AI Medical Compendium Journal:
Journal of hazardous materials

Showing 81 to 90 of 97 articles

Nanocomposites of boronic acid-functionalized magnetic multi-walled carbon nanotubes with flexible branched polymers as a novel desorption/ionization matrix for the capture and direct detection of cis-diol-flavonoid compounds coupled with MALDI-TOF-MS.

Journal of hazardous materials
Novel boronic acid-functionalized magnetic multi-walled carbon nanotubes with flexible branched polymer (FeO@MWCNTs@ε-PL@BA) nanocomposites were fabricated and applied as the desorption/ionization matrix for the MALDI-TOF-MS determination of low mole...

Predicting crop root concentration factors of organic contaminants with machine learning models.

Journal of hazardous materials
Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to c...

Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern.

Journal of hazardous materials
Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the uncertainty associated with DCNN predictions remains to be quantified. The training process contains m...

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.

Journal of hazardous materials
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome ...

Application of Artificial Neural Network as a nonhazardous alternative on kinetic analysis and modeling for green synthesis of cobalt nanocatalyst from Ocimum tenuiflorum.

Journal of hazardous materials
The present paper is dedicated to analyze non-hazardous kinetic behaviour and modelling of green synthesized cobalt nanocatalyst (CoNCs), using an Artificial Neural Network (ANN). In order to supplement the trace metal in other applications, CoNCs we...

Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics.

Journal of hazardous materials
Due to the recurrent small spills, oil pollution along coastal regions is still a major environmental issue. Standardized oil fingerprinting techniques are useful for oil spill identifications, but time- and resource-consuming. There have been ongoin...

Inter-regional multimedia fate analysis of PAHs and potential risk assessment by integrating deep learning and climate change scenarios.

Journal of hazardous materials
Polycyclic aromatic hydrocarbons (PAHs) are hazardous compounds associated with respiratory disease and lung cancer. Increasing fossil fuel consumption, which causes climate change, has accelerated the emissions of PAHs. However, potential risks by P...

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

Journal of hazardous materials
The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal sourc...

Microfiltration of saline crude oil emulsions: Effects of dispersant and salinity.

Journal of hazardous materials
Dispersants reduce oil-water interfacial tension making the separation of oil-water emulsions challenging. In this study, crude oil stabilized by the dispersant, Corexit EC9500A, was emulsified in synthetic sea water using a range of Corexit/crude oi...

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...