Optimization of the synthesis and characterizations of chemical bath deposited Cu2S thin films

Semiconducting copper sulphide (Cu₂S) thin films have been deposited on various substrates (SnO₂:F/glass, glass) by the simple and economical chemical bath deposition technique. The depositions were carried out during a deposition time of about 32.5 min in the pH range of 9.4 to 11. The synthesized Cu₂S thin films were characterized using various techniques without any annealing treatment. X-ray diffraction study shows that Cu₂S films exhibit the best crystallinity for pH = 10.2. For this pH value, Auger electron spectroscopy investigations show that Cu₂S thin films grown on an SnO₂/glass substrate exhibit stochiometric composition with [Cu]/[S] concentrations ratio equal to 2.02. Using the Kelvin method, the work function difference (Ф_material– Ф_probe) for the Cu₂S films deposited on SnO₂/glass substrates at the optimum pH value was found to be equal to 145 meV. Hall measurements confirm the p-type electrical conductivity of the obtained films. The electrical resistivity was of the order of 3.85 × 10⁻⁴ Ω-cm. The transmission and reflection coefficients vary in the range of [35–60] % and [5–15] % respectively, in the visible range, and the band gap energy is about 2.37 eV.     

A New Disubstituted Polyacetylene for the Detection of α‐Amino Acids

New imidazole‐functionalized disubstituted polyacetylene was synthesized by utilizing the postfunctional strategy. In addition, its ability to sense copper ions and α‐amino acids by fluorescence quenching has been studied. The quenching of the fluorescence of the imidazole‐functionalized disubstituted polyacetylene was observed at a very low level of Cu²⁺ (7.0 × 10⁻⁷ mol · L⁻¹). The fluorescent intensity decreased rapidly upon the increase of the concentration of the added solution of Cu²⁺. It was expected that the addition of α‐amino acids to the solution of the polyacetylene/Cu²⁺ complex could turn on the fluorescence of the polyacetylene, if α‐amino acids could remove the copper ions from the complex. Glycine, was used for testing: upon the addition of glycine the quenched fluorescence of P1 turned on immediately. The detection limit was as low as 6.0 × 10⁻⁵ mol · L⁻¹.

     

A multivariate study of the performance of an ultrasound-assisted madder dyes extraction and characterization by liquid chromatography-photodiode array detection

An extraction method of madder (Rubia tinctorum) roots dyes is established and optimized to obtain the original chemical composition. A central composite design (CCD) was developed to specify the importance of the three major factors studied (time, temperature and solvent composition) affecting the ultrasound-assisted extraction of this matrix. A preliminary granulometric study of madder roots is realized in the aim to determine the optimal particles size corresponding to the best ultrasound effects. A comparison with the classical extraction method of madder dyes by reflux is described. The identification of the constituents of R. tinctorum is carried out by liquid chromatography coupled with a photodiode array detector (LC-PDA). Anthraquinonic aglycone and heterosidic dyes compounds are characterized by retention time and UV spectrum: alizarin (1,2-dihydroxyanthraquinone), purpurin (1,2,4-trihydroxyanthraquinone), lucidin (1,3-dihydroxy-2-hydroxymethylanthraquinone), rubiadin (1,3-dihydroxy-2-methylanthraquinone), xanthopurpurin (1,3-dihydroxyanthraquinone), pseudopurpurin (1,2,4-trihydroxy-3-carboxyanthraquinone), lucidin primeveroside, ruberythric acid (alizarin primeveroside), galiosin (pseudopurpurin primeveroside) and rubiadin primeveroside. The optimal experimental conditions are 18min, 36 degrees C and 37/63 MeOH/H(2)O (v/v).     

Nanocompatible chemistry toward fabrication of target-specific gold nanoparticles

Nanocompatible chemistry which utilizes a novel nontoxic phosphino amino acid as a reducing agent has resulted in the development of therapeutically useful gold nanoparticles under biologically benign media. Stabilization of gold nanoparticles by the edible gum arabic matrix has provided an effective pathway toward in vivo stable target-specific gold nanoparticles.     

