Solvatochromism in Mixtures of Hydrogen Bond Acceptor (HBA) Solvents with Water.

The UV/Vis absorption energies νmax of Reichardt's dye B30 with respect to ET(30) and 4-nitroaniline (NA) are investigated as a function of the solvent composition Nav,z. in co-solvent/water mixtures. Nav,z. is the average molar concentration of the solvent mixture at a given solvent fraction z. The z can be the mole, the volume or the mass fraction. The co-solvents considered were acetonitrile, acetone, tetrahydrofuran, pyridine, piperidine and 2-(diethylamino)-ethanol. Acetone and acetonitrile can be expected to slightly enhance the water structure at low co-solvent concentrations. This interpretation is supported by the analysis of the refractive index as a function of the solvent composition. In general, it can be stated that the structural complexity of the binary solvent mixtures is mainly responsible for the evolution of the absorption energies ET(30) or νmax(NA) as a function of the mixture composition. In particular, the endothermic solvation of NA in co-solvent/water mixtures and its effect on the νmax(NA) is highlighted.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer.

Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.

Toward sub-second solution exchange dynamics in flow reactors for liquid-phase transmission electron microscopy.

Liquid-phase transmission electron microscopy is a burgeoning experimental technique for monitoring nanoscale dynamics in a liquid environment, increasingly employing microfluidic reactors to control the composition of the sample solution. Current challenges comprise fast mass transport dynamics inside the central nanochannel of the liquid cell, typically flow cells, and reliable fixation of the specimen in the limited imaging area. In this work, we present a liquid cell concept - the diffusion cell - that satisfies these seemingly contradictory requirements by providing additional on-chip bypasses to allow high convective transport around the nanochannel in which diffusive transport predominates. Diffusion cell prototypes are developed using numerical mass transport models and fabricated on the basis of existing two-chip setups. Important hydrodynamic parameters, i.e., the total flow resistance, the flow velocity in the imaging area, and the time constants of mixing, are improved by 2-3 orders of magnitude compared to existing setups. The solution replacement dynamics achieved within seconds already match the mixing timescales of many ex-situ scenarios, and further improvements are possible. Diffusion cells can be easily integrated into existing liquid-phase transmission electron microscopy workflows, provide correlation of results with ex-situ experiments, and can create additional research directions addressing fast nanoscale processes.

Amplifying Sensing Capabilities: Combining Plasmonic Resonances and Fresnel Reflections through Multivariate Analysis.

Multivariate analysis applied in biosensing greatly improves analytical performance by extracting relevant information or bypassing confounding factors such as nonlinear responses or experimental errors and noise. Plasmonic sensors based on various light coupling mechanisms have shown impressive performance in biosensing by detecting dielectric changes with high sensitivity. In this study, gold nanodiscs are used as metasurface in a Kretschmann setup, and a variety of features from the reflectance curve are used by machine learning to improve sensing performance. The nanostructures of the metasurface generate new plasmonic features, apart from the typical resonance that occurs in the classical Kretschmann mode of a gold thin film, related to the evanescent field beyond total internal reflection. When the engineered metasurface is integrated into a microfluidic chamber, the device provides additional spectral features generated by Fresnel reflections at all dielectric interfaces. The increased number of features results in greatly improved detection. Here, multivariate analysis enhances analytical sensitivity and sensor resolution by 200% and more than 20%, respectively, and reduces prediction errors by almost 40% compared to a standard plasmonic sensor. The combination of plasmonic metasurfaces and Fresnel reflections thus offers the possibility of improving sensing capabilities even in commonly available setups.

Evidence of internalized microplastics in mussel tissues detected by volumetric Raman imaging.

Microplastics are a global ecological concern due to their potential risk to wildlife and human health. Animals ingest microplastics, which can enter the trophic chain and ultimately impact human well-being. The ingestion of microplastics can cause physical and chemical damage to the animals' digestive systems, affecting their health. To estimate the risk to ecosystems and human health, it is crucial to understand the accumulation and localization of ingested microplastics within the cells and tissues of living organisms. However, analyzing this issue is challenging due to the risk of sample contamination, given the ubiquity of microplastics. Here, an analytical approach is employed to confirm the internalization of microplastics in cryogenic cross-sections of mussel tissue. Using 3D Raman confocal microscopy in combination with chemometrics, microplastics measuring 1 µm in size were detected. The results were further validated using optical and fluorescence microscopy. The findings revealed evidence of microplastics being internalized in the digestive epithelial tissues of exposed mussels (Mytilus galloprovincialis), specifically within the digestive cells forming digestive alveoli. This study highlights the need to investigate the internalization of microplastics in organisms like mussels, as it helps us understand the potential risks they pose to aquatic biota and ultimately to human health. By employing advanced imaging techniques, challenges associated with sample contamination can be overcome and valuable insights into the impact of microplastics on marine ecosystems and human consumers are provided.

