A Series of Thiadiazolyl-Benzenesulfonamides Incorporating an Aromatic Tail as Isoform-Selective,Potent Carbonic Anhydrase II/XII Inhibitors

We describe the synthesis of a series of thiadiazolyl-benzenesulfonamide derivatives carrying an aromatic tail linked by an amide linker ( 12–34 ), as human carbonic anhydrase (hCA) inhibitors. These thiadiazol derivatives were evaluated against four physiologically relevant CA isoforms (hCA I, II, IX, and XII), and demonstrated intriguing inhibitory activity against CA II with K_i values in the range of 2.4–31.6 nM. Besides hCA II, also hCA XII activity was potently inhibited by some of the derivatives (K_i=1.5–88.5 nM), producing dual inhibitors of both isoforms. Notably, compound 17 was the most potent dual CA II (K_i=3.1 nM) and XII (K_i=1.5 nM) inhibitor with a significant selectivity ratio over CA I and IX isoforms. In conclusion, although all compounds exhibited preferential activity towards hCA II, the nature of the substituents at the tail part of the main scaffold influenced the activity and selectivity toward other isoforms.     

Ab initio and DFT study on 1,4-dinitroglycoluril configurational isomers: cis-DINGU and trans-DINGU

cis-1,4-Dinitroglycoluril (1,4-dinitrotetrahydroimidazo[4,5-d]imidazole-2,5-(1H,3H)-dione, C(4)H(4)N(6)O(6), known as DINGU in the literature) is an important explosive regarded as one of the potential ingredients for LOVA (low vulnerability ammunition) applications. However, there is no study concerning trans-DINGU, to the best of our knowledge. Thus, in the present study the structural and electronic properties of the configurational isomers of DINGU (cis- and trans-isomers) have been investigated by performing density functional theory (DFT) calculations at B3LYP/6-31G(d,p), UB3LYP/6-31G(d,p) levels and also ab initio calculations at RHF/6-31G(d,p) and UHF/6-31G(d,p) levels. The optimized geometries, vibrational frequencies, electronic structures, and some thermodynamical values for the presently considered DINGU isomers have been obtained in their ground states. Comparing the calculated energy data (total electronic energy, heat of formation values and homolytic bond dissociation values of N-NO(2)) and the calculated bond lengths, cis-DINGU was found to be a more stable molecule than the trans-isomer.     

Quantum chemical study on 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO) and some of its constitutional isomers

Presently, certain isomeric compounds of NTO and their tautomers have been investigated by performing density functional theory (DFT) calculations at B3LYP/6-31G(d,p) and ROB3P86/6-311G(d,p) levels and also ab initio calculations at RHF/6-311G(d,p) level. The optimized geometries, vibrational frequencies, electronic structures and some thermodynamical values for the presently considered NTO isomers have been obtained in their ground states. Also, detonation performances were evaluated by the Kammlet-Jacobs equations, based on the calculated densities and heat of formation values. The homolytic bond dissociation energies (BDEs) (at ROB3P86/6-311G(d,p) level) of NNO(2) and CNO(2) for the molecules were calculated. Moreover, aromatic character of NTO and its isomers and tautomers were investigated by performing NICS calculations using the gauge invariant atomic orbital (GIAO) approach at the B3LYP/6-31G(d,p) and B3LYP/cc-pVDZ levels.     

Solution-processed LiF for work function tuning in electrode bilayers

Although ambient processing is the key to low-cost organic solar cell production, high-vacuum thermal evaporation of LiF is often a limiting step, motivating the exploration of solution processing of LiF as an alternative electrode interlayer. Submonolayer films are realized with the assistance of polymeric micelle reactors that enable LiF particle deposition with controlled nanoscale surface coverage. Scanning Kelvin probe reveals a work function tunable with nanoparticle coverage with higher values than that of bare indium tin oxide (ITO).     

Influence of annealing duration on the erosive wear behavior of polyphenylenesulphide composites

The influence of annealing duration on the erosive wear behavior of short glass fiber (40% w/w) and CaCO₃ mineral particulate (25% w/w)–short glass fiber (40% w/w) (total: 65% w/w) reinforced PPS composites has been characterized under various experimental conditions by differential scanning calorimetry (DSC) and erosion measurements. The erosive wear of the composites have been evaluated at different impingement angles (30, 45, 60, and 90°) and at four different annealing periods (30, 60, 90, and 120 min). Increase in the total crystallization causes an improvement in the erosive wear properties of the samples. Annealing time controls the morphology by influencing the degree of crystallinity in the matrix and in the fiber–matrix interface. This formation restricts fiber–matrix debonding. There is no linear proportionality between annealing time and relative degree of crystallization. The results indicate that PPS composites show maximum in wear versus impact angle relation at 60° confirming their semi-ductile failure behavior. The morphologies of eroded surface are examined by the scanning electron microscope.     

Optimization of the Pore Structures of MOFs for Record High Hydrogen Volumetric Working Capacity

Metal–organic frameworks (MOFs) are promising materials for onboard hydrogen storage thanks to the tunable pore size, pore volume, and pore geometry. In consideration of pore structures, the correlation between the pore volume and hydrogen storage capacity is examined and two empirical equations are rationalized to predict the hydrogen storage capacity of MOFs with different pore geometries. The total hydrogen adsorption under 100 bar and 77 K is predicted as n_tot= 0.085× V_p − 0.013× V_p² for cage-type MOFs and n_tot= 0.076× V_p − 0.011× V_p² for channel-type MOFs, where V_p is the pore volume of corresponding MOFs. The predictions by these empirical equations are validated by several MOFs with an average deviation of 5.4%. Compared with a previous equation for activated carbon materials, the empirical equations demonstrate superior accuracy especially for MOFs with high surface area (i.e., S_BET over ≈3000 m² g⁻¹). Guided by these empirical equations, a highly porous Zr-MOF NPF-200 (NPF: Nebraska Porous Framework) is examined to possess outstanding hydrogen total adsorption capacity (65.7 mmol g⁻¹) at 77 K and record high volumetric working capacity of 37.2 g L⁻¹ between 100 and 5 bar at 77 K.     

Incremental construction of classifier and discriminant ensembles

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ensembles, we test subset selection and trees. Fusion is by voting or by a linear model. Using 14 classifiers on 38 data sets, incremental search finds small, accurate ensembles in polynomial time. The discriminant ensemble uses a subset of discriminants and is simpler, interpretable, and accurate. We see that an incremental ensemble has higher accuracy than bagging and random subspace method; and it has a comparable accuracy to AdaBoost, but fewer classifiers.     

Ordering and finding the best of K > 2 supervised learning algorithms

Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the multitest algorithm, whereby we order supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the "best" learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their "goodness." Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test.     

On the class of λ-statistically convergent difference sequences of fuzzy numbers

In this study, we introduce the sets $\left[ V,\lambda ,p\right] _{\Updelta }^{{\mathcal{F}}},\left[ C,1,p\right] _{\Updelta }^{{\mathcal{F}}}$ and examine their relations with the classes of $ S_{\lambda }\left( \Updelta ,{\mathcal{F}}\right)$ and $ S_{\mu }\left( \Updelta ,{\mathcal{F}}\right)$ of sequences for the sequences $\left( \lambda _{n}\right)$ and $\left( \mu _{n}\right) , 0<p<\infty $ and difference sequences of fuzzy numbers.