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In this paper, a generic QSPR (quantitative structure-property relationship) model have been developed to investigate the relation between the critical micelle concentration (cmc) and the molecular structure of 231 gemini cationic surfactants with the various hydrophilic head group. The QSPR modeling were performed using the OCHEM (Online CHEmical Modeling environment), a web-based model development platform. The OCHEM platform provides several molecular descriptors calculation and machine learning methods as a tool to build QSPR models. Eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, ChemAxon, Inductive Descriptor, Mordred, and PyDescriptor are used to calculate molecular parameters of gemini cationic surfactants. Also, eight machine learning methods (MLRA, ASNN, kNN, LibSVM, FSMLR, DNN, RFR, and PLS) are used to develop QSPR models. A total of 64 QSPR models were generated with 6 top-ranked models. Based on the statistical coefficient of QSPR models, the model 5 which is constructed from combination of ASNN method and Mordred descriptors, provided the best QSPR models. The model 5 performed the highest predictive result with R2 = 0.95, q2 = 0.95, RMSE = 0.17, and MAE = 0.11. The model can be access on OCHEM website (https://ochem.eu/model/25147470) and can be used for prediction of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.

A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R2 = 0.95, q2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R2 = 0.87, q2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.

The critical micelle concentration (cmc) is important indexes to determine the performance of surfactants quantitatively. In this study, based on the molecular descriptors, which calculated, by Dragon software, were proposed a quantitative structure-property relationship analysis for prediction of cmc of gemini imidazolium surfactants. In the present study, the associative neural networks (ASNN) and multiple linear regression analysis (MLRA) technique were used to interpret the chemical structural functionality (molecular descriptors) against the cmc of gemini imidazolium surfactants. The models were validated rigorously through 5-fold cross-validation. The ASNN method showed to be better than the MLRA method in terms of the internal and the external prediction accuracy with high statistical quality r2 = 0.96, q2 = 0.95, RMSE = 0.08 and MAE = 0.04 respectively. The developed QSPR models are publicly available on the web site at https://ochem.eu/model/11583111 for ASNN model and https://ochem.eu/model/43858671 for MLRA model. These models can be applied to predict the cmc of new gemini imidazolium surfactants.

Development of anionic surfactant compound is very important because the anionic surfactant class is widely used in people's lives. For instance, anionic surfactants are used as food additives and detergents. T he novel compound of sulfonate -based surfactant or proposed compound ha s predicted the C ritic al M icelle C oncentration (CMC) value of experiment . Quantitative Stru cture-Property Relationship (QSPR) analysis based on semiempiri cal ZINDO/1 calculation was conducted to obtain QSPR equation. Theoretical p redictors or independent variable which have an influence on the value of CMC are used to construct QSPR equation . The theoretical predictors are classified into predictor of electronic properties, solubility and steric. A total of 108 experimental CMC belongs to sulfonate -based surfactant are calculated their theoretical predictor s and analyzed by multiple linear regression. The QSPR equation which is obtained from this study contains the important theoretical predictors . They are solubility properties, molecular weight, molecular size and net charge of carbon atom in the polar part of sulfonate-based surfactant . This QSPR equation c ould be used to predict the CMC value of the novel sulfonate-based surfactant .

A research on the synthesis of sodium pentagamavunonat (Na-PGV-0) and its stability test using UV-Visible spectrophotometer were carried out. The synthesis of Na-PGV-0 carried out by reacting PGV-0 in tetrahydrofuran solvent refluxed with sodium ethoxide in mol comparison (1:2) for two hours. Structure elucidation by spectroscopic methods using UV-Visible, IR, 1H-NMR, 13C-NMR and stability test in water using UV-Visible spectrophotometer. The reaction yields 107,21 % (% w/w) products. A products was soluble in water and methanol. Structure elucidation results indicated that the formed compound where the OH phenolic of pentagamavunon-0 was replaced by sodium ions. Stability test shows that decreasing of sodium pentagamavunonat to 70,7867 % in 600 minutes storage.

