Cp*Ir(III) complexes catalyzed solvent-free synthesis of quinolines, pyrroles and pyridines via an ADC strategy.

A trio of Ir(III) complexes that are held together by a picolinamidato moiety were created. In our earlier research, we demonstrated the catalytic activity of the complexes for producing alpha-alkylated ketones from a ketone or secondary alcohol with a primary alcohol in the presence of a catalytic amount of a Cp*Ir(III) catalyst and tBuOK in toluene at 110 °C using the hydrogen-borrowing technique. Earlier many research groups had synthesized quinoline, pyrrole, and pyridine derivatives using 2-amino alcohol and ketone or secondary alcohol derivatives as starting materials, but in all those cases the reaction conditions are not suitable in terms of green synthesis like more catalyst loading, base loading, long reaction time, and high temperature. In addition, most of the reactions contain phosphine a hazardous by-product, along with the catalyst. Keeping in mind these shortcomings, we tried to expand the use of our catalysts after achieving an excellent result in our previous work, and we were successful in producing quinoline, pyrrole, and pyridine derivatives through acceptor-less dehydrogenative coupling (ADC) procedures at 90-110 °C under neat/solvent-free conditions and achieved good to exceptional yields of those nitrogen-containing heterocycles. This methodology is attractive because it is environmentally benign and allows for the "green" synthesis of nitrogen-containing heterocycles. All that is required is a modest quantity of catalyst and base, and the by-products are merely H2O and H2.

Nanometal surface energy transfer (NSET) from biologically active heterocyclic ligands to silver nanoparticles induces enhanced antimicrobial activity against gram-positive bacteria.

Herein we report the formation of a nanometal surface energy transfer (NSET) pair between a donor biologically active heterocyclic luminescent ligand such as 3-(1,3-Dioxoisoindolin-2-yl)-N, N-dimethylpropan-1-ammonium perchlorate (S4PNL; λem-408 nm) and an acceptor silver nanoparticle (Ag NP; λabs-406 nm). When the S4PNL ligand interacts with Ag NPs, the quenching in their luminescence intensity at 408 nm is noticed, with a Stern-Volmer constant of 0.8 × 104 M-1. The present donor-acceptor pair displays a binding constant of 2.8 × 104 M-1 and binding sites of 1.12. The current work shows the energy transfer from a molecular dipole (S4PNL) to a nanometal surface (Ag NP) and thus follows the nanometal surface energy transfer (NSET) ruler with an energy transfer efficiency of 80.0%, 50% energy transfer efficiency distance (d0) of 4.9 nm, donor-acceptor distance of 3.4 nm. The alteration in the zeta potential value of S4PNL upon interaction with AgNP clearly demonstrates the strong electrostatic interaction between donor and acceptor. Importantly, the current NSET pair shows enhanced antimicrobial activity against gram-positive bacteria such as Bacillus cereus (B. cereus) in comparison to their parent components i.e. S4PNL ligand and Ag NP. The NSET pair shows maximum inhibition against B. cereus (9202.21 ± 463.26 CFU/ml.) at 10% while minimum inhibition is observed at 0.01% of it (39,887.19 ± 242.67 CFU/ml.).

Silver-Polymethylhydrosiloxane-Quaternary Ammonium Coating on Anodized Aluminum with Excellent Antibacterial Property.

Multidrug-resistant bacteria are known to survive on high-touch surfaces for days, weeks, and months, contributing to the rise in nosocomial infections. Inducing antibacterial property in such surfaces can presumably reduce the overall microbial burden and subsequent nosocomial infections in hygiene critical environments. In the present study, a one-pot sol-gel process has been deployed to incorporate silver (Ag) and quaternary ammonium salt (QUAT) bactericides in a polymethylhydrosiloxane (PMHS) matrix. The Ag-PMHS-QUAT nanocomposite was coated on anodized aluminum (AAO/Al) by a simple ultrasound-assisted deposition process. The morphological features and chemical composition of the Ag-PMHS-QUAT nanocomposite have been characterized using SEM, XRD spectroscopy, and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) to confirm the formation of Ag-QUAT nanocomposites within the polymeric network of PMHS. The Ag-PMHS-QUAT nanocomposite coating on anodized aluminum oxide (AAO/Al) coupon exhibited superior antibacterial property with a 6-log bacterial reduction compared to the 5-log reduction for the commercially available antimicrobial copper coupon.

Green Synthesis of Lead Sulphide Nanoparticles for High-Efficiency Perovskite Solar Cell Applications.

In this study, lead sulfide (PbS) nanoparticles were synthesized by the chemical precipitation method using Aloe Vera extract with PbCl2 and Thiourea (H2N-CS-NH2). The synthesized nanoparticles have been investigated using x-ray diffraction (XRD), UV-Vis, energy-dispersive x-ray spectroscopy (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). XRD and TEM results confirm that the films are in the cubic phase. The crystallite size, lattice constant, micro-strain, dislocation density, optical bandgap, etc. have been determined using XRD and UV-Vis for investigating the quality of prepared nanoparticles. The possible application of these synthesized nanoparticles in the solar cells was investigated by fabricating the thin films on an FTO-coated and bare glass substrate. The properties of nanoparticles were found to be nearly retained in the film state as well. The experimentally found properties of thin films have been implemented for perovskite solar cell simulation and current-voltage and capacitance-voltage characteristics have been investigated. The simulation results showed that PbS nanoparticles could be a potential hole transport layer for high-efficiency perovskite solar cell applications.

Anodized Aluminum Surface with Topography-Mediated Antibacterial Properties.

Topography-mediated antibacterial surfaces that inactivate bacteria by physical contact have gained attention in recent years. Contrary to conventional antibacterial coatings, topography-mediated antibacterial surfaces do not suffer from coating instability and possible toxicity problems. In this study, a one-step hard anodization process has been deployed to fabricate a topography-mediated antibacterial aluminum surface. By optimizing anodization parameters, such as the concentration of the electrolyte, current density, and anodization time, desirable features of micronanoscale morphology were achieved. The optimum conditions of anodized aluminum that provided pores of a diameter of 151 ± 37 nm effectively killed 100% of E. coli bacteria.

Efficient Computation of Conditionals in the Dempster-Shafer Belief Theoretic Framework.

The Dempster-Shafer (DS) belief theory constitutes a powerful framework for modeling and reasoning with a wide variety of uncertainties due to its greater expressiveness and flexibility. As in the Bayesian probability theory, the DS theoretic (DST) conditional plays a pivotal role in DST strategies for evidence updating and fusion. However, a major limitation in employing the DST framework in practical implementations is the absence of an efficient and feasible computational framework to overcome the prohibitive computational burden DST operations entail. The work in this article addresses the pressing need for efficient DST conditional computation via the novel computational model DS-Conditional-All. It requires significantly less time and space complexity for computing the Dempster's conditional and the Fagin-Halpern conditional, the two most widely utilized DST conditional strategies. It also provides deeper insight into the DST conditional itself, and thus acts as a valuable tool for visualizing and analyzing the conditional computation. We provide a thorough analysis and experimental validation of the utility, efficiency, and implementation of the proposed data structure and algorithms. A new computational library, which we refer to as DS-Conditional-One and DS-Conditional-All (DS-COCA), is developed and harnessed in the simulations.

Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.

Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML's feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.

Development and Evaluation of Machine Learning Models for Recovery Prediction after Treatment for Traumatic Brain Injury.

