Water-induced formation of an alkali-ion dimer in cryptomelane nanorods

Tunneled metal oxides such as α-Mn₈O₁₆ (hollandite) have proven to be compelling candidates for charge-storage materials in high-density batteries. In particular, the tunnels can support one-dimensional chains of K⁺ ions (which act as structure-stabilizing dopants) and H₂O molecules, as these chains are favored by strong H-bonds and electrostatic interactions. In this work, we examine the role of water molecules in enhancing the stability of K⁺-doped α-Mn₈O₁₆ (cryptomelane). The combined experimental and theoretical analyses show that for high enough concentrations of water and tunnel-ions, H₂O displaces K⁺ ions from their natural binding sites. This displacement becomes energetically favorable due to the formation of K²⁺ dimers, thereby modifying the stoichiometric charge of the system. These findings have potentially significant technological implications for the consideration of cryptomelane as a Li⁺/Na⁺ battery electrode. Our work establishes the functional role of water in altering the energetics and structural properties of cryptomelane, an observation that has frequently been overlooked in previous studies.

Water displaces potassium ions and initiates the formation of a homonuclear dimer ion (K²⁺) in the tunnels of hollandite.     

A process control method for the electric current-activated/assisted sintering system based on the container-consumed power and temperature estimation

Journal of Thermal Analysis and Calorimetry - In this paper, the current, voltage and temperature of the container of an electric current-activated/assisted sintering (ECAS) system are measured and...     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

An efficient one-pot multicomponent approach to 5-amino-7-aryl-8-nitrothiazolo[3,2-a]pyridines

A series of thiazolo[3,2-a]pyridines have been prepared using a multicomponent reaction between aromatic aldehydes, 2-nitromethylenethiazolidine and nitriles containing an active methylene group (malononitrile, ethyl 2-cyanoacetate and 2-phenylsulfonylacetonitrile) in the presence of Et₃N under mild conditions with high yields. One of the compounds shows promising anticancer activity across a range of cancer cell lines.     

Large-scale plasmonic microarrays for label-free high-throughput screening

Microarrays allowing simultaneous analysis of thousands of parameters can significantly accelerate screening of large libraries of pharmaceutical compounds and biomolecular interactions. For large-scale studies on diverse biomedical samples, reliable, label-free, and high-content microarrays are needed. In this work, using large-area plasmonic nanohole arrays, we demonstrate for the first time a large-scale label-free microarray technology with over one million sensors on a single microscope slide. A dual-color filter imaging method is introduced to dramatically increase the accuracy, reliability, and signal-to-noise ratio of the sensors in a highly multiplexed manner. We used our technology to quantitatively measure protein-protein interactions. Our platform, which is highly compatible with the current microarray scanning systems can enable a powerful screening technology and facilitate diagnosis and treatment of diseases.     

Mass-analyzed threshold ionization spectroscopy of pyridine. Structural distortion in the first excited state

For the first time, vibrational spectra of the pyridine cation in the electronic ground state have been measured via several intermediate states (0(0), 16b0(2), 16b0(4), 6a0(1), 6b(1), 16a0(1), 10a0(1) and 12(1)) by Mass-Analyzed Threshold Ionization (MATI) spectroscopy. From the MATI spectra, the adiabatic ionization energy of pyridine has been determined to be 74,185 +/- 6 cm(-1) (9.1978 +/- 0.0008 eV). Several vibronic modes in the ionic ground state could be assigned for the first time. An intensity gain of vibrations having b1 symmetry could be observed by activating the ion ground state. Also, a breakdown of the "delta nu = 0 propensity rule" for the excitation via the 16b(2) and 16b(4) states of the first excited states are displayed in the recorded spectra. In conjunction with ab initio calculations these observations can be explained by a strong geometrical distortion along the 16b vibration in the first excited state, leading to a "boat distortion".     

Modified Burgers-Reimschuessel model for moisture-sensitive polymers

Viscoelastic properties of moisture-sensitive polymers can be significantly affected by moisture in the ambient environment, resulting in drastic changes in the properties as the absorbed moisture content increases. In this article, a simple yet important modification to the Reimschuessel model is introduced by considering both plasticization and anti-plasticization induced by water molecules. The proposed model is validated against the results of four different polymers obtained by Onogi et al., which demonstrates its capability of describing the available data. This model can be used to estimate the performance and service life of products produced using moisture-sensitive polymers. It also reveals that small amounts of diffused moisture might have a stiffening effect on the mechanical properties of hydrophilic polymers.     

A study on theoretical predictive model and experimental findings of melt-electrospinning process

The current study extends a previous work published by the authors in which an analytical predictive model is proposed to simulate the melt-electrospinning process. The analytical model is specifically designed to predict the various behaviors of the melt-electrospun fiber under different material and processing conditions. A brief discussion of this model is presented here to establish context and help the reader capture the modeling philosophy employed. The current study complements the previous work by focusing on the experimental aspects of the research. Correlations between the independent process parameters and the topological attributes of the melt-electrospun fibers are investigated and compared with findings from the theoretical model. The effects of changes in the process parameters on average fiber diameters and the collection diameter are experimentally analyzed using the design of experiments (DOE) techniques. Toward this end, polylactic acid (PLA) is melt-electrospun at different treatment levels of the processing parameters in a controlled environment. Two regression-based models—one for predicting the collection diameter and the other for the fiber diameter—are derived from the DOE data for benchmarking and quantitative evaluation of the predictive performance of the theoretical model. The theoretical model is run based on the same treatment levels as the experiment. The elastic parameter values used in the theoretical simulation are extracted from rheological tests. Comparison between the simulated and the observed fiber characteristics revealed that the collector diameter predictions by the theoretical model exhibited approximately a 16.7% difference compared to 24.2% for the average fiber diameter. Finally, a discussion is presented on the challenges and potential factors contributing to the observed differences. Overall, given the identified challenges and gaps in material characterization, the results of the theoretical predictive model are encouraging.