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Microbial Corrosion01:24

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Microbiologically Influenced Corrosion (MIC) is a significant form of material degradation caused by the metabolic activities of microorganisms. This phenomenon poses substantial challenges across various industries, including oil and gas, maritime, and water treatment sectors.MIC occurs when microorganisms, such as bacteria, archaea, and fungi, colonize metal surfaces, forming biofilms that alter the local electrochemical environment. These biofilms can lead to the production of corrosive...

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Testing of Nanoparticle Release from a Composite Containing Nanomaterial Using a Chamber System
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Published on: November 22, 2016

NEIMiner: nanomaterial environmental impact data miner.

Kaizhi Tang1, Xiong Liu, Stacey L Harper

  • 1Intelligent Automation, Inc., Rockville, MD, USA.

International Journal of Nanomedicine
|October 8, 2013
PubMed
Summary

A new system, NEIMiner, helps researchers and policymakers understand the environmental impact of engineered nanomaterials (eNM). It integrates data to predict nanomaterial fate, uptake, and toxicity, supporting informed risk management.

Keywords:
content management systemdata integrationdata managementdata miningmodel compositionmodelingnanomaterial environmental impact

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Area of Science:

  • Environmental Science
  • Materials Science
  • Computational Biology

Background:

  • Engineered nanomaterials (eNM) offer diverse applications but pose potential environmental risks.
  • Assessing eNM environmental impact requires understanding their fate, bioavailability, and toxicity.
  • Informed risk management for eNM necessitates robust scientific data and predictive models.

Purpose of the Study:

  • To develop a data mining system, NEIMiner, for studying nanomaterial environmental impact (NEI).
  • To create a comprehensive platform for aggregating and analyzing large-scale, distributed NEI data.
  • To support evidence-based decision-making for the responsible development and application of eNM.

Main Methods:

  • Developed a model-driven, data mining system (NEIMiner) with four integrated components.
  • Implemented an NEI modeling framework for layered predictability.
  • Utilized automatic web services and web scraping for data integration.
  • Extended Drupal CMS for data management and access of NEI-related bibliography and characterization data.
  • Incorporated advanced analysis capabilities in the model building layer.

Main Results:

  • Successfully developed and integrated four core components of the NEIMiner system.
  • Demonstrated the system's capability to aggregate and analyze large-scale, distributed NEI data.
  • Established a functional prototype of NEIMiner, accessible online.

Conclusions:

  • NEIMiner provides significant value in assessing nanomaterial environmental impact.
  • The system facilitates a more comprehensive understanding of eNM environmental fate and effects.
  • NEIMiner supports informed risk management strategies for engineered nanomaterials.