Corrosion Behavior of Ni/NiCr/NiCrAlSi Composite Coating on Copper for Application as a Heat Exchanger in Sea Water.

This study introduces a novel Ni/NiCr/NiCrAlSi composite coating to enhance the corrosion resistance of copper, particularly for its use in marine heat exchangers. Utilizing characterization techniques such as scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS), the paper investigates the coating's composition, structure, and corrosion resistance in 3.5 wt.% NaCl aqueous solutions. A significant focus is placed on the role of aluminum within the NiCrAlSi layer, examining its influence on the coating's structure and corrosion behavior. The results indicate that the NiCrAlSi layer with an aluminum content of 5.49 at.% exhibits the most improved corrosion resistance, characterized by the highest corrosion potential and a corrosion current density that is more than one order of magnitude lower compared to the Ni/NiCr coating. The effectiveness of this composite coating is attributed to its multilayer structure and the synergistic effect of alloying elements Cr, Al, and Si, which collectively inhibit corrosive medium penetration. These insights present the Ni/NiCr/NiCrAlSi coating as a promising candidate for copper protection in sea water environments, merging enhanced durability with cost-effectiveness.

Decoupled learning for brain image registration.

Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.

Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics.

Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored.

Phase separation drives the self-assembly of mitochondrial nucleoids for transcriptional modulation.

Mitochondria, the only semiautonomous organelles in mammalian cells, possess a circular, double-stranded genome termed mitochondrial DNA (mtDNA). While nuclear genomic DNA compaction, chromatin compartmentalization and transcription are known to be regulated by phase separation, how the mitochondrial nucleoid, a highly compacted spherical suborganelle, is assembled and functions is unknown. Here we assembled mitochondrial nucleoids in vitro and show that mitochondrial transcription factor A (TFAM) undergoes phase separation with mtDNA to drive nucleoid self-assembly. Moreover, nucleoid droplet formation promotes recruitment of the transcription machinery via a special, co-phase separation that concentrates transcription initiation, elongation and termination factors, and retains substrates to facilitate mtDNA transcription. We propose a model of mitochondrial nucleoid self-assembly driven by phase separation, and a pattern of co-phase separation involved in mitochondrial transcriptional regulation, which orchestrates the roles of TFAM in both mitochondrial nucleoid organization and transcription.