Aqueous‐Solution Route to Zinc Telluride Films for Application to CO2 Reduction

As a photocathode for CO₂ reduction, zinc‐blende zinc telluride (ZnTe) was directly formed on a Zn/ZnO nanowire substrate by a simple dissolution–recrystallization mechanism without any surfactant. With the most negative conduction‐band edge among p‐type semiconductors, this new photocatalyst showed efficient and stable CO formation in photoelectrochemical CO₂ reduction at ?0.2–?0.7 V versus RHE without a sacrificial reagent.     

An Aluminophosphate Molecular Sieve with 36 Crystallographically Distinct Tetrahedral Sites

The structure of the new medium‐pore aluminophosphate molecular sieve PST‐6 is determined by the combined use of rotation electron diffraction tomography, synchrotron X‐ray powder diffraction, and computer modeling. PST‐6 was prepared by calcination of another new aluminophosphate material with an unknown structure synthesized using diethylamine as a structure‐directing agent, which is thought to contain bridging hydroxy groups. PST‐6 has 36 crystallographically distinct tetrahedral sites in the asymmetric unit and is thus crystallographically the most complex zeolitic structure ever solved.     

PET Imaging of Bacterial Infections with Fluorine‐18‐Labeled Maltohexaose

A positron emission tomography (PET) tracer composed of ¹⁸F‐labeled maltohexaose (MH¹⁸F) can image bacteria in vivo with a sensitivity and specificity that are orders of magnitude higher than those of fluorodeoxyglucose (¹⁸FDG). MH¹⁸F can detect early‐stage infections composed of as few as 10⁵ E. coli colony‐forming units (CFUs), and can identify drug resistance in bacteria in vivo. MH¹⁸F has the potential to improve the diagnosis of bacterial infections given its unique combination of high specificity and sensitivity for bacteria.     

Surface-Plasmonic-Field-Induced Photoredox Catalysis and Mediated Electron Transfer for Washing-Free DNA Detection

Distance-dependent electromagnetic radiation and electron transfer have been commonly employed in washing-free fluorescence and electrochemical bioassays, respectively. In this study, we combined the two distance-dependent phenomena for sensitive washing-free DNA detection. A distance-dependent surface plasmonic field induces rapid photoredox catalysis of surface-bound catalytic labels, and distance-dependent mediated electron transfer allows for rapid electron transfer from the surface-bound labels to the electrode. An optimal system consists of a chemically reversible acceptor (Ru(NH₃)₆³⁺), a chemically reversible photoredox catalyst (eosin Y), and a chemically irreversible donor (triethanolamine). Side reactions with O₂ do not significantly decrease the efficiency of photoredox catalysis. Energy transfer quenching between the electrode and the label can be lowered by increasing the distance between them. Washing-free DNA detection had a detection limit of approximately 0.3 nm in buffer and 0.4 nm in serum without a washing step.     

Stress variations during polarization of iron thin film electrode in pH 8.4 borate buffer solution

Journal of Solid State Electrochemistry - The stress variations during polarization of iron thin film electrode in pH 8.4 borate buffer solution have been measured by a cantilever bending...     

Hierarchical Self‐Assembly of Poly‐Pseudorotaxanes into Artificial Microtubules

Hierarchical self‐assembly of building blocks over multiple length scales is ubiquitous in living organisms. Microtubules are one of the principal cellular components formed by hierarchical self‐assembly of nanometer‐sized tubulin heterodimers into protofilaments, which then associate to form micron‐length‐scale, multi‐stranded tubes. This peculiar biological process is now mimicked with a fully synthetic molecule, which forms a 1:1 host‐guest complex with cucurbit[7]uril as a globular building block, and then polymerizes into linear poly‐pseudorotaxanes that associate laterally with each other in a self‐shape‐complementary manner to form a tubular structure with a length over tens of micrometers. Molecular dynamic simulations suggest that the tubular assembly consists of eight poly‐pseudorotaxanes that wind together to form a 4.5 nm wide multi‐stranded tubule.     

γ‐Glutamylcysteine‐ and Cysteinylglycine‐Directed Growth of Chiral Gold Nanoparticles and their Crystallographic Analysis

Chiral optical metamaterials with delicate structures are in high demand in various fields because of their strong light–matter interactions. Recently, a scalable strategy for the synthesis of chiral plasmonic nanoparticles (NPs) using amino acids and peptides has been reported. Reported herein, 3D chiral gold NPs were synthesized using dipeptide γ‐Glu‐Cys and Cys‐Gly and analyzed crystallographically. The γ‐Glu‐Cys‐directed NPs present a cube‐like outline with a protruding chiral wing. In comparison, the NPs synthesized with Cys‐Gly exhibited a rhombic dodecahedron‐like outline with curved edges and elliptical cavities on each face. Morphology analysis of intermediates indicated that γ‐Glu‐Cys generated an intermediate concave hexoctahedron morphology, while Cys‐Gly formed a concave rhombic dodecahedron. NPs synthesized with Cys‐Gly are named 432 helicoid V because of their unique morphology and growth pathway.     

Influence of the dispersed phase upon the behaviour of nylon/rubber blends; crosslinking effect

Nylon 6 and crosslinked SBS rubber blends were prepared. Their impact strength tests demonstrated the possibility of the existence of a maximum in impact strength at a moderate degree of crosslinking level. However, its peak value was not so much eminent as that happened in tensile test or brittle polymer's impact test. This seems due to complex mechanism occurred in dispersed phase. Based on our experimental data, we believe cavitation is important in nylon toughening. Interpretation of nylon toughening mechanism by cavitation and particle deformation is suggested for the reason of low peak value. Even though delamination is also possible, tensile strength data support cavitation in the rubber particle which seems to be more plausible for toughened nylon 6.     

Analysis of phantom centering positioning on image noise and radiation dose in axial scan type of brain CT

ABSTRACT

This study examined the dose and image quality according to the position change of a human phantom in a CT scan. This study used an MDCT 128 Slice CT Scanner instrument. An axial scan was performed with a 16 cm CTDI phantom of a human phantom, and the dose was measured using a pencil chamber meter. The phantom was scanned 10 cm above and below the isocenter and 15 cm above the right and left. The position of the phantom is indicated by C-0 in the isocenter position, S-10 in the upper 10 cm, I-10 in the lower 10 cm, R-15 in the right 15 cm, and L-15 in the left 15 cm. The test was performed 30 times using the brain CT protocol to calculate the dose and the dose width product (DWP). The acquired images were analyzed using the ImageJ program. Statistical analysis was performed using SPSS with one-way ANOVA (p < .05). The mean DWP values of the CT scanner were C-0 31.97 mGy·cm, S-10 24.52 mGy·cm, I-10 24.28 mGy· cm, R-15 17.95 mGy·cm, and L15 17.6 mGy·cm. Compared to the isocenter (C-0), the DLP values measured at each site were 23.3% for S-10, 24% for I-10, 43.8% for R-15, and 44.9% for L15. A significant difference in the one-way ANOVA statistical process was observed (p>0.05). C-0 was measured to be 7.42 HU, S-10 7.87 HU, I-10 8.4 HU, R-15 117 HU, and L-15 13.6 HU for evaluating the image quality. Compared to C-0, S-10 was 5.39%, I-10 was 13.2%, R-15 was 57.6%, and L-15 was 83.2%. The PSNR for S-10, I-10, R-15, and L-15 was 17.37, 17.5, 16.62, and 16.37 dB, respectively. A good quality image can be obtained by positioning the subject precisely in the isocenter in the axial scan, if possible, because the irradiated dose to the subject is low, which can lead to an increase in noise in image reconstruction.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.