Corrosion Resistance of Sulfur-Selenium Alloy Coatings

Super resolution-based methodology for self-supervised segmentation of microscopy images

Data-driven Artificial Intelligence (AI)/Machine learning (ML) image analysis approaches have gained a lot of momentum in analyzing microscopy images in bioengineering, biotechnology, and medicine. The success of these approaches crucially relies on the availability of high-quality microscopy images, which is often a challenge due to the diverse experimental conditions and modes under which these images are obtained. In this study, we propose the use of recent ML-based image super-resolution (SR) techniques for improving the image quality of microscopy images, incorporating them into multiple ML-based image analysis tasks, and describing a comprehensive study, investigating the impact of SR techniques on the segmentation of microscopy images. The impacts of four Generative Adversarial Network (GAN)- and transformer-based SR techniques on microscopy image quality are measured using three well-established quality metrics. These SR techniques are incorporated into multiple deep network pipelines using supervised, contrastive, and non-contrastive self-supervised methods to semantically segment microscopy images from multiple datasets. Our results show that the image quality of microscopy images has a direct influence on the ML model performance and that both supervised and self-supervised network pipelines using SR images perform better by 2%-6% in comparison to baselines, not using SR. Based on our experiments, we also establish that the image quality improvement threshold range [20-64] for the complemented Perception-based Image Quality Evaluator(PIQE) metric can be used as a pre-condition by domain experts to incorporate SR techniques to significantly improve segmentation performance. A plug-and-play software platform developed to integrate SR techniques with various deep networks using supervised and self-supervised learning methods is also presented.

High Mechanical Strength Alloy-like Minerals Prepared by Inorganic Ionic Co-cross-linking

Alloys often combine different metals to generate superior mechanical properties. However, it is challenging to prepare high mechanical strength minerals with similar strategies. Using calcium carbonate (CaC) and calcium phosphate (CaP) as examples, this work synthesizes a group of compounds with the chemical formulas Ca(CO3 )x (PO4 )2(1- x )/3 (0 < x < 1, CaCPs) by cross-linking ionic oligomers. Unlike mixtures, these CaCPs exhibit a single temperature for the phase transition from amorphous to crystallized CaC (calcite) and CaP (hydroxyapatite). By heat-induced synchronous crystallization, dual-phase CaC/CaP with continuous crystallized boundaries are resembled to alloy-like minerals (ALMs). The mechanical properties of the ALMs are adjusted by tailoring their chemical compositions to reach a hardness of 5.6 GPa, which exceed those of control calcite and hydroxyapatite samples by 430% and 260%, respectively. This strategy expands the chemical scope of inorganic materials and holds promise for preparing high-performance minerals.

Eco-friendly Silane-Based Coating for Mitigation of Carbon Steel Corrosion in Marine Environments

Losses from corrosion contribute roughly 3-5% of the gross domestic product of developed nations, and among the many methods used to avoid corrosion, using silane-based coatings is seen to be of the biggest importance due to their low toxicity and superior adhesive qualities. It is essential to develop an anti-corrosion coating that is efficient, economical, and eco-friendly. The corrosion resistance and durability of various silane-based coatings such as 1,2-bis(triethoxysilyl)ethane (BTSE), bis[3-(triethoxysilyl)propyl]tetrasulfide (TESPT), and vinyltrimethylsilane (VTES) for carbon steel 1018 substrates were investigated in a high-salinity environment (4.5 wt % NaCl). The corrosion resistance performance was evaluated via potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) techniques. Results revealed that the TESPT film (pH ≈ 7) has the best corrosion resistance performance on the carbon steel surface in the aggressive chloride environment, that is, 99.6%. The high corrosion resistance of the TESPT film is due to the hydrophobic nature of this silane, which leads to the formation of a stable and dense film. These results were supported by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) analyses.

Recent Progress in Cyclic Aryliodonium Chemistry: Syntheses and Applications

Hypervalent aryliodoumiums are intensively investigated as arylating agents. They are excellent surrogates to aryl halides, and moreover they exhibit better reactivity, which allows the corresponding arylation reactions to be performed under mild conditions. In the past decades, acyclic aryliodoniums are widely explored as arylation agents. However, the unmet need for acyclic aryliodoniums is the improvement of their notoriously low reaction economy because the coproduced aryl iodides during the arylation are often wasted. Cyclic aryliodoniums have their intrinsic advantage in terms of reaction economy, and they have started to receive considerable attention due to their valuable synthetic applications to initiate cascade reactions, which can enable the construction of complex structures, including polycycles with potential pharmaceutical and functional properties. Here, we are summarizing the recent advances made in the research field of cyclic aryliodoniums, including the nascent design of aryliodonium species and their synthetic applications. First, the general preparation of typical diphenyl iodoniums is described, followed by the construction of heterocyclic iodoniums and monoaryl iodoniums. Then, the initiated arylations coupled with subsequent domino reactions are summarized to construct polycycles. Meanwhile, the advances in cyclic aryliodoniums for building biaryls including axial atropisomers are discussed in a systematic manner. Finally, a very recent advance of cyclic aryliodoniums employed as halogen-bonding organocatalysts is described.

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels—cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort.