Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Due to large specific surface area, abundant functional groups and low cost, biochar is
widely used for pollutant removal. The adsorption performance of biochar is related to …

Nanomedicine design is often a trial-and-error process, and the optimization of formulations
and in vivo properties requires tremendous benchwork. To expedite the nanomedicine …

Nanobio interaction studies have generated a significant amount of data. An important next
step is to organize the data and design computational techniques to analyze the nanobio …

Colloidal material design necessitates a collection of computer approaches ranging from
quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) …

The synthesis of nanoparticles is affected by many reaction conditions, and their properties
are usually determined by factors such as their size, shape and surface chemistry. In order …

Organic chemistry has seen colossal progress due to machine learning (ML). However, the
translation of artificial intelligence (AI) into materials science is challenging, where biological …

Abstract Machine learning has made significant progress in assessing the risk associated
with hazardous chemicals. However, most models were constructed by randomly selecting …

Gold nanoparticles are versatile materials for biological applications because their
properties can be modulated by assembling ligands on their surface to form monolayers …

Machine learning has been an emerging scientific field serving the modern multidisciplinary
needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of …