Search

In the rapidly evolving field of artificial intelligence, the ability to harness and integrate knowledge across various domains stands as a paramount challenge and opportunity. This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents, each specialized in distinct knowledge domains. These AI agents, designed to function as domain-specific experts, collaborate in a unified framework to synthesize and provide comprehensive insights that transcend the limitations of single-domain expertise. By facilitating seamless interaction among these agents, our platform aims to leverage the unique strengths and perspectives of each, thereby enhancing the process of knowledge discovery and decision-making. We present a comparative analysis of the different multi-agent workflow scenarios evaluating their performance in terms of efficiency, accuracy, and the breadth of knowledge integration. Through a series of experiments involving complex, interdisciplinary queries, our findings demonstrate the superior capability of domain specific multi-AI agent system in identifying and bridging knowledge gaps. This research not only underscores the significance of collaborative AI in driving innovation but also sets the stage for future advancements in AI-driven, cross-disciplinary research and application. Our methods were evaluated on a small pilot data and it showed a trend we expected, if we increase the amount of data we custom train the agents, the trend is expected to be more smooth.

Microbiologically influenced corrosion (MIC) compromises the integrity of many technologically relevant metals. Protective coatings based on synthetic materials pose potential environmental impacts. Here, we report a MIC resistant coating based on a biofilm matrix of Citrobacter sp. strain MIC21 on underlying copper (Cu) surfaces. Three identical corrosion cells varying in the type of working electrode (annealed Cu, 29.5% coldworked, and 56.2% coldworked Cu) were used. Graphite plate and Ag/AgCl served as counter and reference electrodes, respectively. The working electrolyte was based on lactate-C media along with an inocula consisting of Oleidesulfovibrio alaskensis strain G20 and Citrobacter sp. strain MIC21. Passivating effect of the co-cultured biofilm matrix was observed in the form of an ennoblement effect. Tests based on sequencing, microscopy, and spectroscopy revealed the formation of a compact biofilm matrix dominated by strain MIC21 cells, exopolymers, and insoluble precipitates. This matrix displayed elastic modulus (a measure of rigidity) as high as 0.8 Gpa and increased corrosion resistance by ~10-fold. Interestingly, strain MIC21 has the capacity to inhibit the undesirable growth of aggressive strain G20. Additional corrosion tests also substantiated the passivation effects of strain MIC21. We provide mechanistic insight into the underlying reasons responsible for corrosion prevention behavior of the biofilm matrix., Comment: 46 pages, 5 figures in main manuscript. 9 figures and 5 tables in supplementary information

Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively., Comment: Submitted to Pattern Recognition

The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts., Comment: 12 pages, 8 figures

Despite decades of research, metallic corrosion remains a long-standing challenge in many engineering applications. Specifically, designing a material that can resist corrosion both in abiotic as well as biotic environments remains elusive. Here we design a lightweight sulfur-selenium (S-Se) alloy with high stiffness and ductility that can serve as a universal corrosion-resistant coating with protection efficiency of ~99.9% for steel in a wide range of diverse environments. S-Se coated mild steel shows a corrosion rate that is 6-7 orders of magnitude lower than bare metal in abiotic (simulated seawater and sodium sulfate solution) and biotic (sulfate-reducing bacterial medium) environments. The coating is strongly adhesive and mechanically robust. We attribute the high corrosion resistance of the alloy in diverse environments to its semi-crystalline, non-porous, anti-microbial, and viscoelastic nature with superior mechanical performance, enabling it to successfully block a variety of diffusing species., Comment: 22 pages, 4 figures