A water probe for direct pH measurement of individual particles via micro-Raman spectroscopy.

The acidity of atmospheric aerosols influences fundamental physicochemical processes that affect climate and human health. We recently developed a novel and facile water-probe-based method for directly measuring of the pH for micrometer-size droplets, providing a promising technique to better understand aerosol acidity in the atmosphere. The complex chemical composition of fine particles in the ambient air, however, poses certain challenges to using a water-probe for pH measurement, including interference from interactions between compositions and the influence of similar compositions on water structure. To explore the universality of our method, it was employed to measure the pH of ammonium, nitrate, carbonate, sulfate, and chloride particles. The pH of particles covering a broad range (0-14) were accurately determined, thereby demonstrating that our method can be generally applied, even to alkaline particles. Furthermore, a standard spectral library was developed by integrating the standard spectra of common hydrated ions extracted through the water-probe. The library can be employed to identify particle composition and overcome the spectral overlap problem resulting from similar effects. Using the spectral library, all ions were identified and their concentrations were determined, in turn allowing successful pH measurement of multicomponent (ammonium-sulfate-nitrate-chloride) particles. Insights into the synergistic effect of Cl-, NO3-, and NH4+ depletion obtained with our approach revealed the interplay between pH and volatile partitioning. Given the ubiquity of component partitioning and pH variation in particles, the water probe may provide a new perspective on the underlying mechanisms of aerosol aging and aerosol-cloud interaction.

SERS aptasensor for simultaneous detection of ochratoxin A and zearalenone utilizing a rigid enhanced substrate (ITO/AuNPs/GO) combined with Au@AgNPs.

The contamination of mycotoxins poses a serious threat to global food security, hence the urgent need for simultaneous detection of multiple mycotoxins. Herein, two SERS nanoprobes were synthesized by embedded SERS tags (4-mercaptopyridine, 4MPy; 4-mercaptobenzonitrile, TBN) into the Au and Ag core-shell structure, and each was coupled with the aptamers specific to ochratoxin A (OTA) and zearalenone (ZEN). Meanwhile, a rigid enhanced substrate Indium tin oxide glass/AuNPs/Graphene oxide (ITO/AuNPs/GO) was combined with aptamer functionalized Au@AgNPs via π-π stacking interactions between the aptamer and GO to construct a surface-enhanced Raman spectroscopy (SERS) aptasensor, thereby inducing a SERS enhancement effect for the effective and swift simultaneous detection of both OTA and ZEN. The presence of OTA and ZEN caused signal probes dissociation, resulting in an inverse correlation between Raman signal intensity (1005 cm-1 and 2227 cm-1) and the concentrations of OTA and ZEN, respectively. The SERS aptasensor exhibited wide linear detection ranges of 0.001-20 ng/mL for OTA and 0.1-100 ng/mL for ZEN, with low detection limits (LOD) of 0.94 pg/mL for OTA and 59 pg/mL for ZEN. Furthermore, the developed SERS aptasensor demonstrated feasible applicability in the detection of OTA and ZEN in maize, showcasing its substantial potential for practical implementation.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning.

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Examination of primary aromatic amines content in polylactic acid straws and migration into food simulants using SERS with LC-MS.

Polylactic acid (PLA) straws hold eco-friendly potential; however, residual diisocyanates used to enhance the mechanical strength can generate carcinogenic primary aromatic amines (PAAs), posing health risks. Herein, we present a rapid, comprehensive strategy to detecting PAAs in 18 brands of food-grade PLA straws and assessing their migration into diverse food simulants. Surface-enhanced Raman spectroscopy was conducted to rapidly screen straws for PAAs. Subsequently, qualitative determination of migrating PAAs into various food simulants (4 % acetic acid, 10 % ethanol, 50 % ethanol) occurred at 70 °C for 2 h using liquid chromatography-mass spectrometry. Three PAAs including 4,4'-methylenedianiline, 2,4'-methylenedianiline, and 2,4-diaminotoluene were detected in all straws. Specifically, 2,4-diaminotoluene in 50 % ethanol exceeded specific migration limit of 2 µg/kg, raising safety concerns. Notably, PAAs migration to 10 % and 50 % ethanol surpassed that to 4 % acetic acid within a short 2-hour period. Moreover, PLA straws underwent varying degrees of shape changes before and after migration. Straws with poly(butylene succinate) resisted deformation compared to those without, indicating enhanced heat resistance, while poly(butyleneadipate-co-terephthalate) improved hydrolysis resistance. Importantly, swelling study unveiled swelling effect wasn't the primary factor contributing to the increased PAAs migration in ethanol food simulant, as there was no significant disparity in swelling degrees across different food simulants. FT-IR and DSC analysis revealed higher PAAs content in 50 % ethanol were due to highly concentrated polar ethanol disrupting hydrogen bonds and van der Waal forces holding PLA molecules together. Overall, minimizing contact between PLA straws and alcoholic foods is crucial to avoid potential safety risks posed by PAAs.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Comparison of biochemical changes induced in radioresistant prostate cancer cells by X-rays, radiosensitizing drugs, and a combined therapy using Raman microspectroscopy.

