Application of matrix-assisted laser desorption/ionization mass spectrometric imaging to monitor surface changes of UV-irradiated poly(styrene) films

An automatic method for accurate signal-to-noise ratio estimation and baseline correction of Raman spectra of environmental microplastics

In this study, we introduced a k-iterative double sliding-window (DSW^k) method for the estimation of spectral noise, signal-to-noise ratio (SNR), and baseline correction. The performance was evaluated using simulated spectra and compared against other commonly employed methods. Convergent evaluation determined that a k value of 20 strikes an optimal balance between convergence and computational intensity. The DSW^k method demonstrated outstanding performance across different spectral types (flat baseline, baseline with elevation, baseline with fluctuation, baseline with elevation and fluctuation) coupled with SNR values from 10 to 1000, achieving results that ranged from 1.01 to 1.08 times of the reference value in estimating spectral noise. It also showed that the estimated SNR values are 0.89 to 0.93 times of the reference value, demonstrating a 74.5 % - 131.7 % improvement over the conventional method in spectra with elevated and/or fluctuating baselines. Additionally, the DSW^k method proved effective in correcting baselines and identifying polymers in environmental samples of polyethylene (PE), polypropylene (PP), and polystyrene (PS), despite the limitation of reducing the peak height in spectra with low SNR. This method offers the potential to enhance the automatic and accurate evaluation of spectral quality and could assist in the development of guidelines for more rapid parameter adjustments in Raman measurements.

Mass spectrometry of polymers: A tutorial review

Ever since the inception of synthetic polymeric materials in the late 19th century, the number of studies on polymers as well as the complexity of their structures have only increased. The development and commercialization of new polymers with properties fine-tuned for specific technological, environmental, consumer, or biomedical applications requires powerful analytical techniques that permit the in-depth characterization of these materials. One such method with the ability to provide chemical composition and structure information with high sensitivity, selectivity, specificity, and speed is mass spectrometry (MS). This tutorial review presents and exemplifies the various MS techniques available for the elucidation of specific structural features in a synthetic polymer, including compositional complexity, primary structure, architecture, topology, and surface properties. Key to every MS analysis is sample conversion to gas-phase ions. This review describes the fundamentals of the most suitable ionization methods for synthetic materials and provides relevant sample preparation protocols. Most importantly, structural characterizations via one-step as well as hyphenated or multidimensional approaches are introduced and demonstrated with specific applications, including surface sensitive and imaging techniques. The aim of this tutorial review is to illustrate the capabilities of MS for the characterization of large, complex polymers and emphasize its potential as a powerful compositional and structural elucidation tool in polymer chemistry.

Application of surface-layer matrix-assisted laser desorption/ionization mass spectrometry imaging to pharmaceutical-loaded poly(ester urea) films

Polymer thin films are often used in transdermal patches as a method of continuous drug administration for patients with chronic illness. Understanding the drug segregation and distribution within these films is important for monitoring proper drug release over time. Surface-layer matrix-assisted laser desorption/ionization mass spectrometry imaging (SL-MALDI-MSI) is a unique analytical technique that provides an optical representation of chemical compositions that exist at the surface of polymeric materials. Solvent-free sublimation is employed for application of matrix to the sample surface, so that only molecules in direct contact with the matrix layer are detected. Here, these methodologies are utilized to visualize variations in drug concentration at both the air and substrate interface in pharmaceutical-loaded polymer films.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

A mass spectrometry imaging method for visualizing synthetic polymers by using average molecular weight and dispersity as indices

RATIONALE

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MSI) is considered to be a powerful tool for visualizing the spatial distribution of synthetic polymers. However, a conventional method extracting an image of a specific m/z value is not suitable for polymers, which have a mass distribution. It is necessary to develop the visualization method to show the spatial distribution of entire polymer series.

METHODS

The mass peaks included in polymer series were specified from the average mass spectrum of the entire MSI measurement region by using Kendrick mass defect analysis. The images of those mass peaks were extracted and the number average molecular weight (M_n ), the weight average molecular weight (M_w ) and dispersity (Đ) were calculated for each pixel. Finally, the spatial distribution of the polymer series was summarized to images using M_n , M_w and Đ as indices.

RESULTS

The effects of the methods were investigated by (i) polymers with different mass distributions and (ii) polymers with different repeat units and end-groups. In both cases, the spatial distribution of specific polymer series including several dozens to hundreds of mass peaks was summarized into three images related to M_n , M_w and Đ, which are familiar indices in polymer analysis. The results are able to provide an overview of the spatial variation of each polymer more intuitively.

CONCLUSIONS

The visualization of M_n , M_w and Đ will help provide an overview of the spatial distribution of polymer series combined with ion intensity distribution made by conventional methods. It can be also applied to other mass spectrometric imaging methods such as desorption electrospray ionization (DESI) or time-of-flight secondary ion mass spectrometry (TOF-SIMS).

Visualizing molecular distributions for biomaterials applications with mass spectrometry imaging: a review

Mass spectrometry imaging (MSI) is a rapidly emerging field that is continually finding applications in new and exciting areas. The ability of MSI to measure the spatial distribution of molecules at or near the surface of complex substrates makes it an ideal candidate for many applications, including those in the sphere of materials chemistry. Continual development and optimization of both ionization sources and analyzer technologies have resulted in a wide array of MSI tools available, both commercially available and custom-built, with each configuration possessing inherent strengths and limitations. Despite the unique potential of MSI over other chemical imaging methods, their potential and application to (bio)materials science remains in our view a largely underexplored avenue. This review will discuss these techniques enabling high parallel molecular detection, focusing on those with reported uses in (bio)materials chemistry applications and highlighted with select applications. Different technologies are presented in three main sections; secondary ion mass spectrometry (SIMS) imaging, matrix-assisted laser desorption ionization (MALDI) MSI, and emerging MSI technologies with potential for biomaterial analysis. The first two sections (SIMS and MALDI) discuss well-established methods that are continually evolving both in technological advancements and in experimental versatility. In the third section, relatively new and versatile technologies capable of performing measurements under ambient conditions will be introduced, with reported applications in materials chemistry or potential applications discussed. The aim of this review is to provide a concise resource for those interested in utilizing MSI for applications such as biomimetic materials, biological/synthetic material interfaces, polymer formulation and bulk property characterization, as well as the spatial and chemical distributions of nanoparticles, or any other molecular imaging application requiring broad chemical speciation.

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.