Hybrid Organosulfur Network/MWCNT Composite Cathodes for Li-S Batteries.

Lithium-sulfur (Li-S) batteries hold a promising position as candidates for next-generation high-energy storage systems. Here, we combine inverse vulcanization of sulfur with multiwalled carbon nanotubes (MWCNTs) to increase the conductivity of cathode materials for Li-S batteries. The mixing process of inversely vulcanized sulfur copolymer networks with MWCNTs is aided by shear in a two-roll mill to take advantage of the soft nature of the copolymer. The high-throughput mixing method demands a source of conductive carbon that can be intimately mixed with the S copolymer, rendering MWCNTs an excellent choice for this purpose. The resulting sulfur copolymer network-MWCNTs composites were thoroughly characterized in terms of structure, chemical composition, thermal, and electronic transport properties, and finally evaluated by electrochemical benchmarking. These promising hybrids yielded electrodes with high sulfur content and demonstrate stable electrochemical performance exhibiting a specific capacity of ca. 550 mAh·gsulfur-1 (380 mAh·gelectrode-1) even after 500 charge-discharge cycles at specific current of 167 mA·g-1 (corresponds to 0.1C discharge rate), and thus are superior to melt-infiltrated reference samples.

Temperature-Modulated Reversible Clustering of Gold Nanorods Driven by Small Surface Ligands.

Temperature-modulated colloidal phase of plasmonic nanoparticles is a convenient playground for resettable soft-actuators or colorimetric sensors. To render reversible clustering under temperature change, bulky ligands are required, especially if anisotropic morphologies are of interest. This study showcases thermoresponsive gold nanorods by employing small surface ligands, bis (p-sulfonatophenyl) phenyl-phosphine dihydrate dipotassium salt (BSPP) and native cationic surfactant. Temperature-dependent analysis in real-time allowed to describe the structural features (interparticle distance and cluster size) as well as thermal parameters, melting and freezing temperatures. These findings suggest that neither covalent Au-S bonds nor bulky ligands are required to obtain a robust thermoresponsive system based on anisotropic gold nanoparticles, paving the way to stimuli-responsive nanoparticles with a wide range of sizes and geometries.

Expert-approved best practice recommendations on the use of sedative drugs and intentional sedation in specialist palliative care (SedPall).

BACKGROUND: The use of sedative drugs and intentional sedation in end-of-life care is associated with clinical, ethical and legal challenges. In view of these and of the issue's great importance to patients undergoing intolerable suffering, we conducted a project titled SedPall ("From anxiolysis to deep continuous sedation - Development of recommendations for sedation in palliative care") with the purpose of developing best practice recommendations on the use of sedative drugs and intentional sedation in specialist palliative care and obtaining feedback and approval from experts in this area. DESIGN: Our stepwise approach entailed drafting the recommendations, obtaining expert feedback, conducting a single-round Delphi study, and convening a consensus conference. As an interdisciplinary group, we created a set of best practice recommendations based on previously published guidance and empirical and normative analysis, and drawing on feedback from experts, including patient representatives and of public involvement participants. We set the required agreement rate for approval at the single-round Delphi and the consensus conference at ≥80%. RESULTS: Ten experts commented on the recommendations' first draft. The Delphi panel comprised 50 experts and patient and public involvement participants, while 46 participants attended the consensus conference. In total, the participants in these stages of the process approved 66 recommendations, covering the topics "indications", "intent/purpose [of sedation]", "decision-making", "information and consent", "medication and type of sedation", "monitoring", "management of fluids and nutrition", "continuing other measures", "support for relatives", and "team support". The recommendations include suggestions on terminology and comments on legal issues. CONCLUSION: Further research will be required for evaluating the feasibility of the recommendations' implementation and their effectiveness. The recommendations and the suggested terminology may serve as a resource for healthcare professionals in Germany on the use of sedative drugs and intentional sedation in specialist palliative care and may contribute to discussion on the topic at an international level. TRIAL REGISTRATION: DRKS00015047 (German Clinical Trials Register).

