Revealing spatial distribution and accessibility of cell wall polymers in bamboo through chemical imaging and mild chemical treatments.

Understanding the distribution and accessibility of polymers within plant cell walls is crucial for addressing biomass recalcitrance in lignocellulosic materials. In this work, Imaging Fourier Transform Infrared (FTIR) and Raman spectroscopy, coupled with targeted chemical treatments, were employed to investigate cell wall polymer distribution in two bamboo species at both tissue and cell wall levels. Tissue-level Imaging FTIR revealed significant disparities in the distribution and chemical activity of cell wall polymers between the fibrous sheath and fibrous strand. At the cell wall level, Imaging Raman spectroscopy delineated a distinct difference between the secondary wall and intercellular layer, with the latter containing higher levels of lignin, hydroxycinnamic acid (HCA), and xylan, and lower cellulose. Mild acidified sodium chlorite treatment led to partial removal of lignin, HCA, and xylan from the intercellular layer, albeit to a lesser extent than alkaline treatment, indicating susceptibility of these polymers to chemical treatment. In contrast, lignin in the secondary wall exhibited limited reactivity to acidified sodium chlorite but was slightly removed by alkaline treatment, suggesting stable chemical properties with slight alkaline intolerance. These findings provide valuable insights into the inherent design mechanism of plant cells and their efficient utilization.

Mohs micrographic surgery challenges and new technologies to optimize care of cutaneous malignancies of the ear.

Cutaneous malignancies affecting the ear, exacerbated by extensive ultraviolet (UV) exposure, pose intricate challenges owing to the organ's complex anatomy. This article investigates how the anatomy contributes to late-stage diagnoses and ensuing complexities in surgical interventions. Mohs Micrographic Surgery (MMS), acknowledged as the gold standard for treating most cutaneous malignancies of the ear, ensures superior margin control and cure rates. However, the ear's intricacy necessitates careful consideration of tissue availability and aesthetic outcomes. The manuscript explores new technologies like Reflectance Confocal Microscopy (RCM), Optical Coherence Tomography (OCT), High-Frequency, High-Resolution Ultrasound (HFHRUS), and Raman spectroscopy (RS). These technologies hold the promise of enhancing diagnostic accuracy and providing real-time visualization of excised tissue, thereby improving tumor margin assessments. Dermoscopy continues to be a valuable non-invasive tool for identifying malignant lesions. Staining methods in Mohs surgery are discussed, emphasizing hematoxylin and eosin (H&E) as the gold standard for evaluating tumor margins. Toluidine blue is explored for potential applications in assessing basal cell carcinomas (BCC), and immunohistochemical staining is considered for detecting proteins associated with specific malignancies. As MMS and imaging technologies advance, a thorough evaluation of their practicality, cost-effectiveness, and benefits becomes essential for enhancing surgical outcomes and patient care. The potential synergy of artificial intelligence with these innovations holds promise in revolutionizing tumor detection and improving the efficacy of cutaneous malignancy treatments.

Harnessing Raman spectroscopy for the analysis of plant diversity.

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. RESULTS: Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SIGNIFICANCE: The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Temperature-Caused Changes in Raman Pattern and Protein Profiles of Winter Triticale (x Triticosecale, Wittm.) Field-Grown Seedlings.

Climate change, which causes periods with relatively high temperatures in winter in Poland, can lead to a shortening or interruption of the cold hardening of crops. Previous research indicates that cold acclimation is of key importance in the process of acquiring cereal tolerance to stress factors. The objective of this work was to verify the hypothesis that both natural temperature fluctuations and the plant genotype influence the content of metabolites as well as proteins, including antioxidant enzymes and photosystem proteins. The research material involved four winter triticale genotypes, differing in their tolerance to stress under controlled conditions. The values of chlorophyll a fluorescence parameters and antioxidant activity were measured in their seedlings. Subsequently, the contribution of selected proteins was verified using specific antibodies. In parallel, the profiling of the contents of chlorophylls, carotenoids, phenolic compounds, and proteins was carried out by Raman spectroscopy. The obtained results indicate that a better PSII performance along with a higher photosystem II proteins content and thioredoxin reductase abundance were accompanied by a higher antioxidant activity in the field-grown triticale seedlings. The Raman studies showed that the cold hardening led to a variation in photosynthetic dyes and an increase in the phenolic to carotenoids ratio in all DH lines.

Advancements in Neurosurgical Intraoperative Histology.

Despite their relatively low incidence globally, central nervous system (CNS) tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and the identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and the rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes.

Reliable detection of malachite green by self-assembled SERS substrates based on gold-silicon heterogeneous nano pineapple structures.

Morphology regulation of heterodimer nanoparticles and the use of their asymmetric features for further practical applications are crucial because of the rich optical properties and various combinations of heterodimers. This work used silicon to asymmetrically wrap half of a gold sphere and grew gold branches on the bare gold surface to form heterogeneous nano pineapples (NPPs) which can effectively improve Surface-enhanced Raman scattering (SERS) properties through chemical enhancement and lightning-rod effect respectively. The asymmetric structures of NPPs enabled them to self-assemble into the monolayer membrane with consistent branch orientation. The prepared substrate had high homogeneity and better SERS ability than disorganized substrates, and achieved reliable detection of malachite green (MG) in clams with a detection limit of 7.8 × 10-11 M. This work provided a guide to further revise the morphology of heterodimers and a new idea for the use of asymmetric dimers for practically photochemical and biomedical sensing.

Regenerated SERS substrate based on Ag/AuNPs-TiO2-oxidized carbon cloth for detection of imidacloprid.

Imidacloprid (IMI) are widely used in modern tea industry for pest control, but IMI residues pose a great threat to human health. Herein, we propose a regeneration metal-semiconductor SERS substrate for IMI detection. We fabricated the SERS sensor through the in-situ growth of a nano-heterostructure incorporating a semiconductor (TiO2) and plasmonic metals (Au, Ag) on oxidized carbon cloth (OCC). Leveraging the high-density hot spots, the formed Ag/AuNPs-TiO2-OCC substrate exhibits higher enhancement factors (1.92 × 108) and uniformity (RSD = 7.68%). As for the detection of IMI on the substrate, the limit of detection was lowered to 4.1 × 10-6 µg/mL. With a hydrophobic structure, the Ag/AuNPs-TiO2-OCC possessed excellent self-cleaning performance addressing the limitation of single-use associated with traditional SERS substrates, as well as the degradation capability of the substrate under ultraviolet (UV) light. Accordingly, Ag/AuNPs-TiO2-OCC showcases outstanding SERS sensing and regenerating properties, making it poised for extensive application in the field of food safety assurance.

Investigation of Melphalan interaction as an alkylating agent with nucleotides by using surface enhanced Raman spectroscopy (SERS).

SERS (Surface Enhanced Raman Spectroscopy) is a new Raman spectroscopy which relies on Surface Plasmon Resonance (SPR) of metal nanoparticles. We have applied colloidal silver and gold nanoparticles as amplifier agents to enhance nucleotide Raman signals. It is observed that without these enhancing agents, it is impossible to investigate nucleotide spectrum due to weak Raman signals. Interaction mechanism of Melphalan, an anticancer drug with four nucleotides (Adenine, Cytosine, Guanine, Thymine) was investigated using SERS to detect and identify changes due to alkylating process in Raman spectra. After incubating Melphalan drug with nucleotides for 24 h at 37 °C, some changes occurred in SERS spectrum and interpretation of SERS spectra revealed the influence of the alkyl substitution on peaks and Raman shifts. After incubation of Melphalan with each nucleotide, intensity of relevant SERS signals assigned to Amid III group of Cytosine and Amid I of Thymine decreased significantly, confirming alkylating taking place. In this study, we also investigated the effect of nanoparticles type on nucleotide spectrum. We could not obtain useful information in the cases of guanine nucleotide. The SERS spectrum of Cytosine as an example of nucleotides in aqueous solution compared to solid state and results demonstrated that in solid state better signals were obtained than in liquid state.

Label-free Raman spectroscopy reveals tumor microenvironmental changes induced by intermittent fasting for the prevention of breast cancer in animal model.

The development of tools that can provide a holistic picture of the evolution of the tumor microenvironment in response to intermittent fasting on the prevention of breast cancer is highly desirable. Here, we show, for the first time, the use of label-free Raman spectroscopy to reveal biomolecular alterations induced by intermittent fasting in the tumor microenvironment of breast cancer using a dimethyl-benzanthracene induced rat model. To quantify biomolecular alterations in the tumor microenvironment, chemometric analysis of Raman spectra obtained from untreated and treated tumors was performed using multivariate curve resolution-alternative least squares and support vector machines. Raman measurements revealed remarkable and robust differences in lipid, protein, and glycogen content prior to morphological manifestations in a dynamically changing tumor microenvironment, consistent with the proteomic changes observed by quantitative mass spectrometry. Taken together with its non-invasive nature, this research provides prospective evidence for the clinical translation of Raman spectroscopy to identify biomolecular variations in the microenvironment induced by intermittent fasting for the prevention of breast cancer, providing new perspectives on the specific molecular effects in the tumorigenesis of breast cancer.