Traumatic brain injury (TBI) is a leading cause of death and disability yet treatment strategies remain elusive. Advances in machine learning present exciting opportunities for developing personalized medicine and informing laboratory research. However, their feasibility has yet to be widely assessed in animal research where data are typically limited or in the TBI field where each patient presents with a unique injury. The Operation Brain Trauma Therapy (OBTT) has amassed an animal dataset that spans multiple types of injury, treatment strategies, behavioral assessments, histological measures, and biomarker screenings. This paper aims to analyze these data using supervised learning techniques for the first time by partitioning the dataset into acute input metrics (i.e. 7 days post-injury) and a defined recovery outcome (i.e. memory retention). Preprocessing is then applied to transform the raw OBTT dataset, e.g. developing a class attribute by histogram binning, eliminating borderline cases, and applying principal component analysis (PCA). We find that these steps are also useful in establishing a treatment ranking; Minocycline, a therapy with no significant findings in the OBTT analyses, yields the highest percentage recovery in our ranking. Furthermore, of the seven classifiers we have evaluated, Naïve Bayes achieves the best performance (67%) and yields significant improvement over our baseline model on the preprocessed dataset with borderline elimination. We also investigate the effect of testing on individual treatment groups to evaluate which groups are difficult to classify, and note the interpretive qualities of our model that can be clinically relevant.Clinical Relevance- These studies establish methods for better analyzing multivariate functional recovery and understanding which measures affect prognosis following traumatic brain injury.

Silver-Polymethylhydrosiloxane Nanocomposite Coating on Anodized Aluminum with Superhydrophobic and Antibacterial Properties.

Biofilm formation on both animate and inanimate surfaces serves as an ideal bacterial reservoir for the spread of nosocomial infections. Designing surfaces with both superhydrophobic and antibacterial properties can help reduce initial bacterial attachment and subsequent biofilm formation. In the present study, a two-step approach is deployed to fabricate silver-polymethylhydrosiloxane (Ag-PMHS) nanocomposites, followed by a simple dip-coating deposition on anodized Al. Ag-nanoparticles (Ag-NPs) are synthesized in situ within a PMHS polymeric matrix. Morphological features of Ag-PMHS coating observed by scanning electron microscopy shows heterogeneous micro-nano-structures. The chemical compositions of these coatings were characterized using X-ray diffraction and attenuated total reflection-Fourier transform infrared spectroscopy, which indicate the presence of a low-energy PMHS polymer. The as-synthesized Ag-PMHS nanocomposite demonstrated excellent antibacterial properties against clinically relevant planktonic bacteria with zone of inhibition values of 25.3 ± 0.5, 24.8 ± 0.5, and 23.3 ± 3.6 mm for Pseudomonas aeruginosa (P.A) (Gram -ve), Escherichia coli (E. coli) (Gram -ve), and Staphylococcus aureus (S.A) (Gram +ve), respectively. The Ag-PMHS nanocomposite coating on anodized Al provides an anti-biofouling property with an adhesion reduction of 99.0, 99.5, and 99.3% for Pseudomomas aeruginosa (P.A), E. coli, and S. aureus (S.A), respectively. Interestingly, the coating maintained a stable contact angle of 158° after 90 days of immersion in saline water (3.5 wt % NaCl, pH 7.4). The Ag-PMHS nanocomposite coating on anodized Al described herein demonstrates excellent antibacterial and anti-biofouling properties owing to its inherent superhydrophobic property.

Ultraviolet-Durable Superhydrophobic Nanocomposite Thin Films Based on Cobalt Stearate-Coated TiO2 Nanoparticles Combined with Polymethylhydrosiloxane.

Ultraviolet (UV)-durable superhydrophobic nanocomposite thin films have been successfully fabricated on aluminum substrates by embedding cobalt stearate (CoSA)-coated TiO2 nanoparticles in a hydrophobic polymethylhydrosiloxane (PMHS) matrix (PMHS/TiO2@CoSA) using the sol-gel process. When compared to the sharp decrease of water contact angle (WCA) on the superhydrophobic PMHS/TiO2 thin films, the PMHS/TiO2@CoSA superhydrophobic thin films exhibited a nearly constant WCA of 160° under continuous UV irradiation for more than 1 month. The designed scheme of the TiO2@CoSA core-shell structure not only increased the hydrophobic properties of the TiO2 nanoparticle surface but also confined the photocatalytic efficiency of TiO2 nanoparticles. A plausible model has been suggested to explain the UV-durable mechanism of the superhydrophobic nanocomposite thin films based on PMHS/TiO2@CoSA. Furthermore, the elongated lifetime in the exposure of the solar light imparts this superhydrophobic nanocomposite thin film with potential practical applications where UV-resistant properties are emphasized including corrosion-resistant building walls, anti-icing airplanes, self-cleaning vehicles, and so forth.