighted. To this end, several numerical results derived for perfect and non-equilibrium reacting gas approximations are provided and compared. In this framework, a possible Earth-entry scenario for the proposed capsule-type vehicle is reported and extensively analysed by means of Navier–Stokes computations, performed both in trajectory-based and in space-based design approaches. Numerical results highlight that the accuracy of aerodynamic coefficients depends on the complexity of reaction mechanism considered in flowfield computations. For example, if the Zeldovich model is considered, the results produced are within 1 per cent of that of a solution with complete reaction mechanism, while the simulation speed up efficiency reaches about 40 per cent. This work underlines the fact that the CPU speed up depends, in particular, on the accuracy expected in vehicle pitching moment assessment, thus confirming that this parameter is one of the most critical vehicle aerodynamic performances to address in the case of a real gas-dominated flow. On the contrary, vehicle aerothermodynamic results show that, for a reliable heat flux evaluation, flowfield computations require a full reaction mechanism, as wall catalyticity plays a significant role when assessing vehicle aeroheating.