Copper partitioning between granitic silicate melt and coexisting aqueous fluid at 850°C and 100 MPa

Experiments on the partitioning of Cu between different granitic silicate melts and the respective coexisting aqueous fluids have been performed under conditions of 850 ℃, 100 MPa and oxygen fugacity （fO2） buffered at approaching Ni-NiO （NNO）. Partition coefficients of Cu （Dcu = Cfluid/Cmelt） were varied with different alumina/alkali mole ratios [Al2O3/（Na2O ＋ K2O）, abbreviated as Al/ Alk], Na/K mole ratios, and SiO2 mole contents. The DCu increased from 1.28 ± 0.01 to 22.18 ±0.22 with the increase of Al/Alk mole ratios （ranging from 0.64 to 1.20） and Na/K mole ratios （ranging from 0.58 to 2.56）. The experimental results also showed that Dcu was positively correlated with the HCl concentration of the starting fluid. The Dcu was independent of the SiO2 mole content in the range of SiO2 content considered. No Dcu value was less than 1 in our experiments at 850 ℃ and 100 MPa, indicating that Cu preferred to enter the fluid phase rather than the coexisting melt phase under most conditions in the melt-fluid system, and thus a significant amount of Cu could be transported in the fluid phase in the magmatichydrothermal environment. The results indicated that Cu favored partitioning into the aqueous fluid rather than the melt phase if there was a high Na/K ratio, Na-rich, peraluminous granitic melt coexisting with the high Cl^- fluid.

Partition coefficient prediction of Baker's yeast invertase in aqueous two phase systems using hybrid group method data handling neural network

A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting(AARD = 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.

Efficient Separation of Butyric Acid by an Aqueous Two-phase System with Calcium Chloride

One of the bottlenecks for bioproduction of butyric acid as bulk chemical is the difficulty in separating butyric acid from the fermentation broth,compared with the petroleum-based chemical synthesis method.In the present work,a novel separation methodology was developed based on an aqueous two-phase system with inor-ganic salts.Calcium chloride was screened out for effective separation of butyric acid from butyric acid-water-salt systems.Within appropriate concentration range of butyric acid and salt,butyric acid was enriched in the upper phase and most of calcium ions remained in the lower phase.This"salting out"effect is very efficient to separate butyric acid from the simulated butyrate fermentation broth,which consists of butyric acid and acetic acid with concentration ratio of 4︰1,so that the final ratio of butyric acid/acetic acid in the upper phase is improved to 9.87. The aqueous two-phase system was used to separate butyric acid from the actual fermentation broth with satisfac-tory result.

DFT Study on Dissolvability-structure Relationship of Halogeno-benzene

Structural parameters of 36 fluorobenzene, chlorobenzene and bromobenzene compounds were computed at the B3LYP/6-31G^＊ level with DFT method. Based on the experimental data of aqueous solubility （–lgSw） and n-octanol/water partition coefficient （lgKow）, using the modified theoretical linear solvation energy relation （MTLSER） model, correlation equations that can predict –lgSw and lgKow were developed using structural parameters as theoretical descriptors. The correlation coefficients （r） of these equations are 0.9728 and 0.9967, respectively. These equations were further validated by variance inflation factors （VIF） and t-test methods and then used to predict –lgSw and lgKow values. By comparison, the correlation and predictive ability of our work are more advantageous than those calculated from valence molecular connectivity indices method and molecular topology index method.