Hydrogen sulfide gas detection by Au-decorated ZnO nanotube: a computational study and comparison to experimental observations

Based on the experimental reports, Au-decoration on the ZnO nanostructures dramatically increases the electronic sensitivity to H₂S gas. In the current study, we computationally scrutinized the mechanism of Au-decoration on a ZnO nanotube (ZON) and the influence on its sensing behavior toward H₂S gas. The intrinsic ZON weakly interacted with the H₂S gas with an adsorption energy of ?11.2 kcal/mol. The interaction showed no effect on the HOMO–LUMO gap and conductivity of ZON. The predicted response of intrinsic ZON toward H₂S gas is 6.3, which increases to 78.1 by the Au-decoration at 298 K. The corresponding experimental values are about 5.0 and 80.0, indicating excellent agreement with our findings. We showed that the Au atom catalyzes the reaction 3O₂?+?2H₂S?→?2SO₂?+?2H₂O. Our calculated energy barrier (at 298 K) is about 12.3 kcal/mol for this reaction. The gap and electrical conductance Au-ZON largely changed by this reaction are attributed to the electron donation and back-donation processes. The obtained recovery time is about 1.35 ms for desorption of generated gases from the surface of the Au-ZON sensor.     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Preclinical Evaluation of 99mTc-ZHER2:41071, a Second-Generation Affibody-Based HER2-Visualizing Imaging Probe with a Low Renal Uptake

Radionuclide imaging of HER2 expression in tumours may enable stratification of patients with breast, ovarian, and gastroesophageal cancers for HER2-targeting therapies. A first-generation HER2-binding affibody molecule [^99mTc]Tc-ZHER2:V2 demonstrated favorable imaging properties in preclinical studies. Thereafter, the affibody scaffold has been extensively modified, which increased its melting point, improved storage stability, and increased hydrophilicity of the surface. In this study, a second-generation affibody molecule (designated ZHER2:41071) with a new improved scaffold has been prepared and characterized. HER2-binding, biodistribution, and tumour-targeting properties of [^99mTc]Tc-labelled ZHER2:41071 were investigated. These properties were compared with properties of the first-generation affibody molecules, [^99mTc]Tc-ZHER2:V2 and [^99mTc]Tc-ZHER2:2395. [^99mTc]Tc-ZHER2:41071 bound specifically to HER2 expressing cells with an affinity of 58 ± 2 pM. The renal uptake for [^99mTc]Tc-ZHER2:41071 and [^99mTc]Tc-ZHER2:V2 was 25–30 fold lower when compared with [^99mTc]Tc-ZHER2:2395. The uptake in tumour and kidney for [^99mTc]Tc-ZHER2:41071 and [^99mTc]Tc-ZHER2:V2 in SKOV-3 xenografts was similar. In conclusion, an extensive re-engineering of the scaffold did not compromise imaging properties of the affibody molecule labelled with ^99mTc using a GGGC chelator. The new probe, [^99mTc]Tc-ZHER2:41071 provided the best tumour-to-blood ratio compared to HER2-imaging probes for single photon emission computed tomography (SPECT) described in the literature so far. [^99mTc]Tc-ZHER2:41071 is a promising candidate for further clinical translation studies.     

Inter-cell interference in multi-tier heterogeneous cellular networks: modeling and constraints

Telecommunication Systems - Interference is the main source of capacity limitation in wireless networks. In some medium access technologies in cellular networks, such as OFDMA, the allocation of...     

Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

Modification of narrow‐spectrum peptidomimetic polyurethanes with fatty acid chains confers broad‐spectrum antibacterial activity

Each year, thousands of patients die from antimicrobial‐resistant bacterial infections that fail to respond to conventional antibiotic treatment. Antimicrobial polymers are a promising new method of combating antibiotic‐resistant bacterial infections. We have previously reported the synthesis of a series of narrow‐spectrum peptidomimetic antimicrobial polyurethanes that are effective against Gram‐negative bacteria, such as Escherichia coli; however, these polymers are not effective against Gram‐positive bacteria, such as Staphylococcus aureus. With the aim of understanding the correlation between chemical structure and antibacterial activity, we have subsequently developed three structural variants of these antimicrobial polyurethanes using post‐polymerization modification with decanoic acid and oleic acid. Our results show that such modifications converted the narrow‐spectrum antibacterial activity of these polymers into broad‐spectrum activity against Gram‐positive species such as S. aureus, however, also increasing their toxicity to mammalian cells. Mechanistic studies of bacterial membrane disruption illustrate the differences in antibacterial action between the various polymers. The results demonstrate the challenge of balancing antimicrobial activity and mammalian cell compatibility in the design of antimicrobial polymer compositions. © 2019 Society of Chemical Industry     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.