The importance of choosing a proper validation strategy in predictive models. A tutorial with real examples.

Machine learning is the art of combining a set of measurement data and predictive variables to forecast future events. Every day, new model approaches (with high levels of sophistication) can be found in the literature. However, less importance is given to the crucial stage of validation. Validation is the assessment that the model reliably links the measurements and the predictive variables. Nevertheless, there are many ways in which a model can be validated and cross-validated reliably, but still, it may be a model that wrongly reflects the real nature of the data and cannot be used to predict external samples. This manuscript shows in a didactical manner how important the data structure is when a model is constructed and how easy it is to obtain models that look promising with wrong-designed cross-validation and external validation strategies. A comprehensive overview of the main validation strategies is shown, exemplified by three different scenarios, all of them focused on classification.

Light-Driven Self-Oscillation of Thermoplasmonic Nanocolloids.

Self-oscillation-the periodic change of a system under a non-periodic stimulus-is vital for creating low-maintenance autonomous devices in soft robotics technologies. Soft composites of macroscopic dimensions are often doped with plasmonic nanoparticles to enhance energy dissipation and generate periodic response. However, while it is still unknown whether a dispersion of photonic nanocrystals may respond to light as a soft actuator, a dynamic analysis of nanocolloids self-oscillating in a liquid is also lacking. This study presents a new self-oscillator model for illuminated colloidal systems. It predicts that the surface temperature of thermoplasmonic nanoparticles and the number density of their clusters jointly oscillate at frequencies ranging from infrasonic to acoustic values. New experiments with spontaneously clustering gold nanorods, where the photothermal effect alters the interplay of light (stimulus) with the disperse system on a macroscopic scale, strongly support the theory. These findings enlarge the current view on self-oscillation phenomena and anticipate the colloidal state of matter to be a suitable host for accommodating light-propelled machineries. In broad terms, a complex system behavior is observed, which goes from periodic solutions (Hopf-Poincaré-Andronov bifurcation) to a new dynamic attractor driven by nanoparticle interactions, linking thermoplasmonics to nonlinearity and chaos.

The mere presence of a smartphone reduces basal attentional performance.

The smartphone has become an indispensable part of everyday life. It enables endless possibilities and offers persistent access to a multiplicity of entertainment, information, and social contacts. The development towards a greater use and a persistent presence of the smartphone does not only lead to advantages, but also raises potential for negative consequences and a negative influence on attention. In this research, the hypothesis of the mere smartphone presence leading to cognitive costs and a lower attention is being tested. The smartphone may use limited cognitive resources and consequently lead to a lower cognitive performance. To investigate this hypothesis, participants aged 20-34 perform a concentration and attention test in the presence and absence of a smartphone. The results of the conducted experiment imply that the mere presence of a smartphone results in lower cognitive performance, which supports the hypothesis of the smartphone presence using limited cognitive resources. The study as well as the subsequent results and the resulting practical implications are presented and discussed in this paper.

Misinterpretation in microplastic detection in biological tissues: When 2D imaging is not enough.

The presence of microplastics in the food chain is a public concern worldwide, and its analysis is an analytical challenge. In our research, we apply Raman imaging to study the presence of 1 µm polystyrene microplastics in cryosections of Mytilus galloprovincialis due to its wide geographic distribution, widespread occurrence in the food web, and general high presence in the environment. Ingested microplastics are accumulated in the digestive tract, but a large number can also be rapidly eliminated. Some authors state that the translocation of microplastics to the epithelial cells is possible, increasing the risk of microplastics transmission along the food chain. However, as seen in our study, a surface imaging approach (2D) is probably not enough to confirm the internalization of particles and avoid misinterpretation. In fact, while some microplastic particles were detected in the epithelium by 2D Raman imaging, further 3D Raman imaging analysis demonstrated that those particles were dragged from the lumens to the epithelium during sample preparation due to the blade drag effect of the cryotome, and subsequently located on the surface of the analyzed cryosection, discarding the translocation to the epithelial cells. This effect can also happen when the samples are fortuitously contaminated during sample preparation. Several research articles that use similar analytical techniques have shown the presence of microplastics in different types of tissue. It is not our intention to put such results in doubt, but the present work points out the necessity of appropriate three-dimensional analytical methods including data interpretation and the need to go a step further than just surface imaging analysis.

Generalization of Self-Assembly Toward Differently Shaped Colloidal Nanoparticles for Plasmonic Superlattices.

Periodic superlattices of noble metal nanoparticles  have demonstrated superior plasmonic properties compared to randomly distributed plasmonic arrangements due to near-field coupling and constructive far-field interference. Here, a chemically driven, templated self-assembly process of colloidal gold nanoparticles is investigated and optimized, and the technology is extended toward a generalized assembly process for variously shaped particles, such as spheres, rods, and triangles. The process yields periodic superlattices of homogenous  nanoparticle clusters on a centimeter scale. Electromagnetically simulated absorption spectra and corresponding experimental extinction measurements demonstrate excellent agreement in the far-field for all particle types and different lattice periods. The electromagnetic simulations reveal the specific nano-cluster near-field behavior, predicting the experimental findings provided by surface-enhanced Raman scattering measurements. It turns out that periodic arrays of spherical nanoparticles produce higher surface-enhanced Raman scattering enhancement factors than particles with less symmetry as a result of very well-defined strong hotspots.

Photonic Technology for In Vivo Monitoring of Hypoxia-Ischemia.

Surveillance of physiological parameters of newborns during delivery triggers medical decision-making, can rescue life and health, and helps avoid unnecessary cesareans. Here, the development of a photonic technology for monitoring perinatal asphyxia is presented and validated in vivo in a preclinical stage. Contrary to state of the art, the technology provides continuous data in real-time in a non-invasive manner. Moreover, the technology does not rely on a single parameter as pH or lactate, instead monitors changes of the entirety of physiological parameters accessible by Raman spectroscopy. By a fiber-coupled Raman probe that is in controlled contact with the skin of the subject, near-infrared Raman spectra are measured and analyzed by machine learning algorithms to develop classification models. As a performance benchmarking, various hybrid and non-hybrid classifiers are tested. In an asphyxia model in newborn pigs, more than 1000 Raman spectra are acquired at three different clinical phases-basal condition, hypoxia-ischemia, and post-hypoxia-ischemia stage. In this preclinical proof-of-concept study, figures of merit reach 90% levels for classifying the clinical phases and demonstrate the power of the technology as an innovative medical tool for diagnosing a perinatal adverse outcome.

Towards multi-molecular surface-enhanced infrared absorption using metal plasmonics.

Surface-enhanced infrared absorption (SEIRA) leads to a largely improved detection of polar molecules, compared to standard infrared absorption. The enhancement principle is based on localized surface plasmon resonances of the substrate, which match the frequency of molecular vibrations in the analyte of interest. Therefore, in practical terms, the SEIRA sensor needs to be tailored to each specific analyte. We review SEIRA sensors based on metal plasmonics for the detection of biomolecules such as DNA, proteins, and lipids. We further focus this review on chemical SEIRA sensors, with potential applications in quality control, as well as on the improvement in sensor geometry that led to the development of multiresonant SEIRA substrates as sensors for multiple analytes. Finally, we give an introduction into the integration of SEIRA sensors with surface-enhanced Raman scattering (SERS).

Design and Development of a Bimodal Optical Instrument for Simultaneous Vibrational Spectroscopy Measurements.

Vibrational spectroscopy techniques are widely used in analytical chemistry, physics and biology. The most prominent techniques are Raman and Fourier-transform infrared spectroscopy (FTIR). Combining both techniques delivers complementary information of the test sample. We present the design, construction, and calibration of a novel bimodal spectroscopy system featuring both Raman and infrared measurements simultaneously on the same sample without mutual interference. The optomechanical design provides a modular flexible system for solid and liquid samples and different configurations for Raman. As a novel feature, the Raman module can be operated off-axis for optical sectioning. The calibrated system demonstrates high sensitivity, precision, and resolution for simultaneous operation of both techniques and shows excellent calibration curves with coefficients of determination greater than 0.96. We demonstrate the ability to simultaneously measure Raman and infrared spectra of complex biological material using bovine serum albumin. The performance competes with commercial systems; moreover, it presents the additional advantage of simultaneously operating Raman and infrared techniques. To the best of our knowledge, it is the first demonstration of a combined Raman-infrared system that can analyze the same sample volume and obtain optically sectioned Raman signals. Additionally, quantitative comparison of confocality of backscattering micro-Raman and off-axis Raman was performed for the first time.

Identification of Hypoxia-Ischemia by Chemometrics Considering Systemic Changes of Physiology.

Perinatal asphyxia represents a major medical disorder and is related to around a fourth of all neonatal deaths worldwide. Specific thresholds for lactate or pH levels define the gold standard for detecting hypoxic-ischemic events as physiological abnormalities. In contrast to current gold standard, we analyze the systemic picture, represented by the whole set of biochemical parameters from blood gas analysis, by multiparametric machine learning algorithms. In a swine model with 22 objects, we investigate the impact of neonatal hypoxic-ischemic encephalopathy on 18 individual physiological parameters. In a first approach, we study the statistical significance of individual parameters by univariate analysis methods. In a second approach, we take the most relevant parameters as input for the development of predictive models by different hybrid and non-hybrid classification algorithms. The predictive power of our multiparametric models outperforms by far the limited performance of pH and lactate as reliable indicators, despite strong correlation with hypoxic-ischemic events. We have been able to detect hypoxic-ischemic events even one hour after the episode, with accuracies close to 100% in contrast to pH or lactate-based diagnosis with 62% and 78%, respectively. By all machine learning algorithms, lactate is recognized as the main contributor due to its longer-term evidence of hypoxia-ischemia episodes. However, substantial improvement of the diagnosis is achieved by predictions based on a systemic picture of different physiological parameters. Our results prove the potential applicability of our method as a support tool for decision-making that will allow obstetricians to identify hypoxic-ischemic episodes more accurately during labor.

Mechanically Tunable Lattice-Plasmon Resonances by Templated Self-Assembled Superlattices for Multi-Wavelength Surface-Enhanced Raman Spectroscopy.

Lattice plasmons, i.e., diffractively coupled localized surface plasmon resonances, occur in long-range ordered plasmonic nanostructures such as 1D and 2D periodic lattices. Such far-field coupled resonances can be employed for ultrasensitive surface-enhanced Raman spectroscopy (SERS), provided they are spectrally matched to the excitation wavelength. The spectral positions of lattice plasmon modes critically depend on the lattice period and uniformity, owing to their pronounced sensitivity to structural disorder. We report the fabrication of superlattices by templated self-assembly of gold nanoparticles on a flexible support, with tunable lattice-plasmon resonances by means of macroscopic strain. We demonstrate that the highest SERS performance is achieved by matching the lattice plasmon mode to the excitation wavelength, by post-assembly fine-tuning of long-range structural parameters. Both asymmetric and symmetric lattice deformations can be used to adapt a single lattice structure to both red-shifted and blue-shifted excitation lines, as exemplified by lattice expansion and contraction, respectively. This proof-of-principle study represents a basis for alternative designs of adaptive functional nanostructures with mechanically tunable lattice resonances using strain as a macroscopic control parameter.

The influence of the cation structure on the basicity-related polarity of ionic liquids.

UV/Vis absorption data of (E)-4-(2-[5-{4,4,5,5-tetramethyl-1,3,2-dioxaborolane-2-yl}thiene-2-yl]vinyl)-2-(dicyano-methylene)-3-cyano-5,5-dimethyl-2,5-dihydrofuran (ThTCF) as a solvatochromic probe is applied to examine the anion coordination strength (e.g. of N(CN)2, BF4, PF6, N(Tf)2, CF3COO) as a function of the cation structure of ionic liquids. Several 1-n-alky-3-methylimidazolium- and tetraalkylammonium CH3-NR3+-based ILs with different n-alkyl chain lengths (R = -C4H9, -C6H11, -C8H17, -C10H21) are considered. UV/Vis absorption data of ThTCF show subtle correlations with hydrogen bond accepting (HBA) ability-related measurands such as Kamlet-Taft ß, Freire's EHB, and Laurence ß1 parameter as a function of anion and cation structure. The different influence of the n-alkyl chain length of imidazolium- and tetraalkylammonium-based ILs on the dipolarity and HBA strength is confirmed by comparison with the 14N isotropic hyperfine coupling constants (Aiso) of a positively (CATI) and negatively charged spin probe (TSKCr) of TEMPO-type [(2,2,6,6-tetramethylpiperidin-1-yl)oxyl] and quantum chemically derived dipoles of the cations. The Aiso values correlate with the absorption energy of ThTCF and EHB, but in different ways depending on the anion or charge of the spin probe. In a final discussion of the ß, EHB, and ß1 scales in relation to ThTCF, the importance of the molar concentration N of ionic liquids for the physical significance of the respective parameters is discussed.