ABSTRACT
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. Nicotinic acid derivatives were synthesized and used as the 'bit compounds'. Their volatility and ease of ionization make these molecules especially suitable for DART-MS detection. The application of DART-MS as a method with an ambient ionization technique, enables the re-reading of digital chemical codes embedded in the material of ordinary objects. Our method is designed to store and read back short pieces of digital information, up to several hundred bits. These codes can have the function of barcodes or QR codes, as shown in our proof-of-principle applications. First, modelling a QR code as a link to our university's website, three solutions were prepared, each representing 22 bits. Proceeding further, the bit compounds were incorporated into a polymer matrix that is suitable for 3D printing, and a toy ship was created with a hidden barcode. In addition, decoding software was developed to process the DART-MS spectra. The nicotinic acid components representing the bits dominated the DART-MS spectra and error-free decoding was achieved.
ABSTRACT
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. The target values of the ANNs were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy. Our GPC-ANN method is demonstrated by the analysis of various poloxamers, i.e., poly(ethylene oxide) (PEO)-poly(propylene oxide) (PPO) block copolymers. Two ANNs were constructed. The first one (ANN_1) works in a wider mass range (from 900 to 12,500 dalton), while the second one (ANN_2) produces more output values. ANN_2 can thus predict seven characteristic copolymer parameters, namely, two average molecular weights, the average weight fraction of the EO unit, and four average numbers of the repeat units. The correlation between the experimentally obtained outputs and the predicted ones is high (r > 0.98). The accuracy of the ANNs is very convincing, and both ANNs predict the number-average molecular weight (Mn) with an accuracy below 5%. Furthermore, this work is the first step for creating an open database and applications extending the use of the GPC-ANN method for the analysis of copolymers.
ABSTRACT
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. In addition, our flavonoid-filtering algorithm facilitates the determination of the elemental composition. M-MARA flavonoid-filtering uses simple mathematical and logical operations and thus, it can easily be implemented in a regular spreadsheet software. A huge benefit of our method is the high speed and the low demand for computing power and memory that enables the real time application even for tandem mass spectrometric analysis. Our novel method was applied for the electrospray ionization (ESI) DIMS spectra of various herb extract, and the filtered mass spectral data were subjected to chemometrics analysis using principal component analysis (PCA).
