Thermolysis of disubstituted 1,2-dioxetanes: Activation parameters and chemiexcitation yields

trans-3-Methyl-4-(p-anisyl)-1,2-dioxetane 1, trans-3-methyl-4-(o-anisyl)-1,2-dioxetane 2 , 3-methyl-3-benzyl-1,2-dioxetane 3 , and 3-methyl-3-p-methoxybenzyl-1,2-dioxetane 4 were synthesized in low yield by the β-bromo hydroperoxide method. The activation parameters were determined by the chemiluminescence method (for 1 ΔG≠ = 22.8 ± 0.3 kcal/mol, Δ≠ = 22.2, ΔS≠ = −1.7 e.u., k₆₀ = 7.6 × 10⁻³s⁻¹; for 2 ΔG≠ + 23.6 ± 0.3 kcal/mol, ΔH≠ = 22.8, ΔS≠ = −2.2 e.u., k₆₀ = 2.5 × 10⁻³S⁻¹; for 3 ΔG≠ = 24.0 ± 0.4 kcal/mol, ΔH≠ = 23.1, ΔS≠ = −2.7 e.u., k₆₀ = 1.2 × 10⁻³S⁻¹; for 4 ΔG≠ = 24.0 ± 0.2 kcal/mol, ΔH≠, = 23.2, ΔS≠, = −2.4 e.u., k₆₀ = 1.2 × 10⁻³s⁻¹). Thermolysis of 1–4 produced excited carbonyl fragments (direct production of high yields of triplets relative to excited singlets) [chemiexcitation yields ϕ^T, ϕ^S, respectively: for 1 0.02, 0.0001; for 2 0.02, 0.0001; for 3 0.03, 0.0002; for 4 0.02, 0.0001]. The effect of paramethoxyaryl substitution was consistent with electronic effects. The ortho substitution in 2 resulted in an increase in stability of the dioxetane, opposite that observed for an electronic effect. The results are discussed in relation to a diradical-like mechanism.     

Construction of amperometric biosensor modified with conducting polymer/carbon dots for the analysis of catechol

Phenolic compounds used in food industries and pesticide industry, are environmentally toxic and pollute the rivers and ground water. For that reason, detection of phenolic compounds such as catechol by using simple, efficient and cost-effective devices have been becoming increasingly popular. In this study, a suitable and a novel matrix was composed using a novel conjugated polymer, namely poly[1-(5-(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophen-2-yl)furan-2-yl)-5-(2-ethylhexyl)-3-(furan-2-yl)-4H thieno[3,4-c]pyrrole-4,6(5H)-dione] (PFTBDT) and carbon dots (CDs) to detect catechol. PFTBDT and CDs were synthesized and the optoelectronic properties of PFTBDT were investigated via electrochemical and spectroelectrochemical studies. Laccase enzyme was immobilized onto the constructed film matrix on the graphite electrode. The proposed biosensor was found to have a low detection limit (1.23 μM) and a high sensitivity (737.44 μA/mM.cm⁻²) with a linear range of 1.25–175 μM. Finally, the applicability of the proposed enzymatic biosensor was evaluated in a tap water sample and a satisfactory recovery (96–104%) was obtained for catechol determination.     

Investigation of energy performance in a U-shaped evacuated solar tube collector using oxide added nanoparticles through the emitter,absorber and transmittal environments via discrete ordinates radiation method

Journal of Thermal Analysis and Calorimetry - This work is a three-dimensional numerical study of a U-shaped evacuated tube solar collector employing different types of oxide nanofluids including...     

Separation/Competitive Transport of Heavy Metal Ions across the Bulk Liquid Membranes with N,N',N'-Tris(4-methylphenyl)phosphoric Triamide As Carrier

Russian Journal of Physical Chemistry A - The competitive bulk liquid membrane transport of seven metal ion by N,N',N''-tris(4-methylphen-yl)phosphoric triamide carrier was studied...     

High-Resolution Temporo-Ensemble PIV to Resolve Pore-Scale Flow in 3D-Printed Fractured Porous Media

Transport in Porous Media - Fractures are conduits that can enable fast advective transfer of (fluid, solute, reactant, particle, etc.) mass and energy. Such fast transfer can significantly affect...     

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

Similar to advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI-driven modeling and computational approaches that are currently applied to those objects. An application-driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO₂ conversion as a use case.     

Interactive fuzzy goal programming for a multi-objective closed-loop logistics network

Over the last decade, there has been increased attention to closed-loop logistics networks. Environmental legislation requires companies to be more responsible by collecting used products from customers. Companies can also benefit from savings that are related to recovering and recycling used products. Unlike previous studies, which only consider single products or a single period of time in multi-objective problems, this paper considers a multi-product multi-period closed-loop logistics network with different types of facilities. A?multi-objective mixed-integer nonlinear programming formulation is developed to minimize the total cost, the delivery time of new products, and the collection time of used products. Thus, this model better approximates real-life applications of closed-loop logistics problems. Interactive fuzzy goal programming (IFGP) is applied to solve the model for handling multiple objective problems with conflicting objectives and to address the imprecise nature of decision-makers?? aspiration levels for goals. The results from computational experiments performed here show that by changing the upper or lower bound of each objective function, one can obtain a better final solution of the problem and also can provide more options for decision makers to choose from based on their situation. Finally, the utilization rate of facilities is shown to be an important indicator when designing a logistics network.     

Intelligent control of chaos using linear feedback controller and neural network identifier

A method for controlling chaos when the mathematical model of the system is unknown is presented in this paper. The controller is designed by the pole placement algorithm which provides a linear feedback control method. For calculating the feedback gain, a neural network is used for identification of the system from which the Jacobian of the system in its fixed point can be approximated. The weights of the neural network are adjusted online by the gradient descent algorithm in which the difference between the system output and the network output is considered as the error to be decreased. The method is applied on both discrete-time and continuous-time systems. For continuous-time systems, equivalent discrete-time systems are constructed by using the Poincare map concept. Two discrete-time systems and one continuous-time system are tested as examples for simulation and the results show good functionality of the proposed method. It can be concluded that the chaos in systems with unknown dynamics may be eliminated by the presented intelligent control system based on pole placement and neural network.     

Application of particle swarm optimization in chaos synchronization in noisy environment in presence of unknown parameter uncertainty

In this paper, particle swarm optimization (PSO) is applied to synchronize chaotic systems in presence of parameter uncertainties and measurement noise. Particle swarm optimization is an evolutionary algorithm which is introduced by Kennedy and Eberhart. This algorithm is inspired by birds flocking. Optimization algorithms can be applied to control by defining an appropriate cost function that guarantees stability of system. In presence of environment noise and parameter uncertainty, robustness plays a crucial role in succeed of controller. Since PSO needs only rudimentary information about the system, it can be a suitable algorithm for this case. Simulation results confirm that the proposed controller can handle the uncertainty and environment noise without any extra information about them. A comparison with some earlier works is performed during simulations.