Multi-objective optimization of chemical reaction conditions based on a kinetic model

Коледина, К. Ф.; Koledin, S. N.; Karpenko, Anatoly; Gubaydullin, I. M.; Vovdenko, M. K.

doi:10.1007/s10910-018-0960-z

Cited by 32 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…28 While the efficacy of AI methods for accelerated formulation discovery and synthesis optimization of colloidal LHP QDs was demonstrated, the large data requirements make fine tuning of these parameters unrealistic to attain experimentally. Additionally, the advantages of such ENN algorithms over conventional GPR-based and other established methods such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [41][42][43] or Covariance Matrix Adaption-Evolutionary Strategy (CMA-ES) 42 remain unclear due to the inherent variability in simple optimization performance for uninformed chemical synthesis studies.…”

Section: Performance Comparisonsmentioning

confidence: 99%

Accelerated AI development for autonomous materials synthesis in flow

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: Performance Comparisonsmentioning

confidence: 99%

Accelerated AI development for autonomous materials synthesis in flow

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Autonomous experimentation techniques unlock continuous and extended material exploration, overcoming the limitations of a manual sampling and selection approach. These integrated technologies have been extensively applied in pharmaceuticals, [193] catalysis, [194] and organic reaction studies. [195] Applications in nanoscience, however, have primarily been in guided QD syntheses due to their accessible optical properties.…”

Section: Aidriven Accelerated Materials Discovery and Optimizationmentioning

confidence: 99%

Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow

Campbell

Bateni

Volk

et al. 2020

Part & Part Syst Charact

View full text Add to dashboard Cite

Semiconductors are intriguing due to their unique electrical properties, particularly the behavior of their electrons in the presence of different stimuli (e.g., electric field, magnetic field, light irradiation), which differ greatly from conducting (i.e., metals) and insulating materials. In insulators and semiconductors, the available electronic states are discontinuous, with the existence of a gap between the lower energy states, commonly referred to as the valence band (VB), and the higher energy states, known as the conduction band (CB). The distinguishing factor between these materials is the size of the energy difference between the highest energy state in the VB and the lowest energy state in the CB, called the band gap. In insulators, the band gap is very large, making it difficult, if not impossible, to cause an electron to move from the VB to the CB. By comparison, semiconductors possess narrow to moderate band gaps, implying that it is possible to introduce enough energy to an electron to cause it to move from the VB to the CB, enabling electron transfer, the use of high energy electrons, or the emission of energy (i.e., photons) when the electron returns from the CB to the VB. The wide range of industrial application-specific requirements demands versatility in the target characteristics of semiconductor materials (e.g., size, morphology, composition, band gap, emission wavelength), which are vastly different from those commonly used in thin-film transistors. Typically, the semiconductor materials synthesized for use in the above applications are particulate materials produced across a range of sizes spanning five orders of magnitude (from just a few nm to hundreds of µm), and can be divided into two overarching categories, nano-and microsized particulate materials. Nanosized semiconductor materials, due in large part to their miniscule dimensions, present a variety of intriguing characteristics not observed in microsized semiconductor particles. First and foremost, if the dimensions of the semiconductor material decrease below a certain threshold known as the Bohr radius, the absorption and emission energies become highly dependent on the particle size, a phenomenon known as quantum confinement. [28-30] Moreover, nanosized structures have significantly larger surface-to-volume ratios which increases the surface contribution to the total free energy of the semiconductor materials, making them highly soluble [29] and therefore much easier to process and handle. Thus far, nanosized semiconductor particles have been effectively utilized in sensors, [7] LEDs, [9] Controlled synthesis of semiconductor nano/microparticles has attracted sub stantial attention for use in numerous applications from photovoltaics to photo catalysis and bioimaging due to the breadth of available physicochemical and optoelectronic properties. Microfluidic material synthesis strategies have recently been demonstrated as an effective technique for rapid development, controlled synthesis, and continuous manufacturing of solutionpr...

show abstract

“…To simplify the model [24], only four dominant reaction pathways such as adsorption(a), desorption(d), reduction(r), and oxidation(0) are included in 1D model [25]: Corresponding reaction rates were taken from [26].…”

Section: Governing Equations and Assumptionsmentioning

confidence: 99%

Development of the model for a diesel engine catalytic converter

2019

View full text Add to dashboard Cite

One of the key issues of the modern engine development is to comply with today’s stringent emission standards. It forces the manufacturers to enhance in-engine and after treatment emission reduction technologies continuously. The selective catalytic reduction (SCR) is still the most effective technique for nitrogen oxides removal from exhaust gases of vehicles with diesel engines. Numerical modelling is widely used for SCR systems development and assessment. In this paper, a simplified one-dimensional numerical model of diesel SCR catalyst, which was implemented in Matlab, is described. The algorithm for automatic mesh generation describing real cross-section geometry of the catalyst block and the calculation procedure allowing to take into account non-uniform distribution of the gas flow parameters at the catalyst inlet are presented. Model was validated by the experimental data available in the literature. Numerical simulations for the full-scale modern SCR catalyst were carried out. The effect of the gas velocity non-uniformity at the catalyst inlet on the overall NOx reduction efficiency was evaluated.

show abstract

Multi-objective optimization of chemical reaction conditions based on a kinetic model

Cited by 32 publications

References 16 publications

Accelerated AI development for autonomous materials synthesis in flow

Accelerated AI development for autonomous materials synthesis in flow

Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow

Development of the model for a diesel engine catalytic converter

Contact Info

Product

Resources

About