E nterobacter aerogenes merupakan bakteri penyebab berbagai macam infeksi. Salah satu pengobatan berbagai penyakit infeksi yaitu dengan pemberian antibiotik. Penggunaan antibiotik sintetik secara terus menerus dapat menyebabkan resistensi, sehingga untuk mengatasinya diperlukan pencarian bahan obat alami seperti ekstrak tanaman yang berpotensi sebagai antibakteri, salah satunya yaitu ekstrak daun mangga bacang (Mangifera foetida L.). Penelitian ini bertujuan untuk mengetahui potensi ekstrak daun mangga bacang sebagai antibakteri terhadap E. aerogenes , penentuan konsentrasi hambat tumbuh minimum (KHTM) dan mengidentifikasi golongan senyawa aktif dari ekstrak daun mangga bacang. Ekstrak daun mangga bacang diperoleh dengan cara maserasi menggunakan metanol. Ekstrak yang diperoleh diuji aktivitas antibakteri dengan menggunakan metode difusi sumur. Konsentrasi yang digunakan 1000 ppm, kontrol positif tetrasiklin 1000 ppm dan kontrol negatif aquades. Penentuan konsentrasi hambat tumbuh minimum menggunakan konsentrasi 1000 ppm, 500 ppm, 250 ppm, 125 ppm, 65 ppm, 30 ppm, 15 ppm, 10 ppm, 5 ppm. Ekstraksi daun mangga bacang menunjukan rendemen sebesar 10,61% (b/b) dan diameter zona hambat aktivitas antibakteri konsentrasi 1000 ppm sebesar 5,46 mm. KHTM ekstrak metanol daun mangga bacang terhadap E. aerogenes yaitu pada konsentrasi 5 ppm dengan diameter zona hambat sebesar 1,78 mm. Berdasarkan hasil uji fitokimia ekstrak metanol daun mangga bacang menunjukkan adanya senyawa golongan alkaloid, flavonoid, stereoid, polifenol, tanin, dan saponin. Abstract E nterobacter aerogenes is causes various bacterium infections. One of the treatment of various infectious diseases is by antibiotics treatment. The long-term use of synthetic antibiotics causes antibiotic resistance, so that research exploration of new antibiotic is needed, such as plant extracts potentially as an antibacterial, one of them is leaves extract of mango bacang ( Mangifera foetida L.). This study aimed to examine the potential of leaves mango bacang extract as an antibacterial againts E. aerogenes , the determination of minimum inhibition concentration (MIC) and active compounds of leaves mango bacang extract. Leaves mango bacang extract obtained methanol maceration. The extract obtained was tested antibacterial activity by using the diffusion wells method. Concentration used was 1000 ppm, tetracycline of 1000 ppm as positif control and aquades as negative control. The determination of MIC was used various concentrations of 1000 ppm, 500 ppm, 250 ppm, 125 ppm, 65 ppm, 30 ppm, 15 ppm, 10 ppm, and 5 ppm. The extraction mango bacang leaves with yielded 10.61 % (b/b) and diameter of zone of the antibacterial activity extract of 1000 ppm was 5.46 mm. The MIC of the methanol extract against E. aerogenes was 5 ppm with the zone diameter of 1.78 mm. Phytochemistry analysis of mango bacang leaves extract using methanol result the alkaloid, flavonoid, stereoid, catakin, tannin, and saponin.

Telah dilakukan penelitian tentang studi teoritis struktur dan spektrum elektronik kompleks [Ln(pytpy)(NO3)3] (Ln=Eu, Tb, pytpy=4’-(2-pyrrolyl)-2,2’:6’,2”-terpyridine) menggunakan metode komputasi semiempiris Sparkle/RM1. Metode Sparkle/RM1 ini dapat digunakan untuk memperoleh struktur geometri paling stabil dan parameter struktur dalam keadaan gas sebagai simulasi metode eksperimen yang mungkin terlalu mahal. Perhitungan spektrum elektronik dilakukan dengan ZINDO/S, dengan mengganti sparkle dengan titik muatan +3 pada koordinat yang dihasilkan melalui Sparkle/RM1. Hasil penelitian menunjukkan bahwa ion lantanida pada kompleks [Ln(pytpy)(NO3)3] memiliki bilangan koordinasi 9, yang berikatan dengan satu ligan tridentat pytpy dan tiga gugus nitrat sebagai ligan bidentat. Panjang ikatan rata-rata ion lantanida dengan atom N pada ligan pytpy adalah 2,541 Å dan 5,14 Å untuk Eu3+ dan Tb3+. Studi teoritis spektroskopi menunjukkan bahwa metode Sparkle/RM1 cukup akurat dalam memprediksi spektrum IR ligan pytpy, kompleks [Eu(pytpy)(NO3)3] maupun [Tb(pytpy)(NO3)3]. Spektrum UV-Vis kompleks [Eu(pytpy)(NO3)3] dan [Tb(pytpy)(NO3)3] memiliki intensitas serapan yang lebih kuat dan absorbansi yang lebih besar dari spektrum UV-Vis ligan pytpy.