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Published in Luis Liz-Marzán, Colloidal Synthesis of Plasmonic Nanometals, 2020
Stefanos Mourdikoudis, Luis M. Liz-Marzán
The third room-temperature ferromagnetic element, Ni, has also been synthesized in nanoscale sizes with the help of OAm. Highly disordered Ni nanoparticles with a size range of 8–16 nm were produced by decomposition of nickel acetylacetonate (Ni(acac)2), in the presence of OAm as ligand, together with OAc and trioctylphosphine (TOP).15 Carenco et al. investigated the role of the binary ligand system comprising OAm and TOP for the synthesis of monodisperse, size tunable (ca. 2 to 30 nm) Ni nanoparticles, by the thermal decomposition of Ni(acac)2.16 OAm served as the main reducing agent while TOP provided a tunable surface stabilization through coordination on the Ni(0) surface. The simple route of combining Ni(acac)2 with OAm at high temperatures was also studied by Zhang et al. through three different independent processes: direct thermolysis, seed-assisted growth, and hot injection. The product contained single-crystalline fcc Ni particles (20–60 nm) with narrow size distributions.17 On the other hand, applying a strong reducing agent such as borane tributylamine (BTB) resulted in the production of small (~3 nm) Ni nanoparticles using Ni(acac)2 as precursor and OAm as solvent and cosurfactant, together with OAc.18 Those particles showed good catalytic behavior for hydrogen release from the hydrolysis of ammonia borane at ambient conditions.
Thermochemistry, Electrochemistry, and Solution Chemistry
Published in W. M. Haynes, David R. Lide, Thomas J. Bruno, CRC Handbook of Chemistry and Physics, 2016
W. M. Haynes, David R. Lide, Thomas J. Bruno
Name 1,1,1,3-Tetrachloro-2,2,3,3-tetra uoropropane Tetracosane Tetradecane Tetradecanoic acid 1-Tetradecanol Tetraethylsilane Tetra uoroethene Tetra uoroethene Tetra uoromethane Tetra uoromethane Tetra uoromethane Tetrahydro-2,5-dimethoxyfuran Tetrahydro-2,5-dimethoxyfuran 1,2,3,4-Tetrahydronaphthalene Tetrahydropyran Tetrahydropyran 1,2,4,5-Tetramethylbenzene N,N,N',N'-Tetramethyl-4,4'diaminobenzophenone Tetramethylsilane eophylline ioacetamide iourea iourea 2- ioxo-4-thiazolidinone iram - reonine - reonine - reonine - reonine - reonine - reonine ymidine ymine ymol Tolazamide Tolbutamide o-Tolidine Toluene Toluene Toluene Toluene p-Toluenesulfonic acid o-Toluic acid m-Toluic acid p-Toluic acid p-Toluic acid p-Toluic acid 1,3,5-Triazine-2,4,6-triamine 1,3,5-Triazine-2,4,6-triamine 1H-1,2,4-Triazol-3-amine 1,2,4-Tribromobenzene 1,3,5-Tribromobenzene 1,1,2-Tribromoethane Tribromo uoromethane Tribromomethane 2,4,6-Tribromophenol Tributylamine Tributyl phosphate Tributyrin Trichloroacetaldehyde
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Published in Eli Ruckenstein, Hangquan Li, Chong Cheng, Concentrated Emulsion Polymerization, 2019
Styrene (Aldrich), divinylbenzene (DVB) (containing 45% 3- and 4-ethylvinylbenzene) (Aldrich) and VBC (Kodak) were used after distillation under reduced pressure. The dispersant, sodium dodecylsulphate (SDS) (Aldrich), was used as received. The initiator, azobis(isobutyronitrile) (AIBN) (Alfa), was recrystallized from methanol. Tributylphosphine (TBP) (99%, Aldrich), triethylphosphine (TEP) (99%, Aldrich), tributylamine (TBA) (99%, Aldrich), triethylamine (TEA) (99%, Aldrich), trimethyl-amine (TMA) (99%, Aldrich), ethyl bromoacetate (98%, Aldrich), 2,2-dimethyl-l,3-dioxane-4,6-dione (98%, Aldrich), potassium chromate (98%, ACS grade, Aldrich), silver nitrate (>99%, ACS grade, Aldrich) and various solvents were used without further purification.
Reactive separation of malic acid from aqueous solutions and modeling by artificial neural network (ANN) and response surface methodology (RSM)
Published in Journal of Dispersion Science and Technology, 2022
Tais Evlik, Yavuz Selim Aşçı, Nilay Baylan, Halil Gamsızkan, Süheyla Çehreli
Reactive separation of malic acid from aqueous solutions has been tested commonly by using amine-based extractants in solvents such as hexane, toluene, chloroform, methyl isobutyl ketone, and octanol.[18,37–44] However, there are limited modeling studies about the reactive separation of malic acid with amine-based extractants in the literature.[45] In this study, the experimental data and modeling of reactive separation of malic acid from aqueous solutions has been examined by using tributylamine (TBA) in octyl acetate as an untested solvent. The effects of initial malic acid concentration, initial TBA concentration in organic phase, and phase ratio (organic phase volume/aqueous phase volume) on the reactive separation were determined experimentally. In order to obtain an objective conclusion, there is a need for statistical analysis and modeling of experimental results. In this context, the experimental data were analyzed, optimized and modeled by both artificial neural network (ANN) and response surface methodology (RSM). RSM is a collection of statistical and mathematical techniques that explains the relationship between independent variables (factors) and responses. The objective of RSM is the finding the optimal response.[46] The factors are experimental variables that can be changed independently of each other. Responses or dependent variables are the measured or calculated values of the results from the experiments. Among the most known designs are Box–Behnken design, central composite design, and D-optimal design.[47] Central composite design was employed to obtain the optimum separation conditions and to explore the relationship between the variables and response. The various variables such as initial malic acid concentration, TBA concentration in organic phase, and organic/aqueous phase ratio were selected as factors, and extraction efficiency was chosen as a response. On the other hand, in recent years, ANN method for the design and optimization is widely preferred.[48–50] ANN is a simplified mathematical and computational model inspired by the biological neural networks structure.[51] ANNs are mostly arranged in three kinds of layers, the input layer which receives the input parameter values, the output layer which yields the model output and one or multiple hidden layers between them. In this study, ANN model was designed to have initial malic acid concentration, TBA concentration in organic phase, and phase ratio as input layers, while the extraction efficiency was the output.