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Ions

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Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS.

Analytica chimica acta
Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibil...

Rapid detection of ionic contents in water through sensor fusion and convolutional neural network.

Chemosphere
Salt contents in soil or groundwater are one of the primary indicators to evaluate contamination levels. Electrical conductivity (EC) or salinity information from the conventional laboratory analysis is typically inefficient in delineating contaminat...

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions.

Journal of chemical theory and computation
Transferable high dimensional neural network potentials (HDNNPs) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previ...

MFPS_CNN: Multi-filter Pattern Scanning from Position-specific Scoring Matrix with Convolutional Neural Network for Efficient Prediction of Ion Transporters.

Molecular informatics
In cellular transportation mechanisms, the movement of ions across the cell membrane and its proper control are important for cells, especially for life processes. Ion transporters/pumps and ion channel proteins work as border guards controlling the ...

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning.

Sensors (Basel, Switzerland)
With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predi...

Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

Analytical chemistry
Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The trainin...

Rapid prototyping of ion-selective electrodes using a low-cost 3D printed internet-of-things (IoT) controlled robot.

Talanta
We report automated fabrication of solid-contact sodium-selective (Na-ISEs) and potassium-selective electrodes (K-ISEs) using a 3D printed liquid handling robot controlled with Internet of Things (IoT) technology. The printing system is affordable an...

Portable Deep Learning-Driven Ion-Sensitive Field-Effect Transistor Scheme for Measurement of Carbaryl Pesticide.

Sensors (Basel, Switzerland)
This research proposes a multiple-input deep learning-driven ion-sensitive field-effect transistor (ISFET) scheme to predict the concentrations of carbaryl pesticide. In the study, the carbaryl concentrations are varied between 1 × 10-1 × 10 M, and t...

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Journal of the American Society for Mass Spectrometry
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a ra...

Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning.

Photosynthesis research
Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states...