Rapid Copolymer Analysis of Unresolved Mass Spectra by Artificial Intelligence.

In this Article, we present a novel data analysis method for the determination of copolymer composition from low-resolution mass spectra, such as those recorded in the linear mode of time-of-flight (TOF) mass analyzers. Our approach significantly extends the accessible molecular weight range, enabling reliable copolymer composition analysis even in the higher mass regions. At low resolution, the overlapping mass peaks in the higher mass range hinder a comprehensive characterization of the copolymers.

Scale up of fully eco-friendly zinc-O(ads) batteries from the lab scale to the prototype level.

Due to their benefits of high specific energy, safety, environmental friendliness and low cost, zinc-air batteries (ZABs) are considered as promising candidates for the next generation of energy storage devices. In this article, we report on the scale-up of our previously developed laboratory cell operating with charcoal cathode and swollen cotton membrane immersed in alkaline electrolyte solution to obtain a multi-cell prototype battery with increased energy density, open-circuit voltage and working discharge current. Furthermore, we designed our battery using adsorbed oxygen (O(ads)), allowing the application of a closed system.

Molecular data storage using direct analysis in real time (DART) ionization mass spectrometry for decoding.

Molecular data storage is becoming a viable alternative to traditional information storage systems. Here, we propose a method where the presence or absence of a given molecule in a mixture of compounds represents a bit of information. As a novel approach, direct analysis in real time (DART) ionization mass spectrometry is used to recover and decode the information stored at the molecular level.

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks.

Gel permeation chromatography (GPC) is a generally applied method for the mass analysis of various polymers and copolymers, but it inherently fails to provide additional important information such as the composition of copolymers. However, we will show that GPC measurements using different solvents can yield not just the correct molecular weight but the composition of the copolymer. Accordingly, artificial neural networks (ANNs) have been developed to process the data of GPC measurements and determine the molecular weight and the chemical composition of the copolymers.

Mass Spectral Filtering by Mass-Remainder Analysis (MARA) at High Resolution and Its Application to Metabolite Profiling of Flavonoids.

Flavonoids represent an important class of secondary metabolites because of their potential health benefits and functions in plants. We propose a novel method for the comprehensive flavonoid filtering and screening based on direct infusion mass spectrometry (DIMS) analysis. The recently invented data mining procedure, the multi-step mass-remainder analysis (M-MARA) technique is applied for the effective mass spectral filtering of the peak rich spectra of natural herb extracts.