AIMC Topic: Metals
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Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of mycotoxins in corn sample.
Food chemistry
Nov 1, 2024
Cereals, grains, and feedstuffs are prone to contamination by fungi during various stages from growth to storage. These fungi may produce harmful mycotoxins impacting food quality and safety. Thus, the development of quick and reliable methods for on...
Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.
Journal of environmental management
Sep 20, 2024
Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative approach of synergistically employing biochar and bacterial inoculum of Serratia marcescens to address to...
Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.
European journal of radiology
Sep 7, 2024
BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal art...
Refining hydrogel-based sorbent design for efficient toxic metal removal using machine learning-Bayesian optimization.
Journal of hazardous materials
Aug 30, 2024
Hydrogel-based sorbents show promise in the removal of toxic metals from water. However, optimizing their performance through conventional trial-and-error methods is both costly and challenging due to the inherent high-dimensional parameter space ass...
The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.
Biological trace element research
Jul 29, 2024
HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile, metal exposure may elevate the risk of HPV infection and amplify its carcinogenic potential, but st...
Potential prediction and coupling relationship revealing for recovery of platinum group metals from spent auto-exhaust catalysts based on machine learning.
Journal of environmental management
Jun 24, 2024
As hazardous waste, the massive generation of spent auto-exhaust catalysts (SACs) puts enormous pressure on environmental management, but provides a rare opportunity for platinum group metals (PGMs) recycling. In this study, machine learning (ML) met...
Bioinspired Liquid Metal Based Soft Humanoid Robots.
Advanced materials (Deerfield Beach, Fla.)
Jun 7, 2024
The pursuit of constructing humanoid robots to replicate the anatomical structures and capabilities of human beings has been a long-standing significant undertaking and especially garnered tremendous attention in recent years. However, despite the pr...
Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives.
Bioresource technology
May 18, 2024
Developing an optimized and targeted design approach for metal-modified biochar based on water quality conditions and management is achievable through machine learning. This study leveraged machine learning to analyze experimental data on phosphate a...
Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.
Chemosphere
May 6, 2024
The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery...
Development of new materials for electrothermal metals using data driven and machine learning.
PloS one
Apr 26, 2024
After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this rese...
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