AIMC Topic: Metals, Heavy

Clear Filters Showing 101 to 110 of 129 articles

A bioinspired optoelectronically engineered artificial neurorobotics device with sensorimotor functionalities.

Nature communications
Development of the next generation of bio- and nano-electronics is inseparably connected to the innovative concept of emulation and reproduction of biological sensorimotor systems and artificial neurobotics. Here, we report for the first time princip...

Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach.

Analytical and bioanalytical chemistry
This work reports on further development of an inhibition electrochemical sensor array based on immobilized bacteria for the preliminary detection of a wide range of organic and inorganic pollutants, such as heavy metal salts (HgCl, PbCl, CdCl), pest...

The application of machine learning methods for prediction of metal sorption onto biochars.

Journal of hazardous materials
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regres...

Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Chemosphere
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...

Analysis of the adsorption and retention models for Cd, Cr, Cu, Ni, Pb, and Zn through neural networks: selection of variables and competitive model.

Environmental science and pollution research international
In this study, the neural networks are used to predict and explain the behavior of different edaphological variables in the adsorption and retention of heavy metals, both isolated and competing. A comparison with the results obtained using multiple r...

Method for the determination of Pb, Cd, Zn, Mn and Fe in rice samples using carbon nanotubes and cationic complexes of batophenanthroline.

Food chemistry
Among cereals, rice is the second most cultivated staple crop in the world. It may be contaminated by toxic heavy metals present in water or soil. Therefore, monitoring the presence of heavy metals in rice and its products is a matter of a great impo...

Efficient heavy metal removal from industrial melting effluent using fixed-bed process based on porous hydrogel adsorbents.

Water research
High adsorption capacity, fast adsorption kinetics, good reusability and low cost are highly demanded for adsorbents used in practical adsorption process. In this study, a porous double network Jute/Polyacrylic acid (Jute/PAA) gel was prepared using ...

Association of co-exposure to heavy metals with renal function in a hypertensive population.

Environment international
BACKGROUND: Chronic kidney disease (CKD) is an increasing health problem worldwide. Recent studies have suggested the potential associations between exposure to metals and CKD events, particularly in participants with hypertension. However, relevant ...

A micro-plate colorimetric assay for rapid determination of trace zinc in animal feed, pet food and drinking water by ion masking and statistical partitioning correction.

Food chemistry
A new micro-plate colorimetric assay was developed for rapid determination of zinc in animal feed, pet food and drinking water. Zinc ion was extracted from sample by trichloroacetic acid and then reacted with 2-(5-Bromo-2-pyridylazo)-5-[N-propyl-N-(3...

Artificial neural networks to evaluate organic and inorganic contamination in agricultural soils.

Chemosphere
The assessment of organic and inorganic contaminants in agricultural soils is a difficult challenge due to the large-scale dimensions of the areas under investigation and the great number of samples needed for analysis. On-site screening techniques, ...