Intelligent Numerical Computing Methods for Determining the Concentration of Citric Acid by Accurate Measurement and Calculation

According to the Bronsted-Lowry theory, an acid is a proton donor, and a base is a proton acceptor. An acid-base reaction involves the proton transfer between chemicals, where a base containing hydroxide ion (OH-) accepts a proton (H+) from an acidic solution to form water (Khan,2016). In the above equation, HCl as an acid donates one H+ ion, and NaOH as a base accepts the proton to form one water molecule (H2O). So, a proton from the acid is transferred to the anion of the base. Then, the metal cation (Na+) and the conjugate base anion (Cl-) form the salt NaCl.

Thermodynamic basis of the α-helix and DNA duplex

Analysis of calorimetric and crystallographic information shows that the α-helix is maintained not only by the hydrogen bonds between its polar peptide groups, as originally supposed, but also by van der Waals interactions between tightly packed apolar groups in the interior of the helix. These apolar contacts are responsible for about 60% of the forces stabilizing the folded conformation of the α-helix and their exposure to water on unfolding results in the observed heat capacity increment, i.e. the temperature dependence of the melting enthalpy. The folding process is also favoured by an entropy increase resulting from the release of water from the peptide groups. A similar situation holds for the DNA double helix: calorimetry shows that the hydrogen bonding between conjugate base pairs provides a purely entropic contribution of about 40% to the Gibbs energy while the enthalpic van der Waals interactions between the tightly packed apolar parts of the base pairs provide the remaining 60%. Despite very different structures, the thermodynamic basis of α-helix and B-form duplex stability are strikingly similar. The general conclusion follows that the stability of protein folds is primarily dependent on internal atomic close contacts rather than the hydrogen bonds they contain.

Liquid-liquid extraction of Nd+3 and Eu+3 from aqueous medium using oxytetracycline in dichloromethane

Liquid-liquid extraction system consisting of oxytetracycline (HOTC) in dichloromethane was developed for extraction of Nd+3 and Eu+3 from acidic solutions using radiometric technique. Various extraction parameters such as pH, equilibration time and metals concentration were optimized. Slope analysis method was used to study the composition of product and was found to be M(OTC)3 [here M=Nd+3 or Eu+3 and OTC = conjugate base of HOTC]. Among cations Al+3 and Fe+3 while among anions F− effected the extraction of these metals. Higher separation factor of these metals was found with many others. The method was successfully applied on spiked sea water.

Dynamic Parallel Kinetic Resolution of α-Ferrocenyl Cation Initiated by Chiral Brønsted Acid Catalyst

The dynamic parallel kinetic resolution (DPKR) of an α-ferrocenyl cation intermediate under the influence of a chiral conjugate base of a chiral phosphoric acid catalyst has been demonstrated in an...

Using Machine Learning to Predict the pKa of C–H Bonds. Relevance to Catalytic Methane Functionalization

Six machine learning models (random forest, neural network, support vector machine, k-nearest neighbors, Bayesian ridge regression, least squares linear regression) were trained on a dataset of 3d transition metal-methyl and -methane complexes to predict p<i>K_a</i>(C–H), a property demonstrated to be important in catalytic activity and selectivity. Results illustrate that the machine learning models are quite promising, with RMSE metrics ranging from 4.6 to 8.8 p<i>K_a</i> units, despite the relatively modest amount of data available to train on. Importantly, the machine learning models agreed that (a) conjugate base properties were more impactful than those of the corresponding conjugate acid, and (b) the energy of the highest occupied molecular orbital conjugate base was the most significant input feature in the prediction of p<i>K_a</i>(C–H). Furthermore, results from additional testing conducted using an external dataset of Sc-methyl complexes demonstrated the robustness of all models, with RMSE metrics ranging from 1.5 to 6.6 p<i>K_a</i> units. In all, this research demonstrates the potential of machine learning models in organometallic catalyst development.

Using Machine Learning to Predict the pKa of C–H Bonds. Relevance to Catalytic Methane Functionalization

Six machine learning models (random forest, neural network, support vector machine, k-nearest neighbors, Bayesian ridge regression, least squares linear regression) were trained on a dataset of 3d transition metal-methyl and -methane complexes to predict p<i>K_a</i>(C–H), a property demonstrated to be important in catalytic activity and selectivity. Results illustrate that the machine learning models are quite promising, with RMSE metrics ranging from 4.6 to 8.8 p<i>K_a</i> units, despite the relatively modest amount of data available to train on. Importantly, the machine learning models agreed that (a) conjugate base properties were more impactful than those of the corresponding conjugate acid, and (b) the energy of the highest occupied molecular orbital conjugate base was the most significant input feature in the prediction of p<i>K_a</i>(C–H). Furthermore, results from additional testing conducted using an external dataset of Sc-methyl complexes demonstrated the robustness of all models, with RMSE metrics ranging from 1.5 to 6.6 p<i>K_a</i> units. In all, this research demonstrates the potential of machine learning models in organometallic catalyst development.

Insights into the behavior of nonanoic acid and its conjugate base at the air/water interface through a combined experimental and theoretical approach

The surface partitioning of a medium chain fatty acid and its conjugate base has been investigated through a combined experimental and theoretical approach of the multi-equilibria involved in the bulk phase and at the air/water interface.

Reaction of Carbidopa with cis-[Cr(C2O4)2(H2O)2]− in Aqueous Medium: A Kinetic, Mechanistic and Antiparkinsonian Study of the Product Complex

For the treatment of Parkinson′s disease, the second most common neurodegenerative disorder, requires a combination of levodopa with a peripheral decarboxylase inhibitor, such as carbidopa which provides a symptomatic relief to patients. Reaction of carbidopa with cis-[Cr(C2O4)2(H2O)2]– has been carried out in aqueous medium over the range 35 ≤ t ≤ 50 ºC, 4.0 ≤ pH ≤ 6.0 , 3.75 × 10-3 mol dm-3 ≤ [carbidopa] ≤ 9.38 × 10-3 mol dm-3, I (KNO3) = 0.1 mol dm-3. There is outersphere association between cis-[Cr(C2O4)2(H2O)2]– and conjugate base of carbidopa followed by first chelation. The characterization of the product was performed by using NMR and infrared spectroscopies. The product showed better antiparkinsonian activity than the combination of levodopa and carbidopa