Effect of Boronizing on Operating Stability of Steel AISI 304L under Erosion Impact of Hard Particles

Metal Science and Heat Treatment - The effect of solid boronizing at 950°C for 2 and 4 h on the phase composition, microstructure, hardness and abrasive wear of steel AISI 304L is studied...     

Effect of the cultivation mode on red pigments production from Monascus ruber

In submerged cultures performed in chemically defined fermentation medium containing glucose and glutamate, the growth and production of water‐soluble red pigments and citrinin by the filamentous fungus Monascus ruber were studied under various carbon/nitrogen (C/N) ratios. The specific production of the red pigments was optimal at a glucose/glutamate ratio of about 10 and then steadily decreased at higher C/N ratio. In contrast, the production of the mycotoxin increased with increased C/N with an optimum in the range of 30–45. In a fed‐batch mode, it was also found that the production of pigments was not favoured in fed‐batch mode by feeding the medium with glucose while keeping the C/N ratio lower than 10. This low production likely resulted from concurrent high accumulation of L‐malic acid that was reported to inhibit this production. In contrast, this mode of cultivation was rather favourable for the production of the mycotoxin.     

Crystallization of progesterone polymorphs using polymer-induced heteronucleation (PIHn) method

Progesterone is a natural hormone steroid used in humans for several treatments and in livestock for artificial insemination, which exhibits two polymorphic forms at ambient conditions: form 1 and form 2. Form 2 is metastable and more soluble than form 1; however, it is not suitable to use as powder raw material because it transforms into form 1 by the effects of grinding. A polymorphic screening of progesterone based on polymer-induced heteronucleation method was performed as an alternative to prepare the metastable form. Polyvinyl alcohol, hydroxypropyl methylcellulose (HPMC), dextran, gelatin, polyisoprene (PI) and acrylonitrile-butadiene (NBR) copolymer were used. Crystals were prepared from 0.5, 10 and 40?mg/mL solutions in acetone at room temperature by solvent evaporation. The samples were characterized by X-ray powder diffraction, differential scanning calorimetry (DSC), scanning electron microcopy and attenuated total reflectance infrared Fourier transform spectroscopy. Form 1 was nucleated from 40?mg/mL solutions on the six polymers and from 10?mg/mL solutions on PI and NBR. The mixture of form 1 and form 2 was obtained from 10?mg/mL solution on HPMC, dextran and gelatin and from 0.5?mg/mL solution crystallizations. Therefore, the polymeric devices, which crystallized the metastable and more soluble polymorph (2) of progesterone, would be a promissory alternative for the pharmaceutical applications.     

On‐the‐Spot Immobilization of Quantum Dots,Graphene Oxide,and Proteins via Hydrophobins

Class I hydrophobin Vmh2, a peculiar surface active and versatile fungal protein, is known to self‐assemble into chemically stable amphiphilic films, to be able to change wettability of surfaces, and to strongly adsorb other proteins. Herein, a fast, highly homogeneous and efficient glass functionalization by spontaneous self‐assembling of Vmh2 at liquid–solid interfaces is achieved (in 2 min). The Vmh2‐coated glass slides are proven to immobilize not only proteins but also nanomaterials such as graphene oxide (GO) and quantum dots (QDs). As models, bovine serum albumin labeled with Alexa 555 fluorophore, anti‐immunoglobulin G antibodies, and cadmium telluride QDs are patterned in a microarray fashion in order to demonstrate functionality, reproducibility, and versatility of the proposed substrate. Additionally, a GO layer is effectively and homogeneously self‐assembled onto the studied functionalized surface. This approach offers a quick and simple alternative to immobilize nanomaterials and proteins, which is appealing for new bioanalytical and nanobioenabled applications.     

A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

The Journal of Supercomputing - Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields...