Marc Brecht

Monodisperse porous polymer spheres are functional materials with attractive properties such as high cohesive strength, strong adsorptivity, and a high degree of surface functionalization due to their large specific surface area. They are widely used in various fields, including biomedicine, instrumental analytics as stationary phases in HPLC columns, and sensor technology. In this work, the formation mechanism of porous poly(glycidyl methacrylate-co-ethylene dimethacrylate) (p(GMA-co-EDMA)) particles was investigated in a time-resolved experiment using scanning electron microscopy (SEM) and two-dimensional confocal Raman spectroscopy. Data analysis revealed that the particles are formed via rapidly reacting anisotropic Janus-like intermediates that assemble into large agglomerates of different morphologies. Spectral unmixing of the Raman data enabled the determination of relative concentration changes in the reactants over time. This study uncovers a pool of previously unknown anisotropic particle species that offer new opportunities for subsequent functionalization and material design.

Miniaturization of liquid chromatography (LC) systems offers numerous advantages. Nevertheless, miniaturized LC is still not widely used. Here, we present the additive manufacturing of hierarchically structured macro- and mesoporous functionalized polymer monoliths and their application for miniaturized reversed-phase chromatographic separations. A CAD-predesigned macroporous structure was generated by 3D printing of a repetitive design of Schoen gyroids from a photoresin via two-photon polymerization. For this, a photoresin formulation was developed containing a porogenic solvent to induce the formation of mesopores and a hydrophobic monomer to achieve a surface functionality suitable for reversed-phase chromatography. This strategy enables independent tuning of macropore geometry and mesoporosity, which is not readily achievable by conventional polymer monolith synthesis. Successful fabrication of hierarchically structured polymer monoliths was demonstrated by scanning electron microscopy and nano-computed tomography. The monolithic stationary phase was connected to a microfluidic system, and chromatographic separation of the nonpolar analytes naphthalene and fluoranthene was demonstrated. This highlights the applicability of the hierarchically structured porous functionalized polymer monolith as a reversed-phase stationary phase.

Monodisperse porous polymer spheres are functional materials with attractive properties such as high cohesive strength, strong adsorptivity, and a high degree of surface functionalization due to their large specific surface area. They are widely used in various fields, including biomedicine, instrumental analytics as stationary phases in HPLC columns, and sensor technology. In this work, the formation mechanism of porous poly(glycidyl methacrylate-co-ethylene dimethacrylate) (p(GMA-co-EDMA)) particles was investigated in a time-resolved experiment using scanning electron microscopy (SEM) and two-dimensional confocal Raman spectroscopy. Data analysis revealed that the particles are formed via rapidly reacting anisotropic Janus-like intermediates that assemble into large agglomerates of different morphologies. Spectral unmixing of the Raman data enabled to determine relative concentration changes of the reactants over time. This study uncovers a pool of previously unknown anisotropic particle species that offer new opportunities for subsequent functionalization and material design.

Introduction: Plate culturing and visual inspection are the gold standard methods for bacterial identification. Despite the growing attention on molecular biology techniques, colony identification using agar plates remains manual, interpretative, and heavily reliant on human experience, making it prone to errors. Advanced imaging techniques, like hyperspectral imaging, offer potential alternatives. However, the use of hyperspectral imaging in the VIS-NIR region has been hindered by sensitivity to various components and culture medium changes, leading to inaccurate results. The application of hyperspectral imaging in the ultraviolet (UV) region has not been explored, despite the presence of specific absorption and emission peaks in bacterial components.Methods: To address this gap, we developed a predictive model for bacterial colony detection and identification using UV hyperspectral imaging. The model utilizes hyperspectral images acquired in the UV wavelength range of 225–400 nm, processed with principal component analysis (PCA) and discriminant analysis (DA). The measurement setup includes a hyperspectral imager, a PC for automated data analysis, and a conveyor belt system to transport agar plates for automated analysis. Four bacterial species (Escherichia coli, Staphylococcus, Pseudomonas, and Shewanella) were cultured on two different media, Luria Bertani and Tryptic Soy, to train and validate the model.Results: The PCA-DA-based model demonstrated high accuracy (90%) in differentiating bacterial species based on the first three principal components, highlighting the potential of UV hyperspectral imaging for bacterial identification.Discussion: This study shows that UV hyperspectral imaging, coupled with advanced data analysis techniques, offers a robust and automated alternative to traditional methods for bacterial identification. The model's high accuracy emphasizes the untapped potential of UV hyperspectral imaging in microbiological analysis, reducing human error and improving reliability in bacterial species differentiation.

Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination R² = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications.

Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups—hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples—while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry.

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics.

Malignant primary brain tumors are a group of highly aggressive and often infiltrating tumors that lack adequate therapeutic treatments to achieve long time survival. Complete tumor removal is one precondition to reach this goal. A promising approach to optimize resection margins and eliminate remaining infiltrative so-called guerilla cells is photodynamic therapy (PDT) using organic photosensitizers that can pass the disrupted blood–brain-barrier and selectively accumulate in tumor tissue. Hypericin fulfills these conditions and additionally offers outstanding photophysical properties, making it an excellent choice as a photosensitizing molecule for PDT. However, the actual hypericin-induced PDT cell death mechanism is still under debate. In this work, hypericin-induced PDT was investigated by employing the three distinct fluorescent probes hypericin, resorufin and propidium iodide (PI) in fluorescence-lifetime imaging microscopy (FLIM). This approach enables visualizing the PDT-induced photodamaging and dying of single, living glioma cells, as an in vitro tumor model for glioblastoma. Hypericin PDT and FLIM image acquisition were simultaneously induced by 405 nm laser irradiation and sequences of FLIM images and fluorescence spectra were recorded to analyze the PDT progression. The reproducibly observed cellular changes provide insight into the mechanism of cell death during PDT and suggest that apoptosis is the initial mechanism followed by necrosis after continued irradiation. These new insights into the mechanism of hypericin PDT of single glioma cells may help to adjust irradiation doses and improve the implementation as a therapy for primary brain tumors.

The peripherical protons of the dye molecule hypericin can undergo structural interconversion (tautomerization) between different isomers separated by a low energy barrier with rates that depends sensitively on the interaction with local chemical environment defined by the nature of host material. We investigate the deuterium (D) isotope effect of hypericin tautomerism at the single‐molecule level to avoid ensemble averaging in different polymer matrices by a combined spectroscopic and computational approach. In the ′innocent′ PMMA matrix only intramolecular isotope effects on the internal conversion channel and tautomerization are observed; while PVA specifically interacts with the probe via H‐ and D‐bonding. This establishes a single molecular picture on intra‐ and intermolecular nano‐environment effects to control chromophore photophysics and ‐chemistry.

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R²_cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes.