AIMC Topic: Metals, Heavy

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Prediction of heavy metals adsorption by hydrochars and identification of critical factors using machine learning algorithms.

Bioresource technology
Hydrochar has become a popular product for immobilizing heavy metals in water bodies. However, the relationships between the preparation conditions, hydrochar properties, adsorption conditions, heavy metal types, and the maximum adsorption capacity (...

Micronutrient seed priming: new insights in ameliorating heavy metal stress.

Environmental science and pollution research international
Plants need to survive with changing environmental conditions, be it different accessibility to water or nutrients, or attack by insects or pathogens. Few of these changes, especially heavy metal stress, can become more stressful and needed strong co...

Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar.

Chemosphere
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, m...

Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.

Chemosphere
Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and ...

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

Journal of hazardous materials
The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stabi...

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum.

Journal of hazardous materials
Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessmen...

Automation in arthrosis research.

SLAS technology
Widespread medical studies require the analysis of suitable sample numbers to discover certain effects. Arthrosis treatment with hip and knee joint endoprostheses introduces multiple materials into the human body. Metal-containing implants may releas...

Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The feasibility analysis of fluorescence hyperspectral imaging technology was studied for the detection of lead content in lettuce leaves. Further, Monte Carlo optimized wavelet transform stacked auto-encoders (WT-MC-SAE) was proposed for dimensional...

Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar.

The Science of the total environment
The process of removal of heavy metals is important due to their toxic effects on living organisms and undesirable anthropogenic effects. Conventional methods possess many irreconcilable disadvantages pertaining to cost and efficiency. As a result, t...

Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

Molecules (Basel, Switzerland)
The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradation products such as particulate wear and corrosion debris, metal-protein complexes, free metallic ion...