Cancer radioresistance is a major problem in radiotherapy. Many strategies have been proposed to overcome this process including the use of radiosensitizing drugs such as C75 or silibinin. The overall result of all treatments (radiotherapy, chemotherapy, and combined treatment) is cancer cell death. On the other hand, each treatment affects cancer cells differently at the molecular level. However, little is known about biochemical changes induced in cancer cells by these treatments (especially in combined therapy) at the submicroscale. In this study, Raman microspectroscopy was applied to follow such changes induced in radioresistant prostate cancer cells by X-rays, radiosensitizing drugs (C75, silibinin), and a combined treatment. The analysis was supported by the Partial Least Squares Regression method to reveal spectral changes induced by an increasing dose of X-rays and concentrations of the drugs. The obtained regression coefficient (ß) plots were compared to each other using a correlation coefficient (R). Our results show that PC-3 cells exhibit dose- and concentration-dependent responses to the treatment with different biochemical changes induced by X-rays in the presence of C75 and silibinin. Moreover, both drugs affect the cells differently at the submicroscale and independently from the X-ray's presence. Finally, C75 shows significant efficiency in the reduction of cell radioresistance.

Raman analysis of aqueous solutions of ethylene glycol and 1,3-propylene glycol: Fundamental and applied aspects.

Aqueous solutions of glycols, on the one hand, are widely used in many applications; on the other hand, they can serve as simple and representative models for studying intra- and intermolecular hydrogen bonds. In this work, we analyze the possibilities and limitations of Raman spectroscopy for fundamental and applied researches of such solutions on the examples of ethylene glycol (EG) and 1,3-propylene glycol (1,3-PG). It is shown that Raman spectroscopy is an effective tool for monitoring temporal changes in the structure of glycol solutions deposited on substrates. This study demonstrates that the water content in the solutions on the substrates decreases rapidly with time, and the rate of this decrease depends on the chemical structure of both glycol and substrate. It was found that the reduction in the water content leads to slight decrease in the contents of gauche-conformers in the backbones of EG and 1,3-PG molecules. It is shown that use of the 1064 nm excitation ensures a reliable Raman analysis of automotive antifreezes containing various dyes, in particular determination of the relative contents of water and glycol.

Integrating multi-spectral imaging and Raman spectroscopy for in vivo endoscopic assessment of rat intestinal tract.

An integrated system for in vivo multi-spectral imaging (MSI) and Raman spectroscopy was developed to understand the external morphology and internal molecular information of biological tissues. The achieved MSI images were reconstructed by eighteen separated images from 400 nm to 760 nm, whose illumination bands were selected with six tri-channel band filters. Based on the spectral analysis algorithms, the spatial distribution patterns of blood volume, blood oxygen content and tissue scatterer volume fraction were visualized. In vivo Raman spectral measurements were executed by inserting specially designed optical probe into instrumental channel of endoscope. By this way, the molecular composition at selected sampling points could be identified with its fingerprint spectral information under the guidance of molecular imaging modality. Therefore, both structural and compositional features of intestinal membrane could be addressed without labeling and continuously. The achieved results testified that our presented methodology reveals insights not easily extracted from either MSI or Raman spectroscopy individually, which brings the enrichment of biological and chemical meanings for future in vivo studies.

In situ surface-enhanced Raman spectroscopy for membrane protein analysis and sensing.

Membrane proteins are involved in a variety of dynamic cellular processes and exploration of the structural basis of membrane proteins is of significance for a better understanding of their functions. In situ analysis of membrane proteins and their dynamics is, however, challenging for conventional techniques. Surface-enhanced Raman spectroscopy (SERS) is powerful in protein structural characterization, allowing for sensitive, in-situ and real-time identification and dynamic monitoring under physiological conditions. In this review, the applications of SERS in probing membrane proteins are outlined, discussed and prospected. It starts with a brief introduction to membrane proteins, SERS theories and SERS-based strategies that commonly-used for membrane proteins. How to assemble phospholipid biolayers on SERS-active materials is highlighted, followed by respectively discussing about direct and indirect strategies for membrane protein sensing. SERS-based monitoring of protein-ligand interactions is finally introduced and its potential in biomedical applications is discussed in detail. The review ends with critical discussion about current challenges and limitations of this research field, and the promising perspectives in both fundamental and applied sciences.

Characterisation of skin penetration pathways using stimulated Raman scattering microscopy.

Understanding the mechanisms governing the penetration of substances into the skin is crucial for the development of safe and effective topical drug delivery systems and skincare products. This study examined the partitioning of model permeants into human skin, by assessing six substances with diverse logP values. We employed stimulated Raman scattering (SRS) microscopy, an ambient, label-free optical imaging technique known for its ability to provide chemical distribution with subcellular resolution. Our investigation assessed partitioning into the two primary pathways through which substances traverse the skin: the intercellular lipid matrix and the intracellular route via corneocyte cells. We observed that the partitioning behaviour was strongly influenced by the lipophilicity of the molecule, with lipophilic compounds showing greater affinity for intercellular matrix with increased lipophilicity. Conversely, hydrophilic molecules demonstrated a preference for corneocyte cells, with their affinity increasing with increased hydrophilicity. The findings contribute to our understanding of the mechanisms underlying topical delivery and offer important implications and new methods beneficial for the development of safe and effective topical products. In addition, the methods presented could be valuable to reveal changes in drug partitioning or to assess targeting approaches in diseased skin models.