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Ions

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Predicting differential ion mobility behaviour using machine learning.

The Analyst
Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means of accurately predicti...

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.

Nucleic acids research
Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties ...

Deep learning for nanopore ionic current blockades.

The Journal of chemical physics
DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades. In this work, we train machine learning models on experimental ionic blockade data from DNA nucleot...

Ionic spiderwebs.

Science robotics
Spiders use adhesive, stretchable, and translucent webs to capture their prey. However, sustaining the capturing capability of these webs can be challenging because the webs inevitably invite contamination, thus reducing its adhesion force. To overco...

Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators.

Journal of visualized experiments : JoVE
Ionic electromechanically active capacitive laminates are a type of smart material that move in response to electrical stimulation. Due to the soft, compliant and biomimetic nature of this deformation, actuators made of the laminate have received inc...

Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections.

Analytical chemistry
Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility throu...

Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

ACS combinatorial science
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...

Optimizing the removal of strontium and cesium ions from binary solutions on magnetic nano-zeolite using response surface methodology (RSM) and artificial neural network (ANN).

Environmental research
The feasibility of using magnetic nano-zeolite (MNZ) to remove cesium and strontium from their binary corrosive solutions was investigated by considering the multi-variant/multi-objective nature of the process. RSM (Response Surface Methodology) and ...

Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled Bees Algorithm.

Bulletin of environmental contamination and toxicology
A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as ch...