Surface characterization of macroporous glycidyl methacrylate based copolymers by inverse gas chromatography

Two samples of macroporous crosslinked poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate), PGME, with different porosity parameters were synthesized by suspension copolymerization and modified by ring-opening reaction of the pendant epoxy groups with ethylene diamine, EDA. Inverse gas chromatography at infinite dilution was used for the determination of adsorption properties of PGME, and copolymer modified with ethylene diamine, PGME-en. Thermodynamic parameters of adsorption, dispersive components of the surface free energies, and the acid/base constants for the copolymer samples were calculated. The calculated dispersive surface energy values, , for PGME and PGME-en are comparable with the literature data for nonconductive polymers.     

Determination of thermodynamic properties of macroporous glycidyl methacrylate‐based copolymers by inverse gas chromatography

Macroporous crosslinked poly(glycidyl methacrylate‐co‐ethylene glycol dimethacrylate) (PGME) was synthesized by suspension copolymerization and modified by ring‐opening reaction of the pendant epoxy groups with ethylene diamine (EDA). Inverse gas chromatography (IGC) at infinite dilution was applied to determine the thermodynamic interactions of PGME and modified copolymer, PGME‐en. The specific surface areas of the initial and modified copolymer samples were determined by the BET method, from low‐temperature nitrogen adsorption isotherms. The specific retention volumes, V, of 10 organic compounds of different chemical nature and polarity (nonpolar, donor, or acceptor) were determined in the temperature range 333–413 K. The weight fraction activity coefficients of test sorbates, , and Flory–Huggins interaction parameters, , were calculated and discussed in terms of interactions of sorbates with PGME and PGME‐en. Also, the partial molar free energy, , partial molar heat of mixing, , sorption molar free energy, ΔG, sorption enthalpy ΔH, and sorption entropy, ΔS, were calculated. Glass transitions in PGME and PGME‐en, determined from IGC data, were observed in the temperature range 373–393 K and 363–373 K, respectively. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 2524–2533, 2005     

Preparative isolation of (+)-beta-eudesmol fromAmyris balsamifera

Summary Optically pure (+)-beta-eudesmol is a possible starting material for the synthesis of several termite defense compounds. A two step procedure for the isolation of gram quantities of (+)-beta-eudesmol from commercially availableAmyris balsamifera oil (syn. West Indian sandalwood oil), containing 8% beta-eudesmol, was developed. Step one consisted of an efficient vacuum distillation of the total oil. Step two was a medium pressure LC separation with an AgNO₃ impregnated silica gel stationary phase. Several other separation procedures failed due to the presence of many closely related sesquiterpene alcohols (75% of the oil).     

Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons

An interpretative strategy (factorial design experimentation+total resolution analysis+chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% dimethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.     

An inverse gas chromatography study of the adsorption of organics on nickel- and copper-hexacyanoferrates at zero surface coverage

The surface properties of Ni and Cu hexacyanoferrates were investigated by the inverse gas chromatography method. Retentions of 10 organic compounds were measured at zero surface coverage in the temperature range 80 to 95 degrees C. The gas/solid partition coefficients and the related thermodynamic data of adsorption (standard free energy change, standard state enthalpies, and entropy changes) also at zero surface coverage were determined. The dispersive component of free surface energy of both hexacyanoferrates, at investigated temperatures, was calculated. The surface acid/base properties were also evaluated using polar adsorbates and the results obtained indicate that nickel hexacyanoferrate is more acidic than copper hexacyanoferrate.     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

Flow-injection chemiluminescence determination of formaldehyde in water

A modification of the Trautz-Schorigin reaction into a flow-injection analysis configuration is described. Different approaches were used at the optimization of chemiluminescence determination of formaldehyde in water based on the reaction of formaldehyde, gallic acid and hydrogen peroxide in an alkaline solution. Detection system with a 218 μl chemiluminescence cell was optimized by both a one-variable-at-a-time method, and a modified simplex method. A calibration graph is linear in the concentration range 4 × 10⁻⁸ to 1 × 10⁻⁵ M HCHO. The detection limit of formaldehyde for a signal-to-noise ratio of 3 is 4 × 10⁻⁸ M. The relative standard deviations for 15 repeated measurements of 1 × 10⁻⁶ and 5 × 10⁻⁶ mol l⁻¹ HCHO are 4.32 and 3.33%, respectively. The analysis time is 1.5 min. The method was applied to the determination of formaldehyde in urban rainwater. A comparison of results found by proposed method with those obtained by fluorimetric reference method provided a good agreement.     

Artificial neural network data analysis for classification of soils based on their radionuclide content

The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from which they were collected. Their radionuclide (²²⁶Ra, ²³⁸U, ²³⁵U, ⁴⁰K, ¹³⁴Cs, ¹³⁷Cs, ²³²Th, and ⁷Be) activities detected by gamma-ray spectrometry were then used as inputs to ANN. Five different training algorithms with different numbers of samples in training sets were tested and compared in order to find the one with the minimum root mean square error (RMSE). The best predictive power for the classification of soils from the fifteen regions was achieved using a network with seven hidden layer nodes and 2500 training epochs using the online back-propagation randomized training algorithm. With the optimized ANN, most soil samples not included in the ANN training data set were correctly classified at an average rate of 92%. The text was submitted by the authors in English.