Harnessing selectivity in chemical sensing via supramolecular interactions: from functionalization of nanomaterials to device applications
Chemical sensing is a strategic field of science and technology ultimately aiming at
improving the quality of our lives and the sustainability of our Planet. Sensors bear a direct …
improving the quality of our lives and the sustainability of our Planet. Sensors bear a direct …
An assessment of the strategies for the energy-critical elements necessary for the development of sustainable energy sources
There have been several strategies developed to increase the diversified supply of energy
so that it can meet all of the future demands for energy. As a result, to ensure a healthy and …
so that it can meet all of the future demands for energy. As a result, to ensure a healthy and …
Advancing rare-earth separation by machine learning
Constituting the bulk of rare-earth elements, lanthanides need to be separated to fully
realize their potential as critical materials in many important technologies. The discovery of …
realize their potential as critical materials in many important technologies. The discovery of …
Pairwise difference regression: a machine learning meta-algorithm for improved prediction and uncertainty quantification in chemical search
Machine learning (ML) plays a growing role in the design and discovery of chemicals,
aiming to reduce the need to perform expensive experiments and simulations. ML for such …
aiming to reduce the need to perform expensive experiments and simulations. ML for such …
[HTML][HTML] A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces
Historically, surface complexation model (SCM) constants and distribution coefficients (K d)
have been employed to quantify mineral-based retardation effects controlling the fate of …
have been employed to quantify mineral-based retardation effects controlling the fate of …
Machine learning-based analysis of overall stability constants of metal–ligand complexes
K Kanahashi, M Urushihara, K Yamaguchi - Scientific Reports, 2022 - nature.com
The stability constants of metal (M)-ligand (L) complexes are industrially important because
they affect the quality of the plating film and the efficiency of metal separation. Thus, it is …
they affect the quality of the plating film and the efficiency of metal separation. Thus, it is …
A machine learning framework for urban mining: A case study on recovery of copper from printed circuit boards
Plenty of research articles on developing methods to recover metals from secondary sources
have been published. These methods are optimized for a specific source and have poor …
have been published. These methods are optimized for a specific source and have poor …
Emerging Rare Earth Element Separation Technologies
Rare earth elements are essential for numerous clean energy applications, yet their mining,
separation, and processing pose significant environmental challenges. Traditional …
separation, and processing pose significant environmental challenges. Traditional …
Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity
The new LOGKPREDICT program integrates HostDesigner molecular design software with
the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted …
the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted …
A Machine Learning-Based Study of Li+ and Na+ Metal Complexation with Phosphoryl-Containing Ligands for the Selective Extraction of Li+ from Brine
N Kireeva, VE Baulin, AY Tsivadze - ChemEngineering, 2023 - mdpi.com
The growth of technologies concerned with the high demand in lithium (Li) sources dictates
the need for technological solutions garnering Li supplies to preserve the sustainability of …
the need for technological solutions garnering Li supplies to preserve the sustainability of …