In physicochemical studies on the sea-surface microlayer (SML) in seawater, the main researches conducted were as follows: (1) It was found that there is an objective layer of sudden change in physical and chemical properties between the SML and the subsurface layer in seawater. (2) The SML thickness was determined and should be about 50?10 ?m. (3) The Gibbs model of the SML was extended, and the multilayer model of the SML was advanced. (4) The original-location method, which corresponds with the traditional removal-location method, was founded and used to determine the SML thickness. The results obtained from the two methods were almost identical. (5) An abnormal phenomenon was found when the Gibbs solution adsorption was applied to the seawater system, the reason for which was discussed preliminarily.

Brefeldin A (BFA; C₁₆H₂₄O₄, MW 280), a naturally occurring macrolide, exhibits diverse biological activities, such as antibiotic, antiviral, cytostatic, antimitotic, and antitumor effects. Owing to the wide activities of BFA, there is a need to develop a method for rapid identification of BFA in crude microbial extracts. In this paper, a method has been established and validated for screening and identification of individual as well as total BFA by high-performance liquid chromatography and liquid chromatography/electrospray mass spectrometry in fungal raw materials.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.

A new synthetic method for the manufacture of glutaric dialdehyde is investigated. Glutaric dialdehyde was prepared by the addition-hydrolysis reaction of benzimidazolium salt with saturated dihalide as the di-Grignard reagent. The yield of glutaric dialdehyde by this method can reach 73%. Both infrared spectra and melting point of the compound were consistent with those reported earlier.

Novel phosphazene cyclomatrix network polymers were synthesized via the nucleophilic displacement of activated nitro groups of tri(4-nitrophenoxy)tri(phenoxy) cyclotriphosphazene and hexa(p-nitrophenoxy) cyclotriphosphazene with hydroxyls of bisphenol A. Both monomers and polymers were characterized by Fourier transform infrared spectroscopy,¹ H nuclear magnetic resonance, and elemental analysis measurements, and their structures were identified. Thermal properties of polymers were investigated using dynamic thermogravimetric analysis in air. The results demonstrated that both cyclomatrix phosphazene polymers 4 and 6 were of excellent thermal stability, and their char yields in air at 800 °C were 45.1 and 43.2%, respectively. According to combustion phenomenon, polymer 4 was supposed to be processed with a good flame-retardant property because of its excellent crosslinked structure during pyrolysis or combustion. However, polymer 6 yielded the opposite result.

X-5 resin, with higher adsorption and easier desorption of naringin, was selected from five kinds of macroporous resins through static adsorption and desorption experiments. Effects of concentration, pH value, and flow rate of naringin extract on the adsorption of naringin by X-5 resin were studied. Meanwhile, the effect of these factors on the desorption of naringin from X-5 resin was also investigated. The experimental results show that the adsorption isotherm of naringin by X-5 resin can be described by the Langmuir isotherm equation. The static maximum adsorption capacity of naringin is 32.6 mg/g with naringin concentration at 2.7 g/L, while the dynamic adsorption capacity of naringin is 23.8 mg/g with naringin extract flow rate at two times that of resin volume per hour. The optimal eluant is 60% (v/v) ethanol water with pH value of 10. The desorption ratio will rise to more than 85% when the flow rate of this optimal eluant is one to two times that of resin volume per hour.

A new environmentally friendly catalyst, H₄Si W₁₂O₄₀-polyaniline (PAn), was prepared, and n-butyraldehyde 1,2-propanediol acetal was synthesized from n-butyraldehyde and 1,2-propanediol in the presence of H₄Si W₁₂O₄₀-PAn. The influence factors of the synthesis were discussed, and the best reaction conditions were found: the molar ratio of n-butyraldehyde to 1,2-propanediol is 1:1.5, the amount of catalyst used is 1.2% of feed stock, and the reaction time is 1.0 h. H₄Si W₁₂O₄₀-PAn is an excellent catalyst for synthesizing n-butyraldehyde 1,2-propanediol acetal, and the yield can reach more than 95.2%.

Many food algae and red tide algae were cultivated in the f/2 medium, and the nitric oxide (NO) concentration of the medium and the cell density were determined. The experiments on algae were conducted when different concentrations of NO were added into the medium using two methods. The results show that low concentrations of NO were self-produced by marine algae during the exponential growth period and were about nmol/L level. But at the end of the period, i.e., 2 or 3 days before the cell density reaches the maximum, an NO peak appeared, with the NO concentration reaching 10 nmol/L. The NO threshold concentration exists according to the influence of exogenous NO on the marine phytoplankton growth. One type is the threshold concentration that can promote algae growth, and its value is between 10 and 1 nmol level, or even lower. The other type can inhibit the phytoplankton growth, which is about ?mol level or higher. The results indicate that red tide algae are far more sensitive to NO than are food algae. The fundamental experimental outcome above may provide a new clue for red tide chemical forecast by inspecting the NO change.

A new zirconia-based stationary phase (DPZ) was prepared by modifying zirconia with dodecylamine-N, N-dimethylenephosphonic acid (DDPA). DDPA was adsorbed on zirconia with only one phosphonic group, with the other being free. Besides the hydrophobic interaction provided by nonpolar dodecyl, DPZ also has dipolar interaction, ion-exchange or electrostatic repellent interaction provided by the free phosphonic group and amino group at different conditions. Separation of bases on this stationary phase was achieved with satisfaction owing to the various retention mechanisms. The influence of methanol content, pH value, ion types, and ionic strength of mobile phase are studied in detail.

A sensitive complex absorptive wave of Ca ARS was obtained by using differential pulse voltammetry when a mercury film glass carbon electrode was immersed in 0.1 mol L^-1 KOH and 4.5?10^-4 mol L^-1 ARS solution. The peak potential obtained was -1.17 V (vs Ag AgCl). The peak current was proportional to the concentration of calcium in the range of 5.0?10^-8 4.2?10^-5 mol L^-1. The detection limit was 2.0?10^-8 mol L^-1. This method was applied successfully to determining traces of calcium in blood serum. The electrochemical behavior of the system was also studied by cyclic voltammetry, and the experiment results showed that the electrode process was an irreversible absorptive with two electrons participating.

A series of basket-shaped hosts with ester-crown handles have been synthesized by treating oligoethylene glycol bischloroacetates [ClCH₂CO(OCH₂CH₂)_nOCOCH2 Cl, n = 1, 2, 3, 4] and 1,2-bis(hydroxyethoxy)benzene bischloroacetates with a diphenylglycoluril-based molecular clip, 1,3:4,6-bis(3,6-dihydroxy-1,2-xylylene)tetrahydro-3a, 6a-diphenylimidazo[5,5]imidazole-2,5(1H, 3H)-dione (1), in dimethyl sulfoxide using K₂CO₃ as a base in yields of 15, 35, 40, 38, and 20%. Their structures were identified by nuclear magnetic resonance and mass spectrum. The reaction conditions were also studied and optimized.

A series of N 2-(5-aryl-1,3,4-oxadiazole-2-yl) actylthioureas have been synthesized from p-chlorophenoxyacetic acid, and then substituted 2-amino-5-aryl-1,3,4-oxadiazoles reacting with acylthiocyanoester, which are derived from the second step, are used. Their structures are confirmed by Infrared, ¹H nuclear magnetic resonance, and elementary analysis. From biological testing, it is found that some of these compounds have good fungicidal activity.