Vibrational microspectroscopy as a tool to unveil new chemotherapeutic strategies against osteosarcoma.

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd2SpmCl4) and spermidine (Pd3Spd2Cl6) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested.

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis.

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum.

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific "metabolic fingerprints" of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are "in-house" protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

Silver nanopopcorns decorated on flexible membrane for SERS detection of nitrofurazone.

The synthesis of three-dimensional silver nanopopcorns (Ag NPCs) onto a flexible polycarbonate membrane (PCM) for the detection of nitrofurazone (NFZ) on the fish surface by surface-enhanced Raman spectroscopy (SERS) is presented. The proposed flexible Ag-NPCs/PCM SERS substrate exhibits significant Raman signal intensity enhancement with the measured enhancement factor of 2.36 × 106. This is primarily attributed to the hotspots created on Ag NPCs, including numerous nanoscale protrusions and internal crevices distributed across the surface of Ag NPCs. The detection of NFZ by this flexible SERS substrate demonstrates a low limit of detection (LOD) of 3.7 × 10-9 M and uniform and reproducible Raman signal intensities with a relative standard deviation below 8.34%. It also exhibits excellent stability, retaining 70% of its efficacy even after 10 days of storage. Notably, the practical detection of NFZ in tap water, honey water, and fish surfaces achieves LOD values of 1.35 × 10-8 M, 5.76 × 10-7 M, and 3.61 × 10-8 M, respectively,  which highlights its effectiveness across different sample types. The developed Ag-NPCs/PCM SERS substrate presents promising potential for sensitive SERS detection of toxic substances in real-world samples.

Kidney stone growth through the lens of Raman mapping.

Bulk composition of kidney stones, often analyzed with infrared spectroscopy, plays an essential role in determining the course of treatment for kidney stone disease. Though bulk analysis of kidney stones can hint at the general causes of stone formation, it is necessary to understand kidney stone microstructure to further advance potential treatments that rely on in vivo dissolution of stones rather than surgery. The utility of Raman microscopy is demonstrated for the purpose of studying kidney stone microstructure with chemical maps at ≤ 1 µm scales collected for calcium oxalate, calcium phosphate, uric acid, and struvite stones. Observed microstructures are discussed with respect to kidney stone growth and dissolution with emphasis placed on < 5 µm features that would be difficult to identify using alternative techniques including micro computed tomography. These features include thin concentric rings of calcium oxalate monohydrate within uric acid stones and increased frequency of calcium oxalate crystals within regions of elongated crystal growth in a brushite stone. We relate these observations to potential concerns of clinical significance including dissolution of uric acid by raising urine pH and the higher rates of brushite stone recurrence compared to other non-infectious kidney stones.

Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate.

BACKGROUND: Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety. RESULTS: In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 105). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca2⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL. SIGNIFICANCE: It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products.

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer.

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm-1 (FTIR) and at 2944 cm-1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm-1, 2974 cm-1 (FTIR) and 1714 cm-1, 2817 cm-1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

Insights into Plastic Degradation Processes in Marine Environment by X-ray Photoelectron Spectroscopy Study.

The present study employs X-ray photoelectron spectroscopy (XPS) to analyze plastic samples subjected to degradation processes with the aim to gain insight on the relevant chemical processes and disclose fragmentation mechanisms. Two model plastics, namely polystyrene (PS) and polyethylene (PE), are selected and analyzed before and after artificial UV radiation-triggered weathering, under simulated environmental hydrodynamic conditions, in fresh and marine water for different time intervals. The object of the study is to identify and quantify chemical groups possibly evidencing the occurrence of hydrolysis and oxidation reactions, which are the basis of degradation processes in the environment, determining macroplastic fragmentation. Artificially weathered plastic samples are analyzed also by Raman and FT-IR spectroscopy. Changes in surface chemistry with weathering are revealed by XPS, involving the increase in chemical moieties (hydroxyl, carbonyl, and carboxyl functionalities) which can be correlated with the degradation processes responsible for macroplastic fragmentation. On the other hand, the absence of significant modifications upon plastics weathering evidenced by Raman and FT-IR spectroscopy confirms the importance of investigating plastics surface, which represents the very first part of the materials exposed to degradation agents, thus revealing the power of XPS studies for this purpose. The XPS data on experimentally weathered particles are compared with ones obtained on microplastics collected from real marine environment for investigating the occurring degradation processes.