Biomolecular interactions and tools for their recognition: focus on the quartz crystal microbalance and its diverse surface chemistries and applications

Interactions between molecules are ubiquitous and occur in our bodies, the food we eat, the air we breathe, and myriad additional contexts. Although numerous tools are available for the recognition of biomolecular interactions, such tools are often limited in their sensitivity, expensive, and difficult to modify for various uses. In contrast, the quartz crystal microbalance (QCM) has sub-nanogram detection capabilities, is label-free, is inexpensive to create, and can be readily modified with a number of diverse surface chemistries to detect and characterize diverse interactions. To maximize the versatility of the QCM, scientists need to know available methods by which QCM surfaces can be modified. Therefore, in addition to summarizing the various tools currently used for biomolecular recognition, explicating the fundamental principles of the QCM as a tool for biomolecular recognition, and comparing the QCM with other acoustic sensors, we systematically review the numerous types of surface chemistries-including hydrophobic bonds, ionic bonds, hydrogen bonds, self-assembled monolayers, plasma-polymerized films, photochemistry, and sensing ionic liquids-used to functionalize QCMs for various purposes. We also review the QCM's diverse applications, which include the detection of gaseous species, detection of carbohydrates, detection of nucleic acids, detection of non-enzymatic proteins, characterization of enzymatic activity, detection of antigens and antibodies, detection of cells, and detection of drugs. Finally, we discuss the ultimate goals of and potential barriers to the development of future QCMs.     

Dynamic Ink Oil Absorption in a Pigmented Porous Coating Medium Studied by Near-Infrared Diffuse Reflectance Spectroscopy

The rate of absorption (both long and short timescale) of typical heatset offset printing ink oils, namely mineral and linseed oil, has been studied on model ground calcium carbonate coating pigment tablets containing various amounts of either styrene–acrylic or styrene–butadiene binder. The pore structure characteristics of the tablets were determined using mercury intrusion porosimetry. The movement of the oils both on the surface of and within the porous structure of the pigment/binder tablets was studied under the influence of pressure-less capillary flow with subsequent diffusion through the connected void volume of the tablet. The wetting was analyzed by near-infrared (NIR) diffuse reflectance spectroscopy both as a single probe measurement and by hyperspectral imaging. The results showed that the rate of oil filling the structure was strongly dependent on the binder amount in the structure as well as the binder chemistry (oil- or non-absorbing binder), which supports previous findings. The liquid properties, and especially the viscosity of the liquid (oil), influenced the absorption rate. The gradients in absorbance indicated the presence of latex blocking access to some pores and reducing connectivity.     

Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility

This study proposes a threshold realized generalized autoregressive conditional heteroscedastic (GARCH) model that jointly models daily returns and realized volatility, thereby taking into account the bias and asymmetry of realized volatility. We incorporate this threshold realized GARCH model with skew Student‐t innovations as the observation equation, view this model as a sharp transition model, and treat the realized volatility as a proxy for volatility under this nonlinear structure. Through the Bayesian Markov chain Monte Carlo method, the model can jointly estimate the parameters in the return equation, the volatility equation, and the measurement equation. As an illustration, we conduct a simulation study and apply the proposed method to the US and Japan stock markets. Based on quantile forecasting and volatility estimation, we find that the threshold heteroskedastic framework with realized volatility successfully models the asymmetric dynamic structure. We also investigate the predictive ability of volatility by comparing the proposed model with the traditional GARCH model as well as some popular asymmetric GARCH and realized GARCH models. This threshold realized GARCH model with skew Student‐t innovations outperforms the competing risk models in out‐of‐sample volatility and Value‐at‐Risk forecasting.     

Quantile forecasting based on a bivariate hysteretic autoregressive model with GARCH errors and time ‐varying correlations

To understand and predict chronological dependence in the second‐order moments of asset returns, this paper considers a multivariate hysteretic autoregressive (HAR) model with generalized autoregressive conditional heteroskedasticity (GARCH) specification and time‐varying correlations, by providing a new method to describe a nonlinear dynamic structure of the target time series. The hysteresis variable governs the nonlinear dynamics of the proposed model in which the regime switch can be delayed if the hysteresis variable lies in a hysteresis zone. The proposed setup combines three useful model components for modeling economic and financial data: (1) the multivariate HAR model, (2) the multivariate hysteretic volatility models, and (3) a dynamic conditional correlation structure. This research further incorporates an adapted multivariate Student t innovation based on a scale mixture normal presentation in the HAR model to tolerate for dependence and different shaped innovation components. This study carries out bivariate volatilities, Value at Risk, and marginal expected shortfall based on a Bayesian sampling scheme through adaptive Markov chain Monte Carlo (MCMC) methods, thus allowing to statistically estimate all unknown model parameters and forecasts simultaneously. Lastly, the proposed methods herein employ both simulated and real examples that help to jointly measure for industry downside tail risk.