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To mitigate the global economic loss exceeding $1 trillion annually from corrosion, this study develops a multifunctional anti-corrosion composite coating by incorporating gallic acid-modified montmorillonite (D-MMT) into an acrylic latex (AL) matrix. The D-MMT, synthesized via ion-exchange, exhibited a significant expansion in interlayer spacing (from 1.270 nm to 1.680 nm) and enhanced dispersion within the polymer.At an optimal 4 wt% loading, the coating exhibited superior mechanical properties-increased hardness (from grade B to HB) and wear resistance-along with improved thermal stability (decomposition temperature elevated from 360 degrees C to 380 degrees C), attributed to hydrogen bonding between resin and nanofillers. Electrochemical analysis demonstrated exceptional corrosion protection, with low-frequency impedance modulus reaching 108 Omega & sdot;cm2 (two orders higher than pure AL) and corrosion current density reduced from 2.307 x 10-7to 2.343 x 10-9 A/ cm2 after 35-day immersion in 3.5 wt% NaCl. XPS and FESEM-EDS analyses revealed a post-damage interface passivation protection mechanism via gallic acid-mediated chelation-reduction, forming a dense passivation layer that effectively blocked active corrosion sites, evidenced by enriched Fe2+ ions(58.3 %) and reduced iron content (29.9 % vs. 74.81 % in AL) at damaged regions due to the strong reducing and chelating capabilities of gallic acid. This work provides a scalable strategy for designing coatings with integrated barrier, mechanical, and self-healing functionalities for industrial applications.
Let $\mathcal{B}$ be a nonunital separable simple stable C*-algebra with strict comparison of positive elements and $T(\mathcal{B})$ having finite extreme boundary, and let $\mathcal{A}$ be a simple unital separable nuclear C*-algebra. We prove that the Paschke dual algebra $\mathcal{A}^d_{\mathcal{B}}$ is $K_1$-injective. As a consequence, we obtain interesting $KK$-uniqueness theorems which generalize the Brown-Douglas-Fillmore essential codimension property.
Motivated by the stellar wind ejected from the upper atmosphere (Corona) of a star, we explore a boundary problem of the two-species nonlinear relativistic Vlasov-Poisson systems in the 3D half space in the presence of a constant vertical magnetic field and strong background gravity. We allow species to have different mass and charge (as proton and electron, for example). As the main result, we construct stationary solutions and establish their nonlinear dynamical asymptotic stability in time and space.
Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and expensive. Here, we introduce a novel application of hyperdimensional computing (HDC), a recently developed computational paradigm inspired by the way the human brain works in encoding information, for the efficient classification of chemical compounds as either toxic or nontoxic. Our methodology employs Simplified Molecular Input Line Entry System (SMILES) representations of compounds, drawing data from the comprehensive Tox21 dataset. We delineate a pipeline wherein these chemical structures are encoded into high-dimensional binary vectors, which subsequently serve as the foundation for training and classification within the HDC framework. This approach leverages HDC's inherent advantages, including its resilience to noise, parallel processing capabilities, and efficacy in identifying intricate patterns. This work demonstrates the viability of HDC as a computationally lightweight first-pass solution for preliminary toxicity screening. This research significantly contributes to the field of cheminformatics by validating HDC's potential in chemical property prediction, thereby facilitating accelerated identification of hazardous substances and mitigating the reliance on intensive laboratory experimentation.
