Simultaneous Inside and Outside Functionalization of Single-Walled Carbon Nanotubes

Functionalizing single-walled carbon nanotubes (SWCNTs) in a robust way that does not affect the sp2 carbon framework is a considerable research challenge. Here we describe how triiodide salts of positively charged macrocycles can be used not only to functionalize SWCNTs from the outside, but simultaneously from the inside. We employed disulfide exchange in aqueous solvent to maximize the solvophobic effect and therefore achieve a high degree of macrocycle immobilization. Characterization by Raman spectroscopy, EDX-STEM and HR-TEM clearly showed that serendipitously this wet-chemical functionalization procedure also led to the encapsulation of polyiodide chains inside the nanotubes. The resulting three-shell composite materials are redox-active and experience an intriguing interplay of electrostatic, solvophobic and mechanical effects that could be of interest for applications in energy storage.

Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry

Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.

A Novel Non-Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Peptide-Decorated Microneedles for the Detection of Microplastics

The escalating utilization of plastics in daily life has resulted in pervasive environmental pollution and consequent health hazards. The challenge of detecting and capturing microplastics, which are imperceptible to the naked eye, is exacerbated by their diminutive size, hydrophobic surface properties, and capacity to absorb organic compounds. This study focuses on the application of peptides, constituted of specific amino acid sequences, and microneedles for the rapid and selective identification of microplastics. Peptides, due to their smaller size and greater environmental stability compared with antibodies, emerge as a potent solution to overcome the limitations inherent in existing detection methodologies. To immobilize peptides onto microneedles, this study employed microneedles embedded with gold nanorods, augmenting them with sulfhydryl (SH) groups at the peptides' termini. The sensor developed through this methodology exhibited efficient peptide binding to the microneedle tips, thereby facilitating the capture of microplastics. Raman spectroscopy was employed for the detection of microplastics, with the results demonstrating successful attachment to the microneedles. This novel approach not only facilitates localized analysis but also presents a viable strategy for the detection of microplastics across diverse environmental settings.

Spin-orbit exciton-induced phonon chirality in a quantum magnet

The interplay of charge, spin, lattice, and orbital degrees of freedom in correlated materials often leads to rich and exotic properties. Recent studies have brought new perspectives to bosonic collective excitations in correlated materials. For example, inelastic neutron scattering experiments revealed non-trivial band topology for magnons and spin-orbit excitons (SOEs) in a quantum magnet CoTiO3 (CTO). Here, we report phonon properties resulting from a combination of strong spin-orbit coupling, large crystal field splitting, and trigonal distortion in CTO. Specifically, the interaction between SOEs and phonons endows chirality to two [Formula: see text] phonon modes and leads to large phonon magnetic moments observed in magneto-Raman spectra. The remarkably strong magneto-phononic effect originates from the hybridization of SOEs and phonons due to their close energy proximity. While chiral phonons have been associated with electronic topology in some materials, our work suggests opportunities may arise by exploring chiral phonons coupled to topological bosons.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in Tetramorium caespitum

Antibiotic resistance genes (ARGs) are ancient but have become a modern critical threat to health. Gut microbiota, a dynamic reservoir for ARGs, transfer resistance between individuals. Surveillance of the antibiotic resistome in the gut during different host growth phases is critical to understanding the dynamics of the resistome in this ecosystem. Herein, we disentangled the ARG profiles and the dynamic mechanism of ARGs in the egg and adult phases of Tetramorium caespitum. Experimental results showed a remarkable difference in both gut microbiota and gut resistome with the development of T. caespitum. Meta-based metagenomic results of gut microbiota indicated the generalizability of gut antibiotic resistome dynamics during host development. By using Raman spectroscopy and metabolomics, the metabolic phenotype and metabolites indicated that the biotic phase significantly changed lipid metabolism as T. caespitum aged. Lipid metabolites were demonstrated as the main factor driving the enrichment of ARGs in T. caespitum. Cuminaldehyde, the antibacterial lipid metabolite that displayed a remarkable increase in the adult phase, was demonstrated to strongly induce ARG abundance. Our findings show that the gut resistome is host developmental stage-dependent and likely modulated by metabolites, offering novel insights into possible steps to reduce ARG dissemination in the soil food chain.

A Study of High-Frequency Noise for Microplastics Classification Using Raman Spectroscopy and Machine Learning

Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.

The effects of land use types on microplastics in river water: A case study on the mainstream of the Wei River, China

Microplastics are widely found in rivers and their sediments, which will cause harm to the water ecological environment. The Wei River is a first-class tributary of the Yellow River, the fifth largest river in the world, and has vulnerable ecological environment and most sediment in the world. However, understanding how anthropogenic activities and environmental factors affect the microplastics distribution in this river is not clear. Based on this, the spatiotemporal distribution of microplastics in the Wei River were investigated. The abundance of microplastics ranged from 1033 to 8333 items/m3 and from 120 to 840 items/kg in the water and in the sediment, respectively. Fibers and fragments were the main shapes of Wei River, microplastics less than 500 μm were the main sizes, and black and white/transparent were the main colors. In Wei River, the abundance of microplastics in urban areas was higher than that in agricultural areas and mountainous areas. Furthermore, the correlation analysis revealed that microplastic abundance in the water was related to anthropogenic activities (population density, per capita GDP and distance) and environmental factors (water temperature, NH3-N, ORP), while in the sediments was correlated with anthropogenic activities (per capita GDP) and environmental factors (water temperature and NH3-N). This study reveals new patterns in microplastic pollution in the Wei River, underscoring the need for targeted environmental strategies. Our findings provide novel insights into the characteristics and distribution of microplastics, significantly adding to the current understanding of riverine microplastic pollution.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25 (56 ). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Determining by Raman spectroscopy the average thickness and N-layer-specific surface coverages of MoS2 thin films with domains much smaller than the laser spot size

Raman spectroscopy is a widely used technique to characterize nanomaterials because of its convenience, non-destructiveness, and sensitivity to materials change. The primary purpose of this work is to determine via Raman spectroscopy the average thickness of MoS2 thin films synthesized by direct liquid injection pulsed-pressure chemical vapor deposition (DLI-PP-CVD). Such samples are constituted of nanoflakes (with a lateral size of typically 50 nm, i.e., well below the laser spot size), with possibly a distribution of thicknesses and twist angles between stacked layers. As an essential preliminary, we first reassess the applicability of different Raman criteria to determine the thicknesses (or layer number, N) of MoS2 flakes from measurements performed on reference samples, namely well-characterized mechanically exfoliated or standard chemical vapor deposition MoS2 large flakes deposited on 90 ± 6 nm SiO2 on Si substrates. Then, we discuss the applicability of the same criteria for significantly different DLI-PP-CVD MoS2 samples with average thicknesses ranging from sub-monolayer up to three layers. Finally, an original procedure based on the measurement of the intensity of the layer breathing modes is proposed to evaluate the surface coverage for each N (i.e., the ratio between the surface covered by exactly N layers and the total surface) in DLI-PP-CVD MoS2 samples.

Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion

Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples' classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Cleaning of direct compression continuous manufacturing equipment through displacement of API residues by excipients

This feasibility study evaluates a cleaning process designed to avoid the use of detergents and reduce operator exposure to the active pharmaceutical ingredient (API). The continuous manufacturing equipment was cleaned using excipients to displace ibuprofen residues from the system. The cleaning process was performed using 3.0 kg of Prosolv® and 3.0 kg of Tablettose® 70. The impact of different volumetric feed rates of the cleaning excipient was assessed. The displacement of API and blend residues was evaluated with in-line near infrared (NIR) spectroscopy. Principal component analysis (PCA) was performed to evaluate the cleaning progress as the Prosolv® flowed through the feeder, mixer and stream sampler. In-place Raman spectra were acquired from the material sticking to detect the ibuprofen residues. The study showed that Prosolv® and Tablettose® can remove ibuprofen residues effectively from the hopper, feeder screw, mixer paddles, shaft and stream sampler. The Process Analytical Technology (PAT) system can be utilized to detect API displacement during the cleaning process. However, dismantling and manual cleaning was required to remove material sticking at the surfaces adjacent to the rotating feeder screws and mixer paddles.

Decoding PFAS contamination via Raman spectroscopy: A combined DFT and machine learning investigation

In this study, density function theory (DFT) is employed to compute Raman spectra of 40 important Perfluoroalkyl substances (PFASs) as listed in Draft Method 1633 by U.S. Environmental Protection Agent. A systematic comparison of their spectral features is conducted, and Raman peaks and vibrational modes are identified. The Raman spectral regions for the main chemical bonds (such as C-C, CF2 & CF3, O-H) and main functional groups (such as -COOH, -SO3H, -C2H4SO3H, and -SO2NH2) are identified and compared. The impacts of branching location in isomer, molecular chain length, and functional groups on the Raman spectra are analyzed. Particularly, the isomers of PFOA alter the peak locations slightly in wavenumber regions of 200 - 800 and 1000 - 1400 cm-1, while for PFOS, spectral features in the 230 - 360, 470 - 680, and 1030 - 1290 cm-1 regions exhibit significant difference. The carbon chain length can significantly increase the number of Raman peaks, while different functional groups give significantly different peak locations. To facilitate differentiation, a spectral database is constructed by introducing controlled noise into the DFT-computed Raman spectra. Subsequently, two chemometric techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), are applied to effectively distinguish among these spectra, both for 40 PFAS compounds and the isomers. The findings demonstrate the promising potential of combining Raman spectroscopy with advanced spectral analysis methods to discriminate between distinct PFAS compounds, holding significant implications for improved PFAS detection and characterization methodologies.

Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer

Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.

Determination of the pressure and composition of wet gas fluid inclusions: An in situ Raman spectroscopic approach

Carbonaceous fluid within mineral-hosted inclusions provides important information for carbon cycle in deep Earth. In addition to CH4 and CO2, heavy hydrocarbons (e.g., C2H6 and C3H8) are frequently observed in carbonaceous fluid inclusions (i.e, wet gas inclusions with C1/∑Ci < 0.95). However, determination of the composition of such complex volatiles is difficult based on traditional microthermometric measurements. Here we carried out experimental calibrations on Raman spectroscopic measurements of the pressure (P) and composition of the CH4 ± C2H6 ± C3H8 ± H2S system at room temperature and 0.1-130 MPa. We confirmed that the C-H symmetric stretching vibration band of CH4 [ν1(CH4), ∼2917 cm-1] shifted to lower wavenumber with rising pressure, thus the P-ν1(CH4) relationship could be applied to calculate the pressure of wet gas. It should be noted that the presence of C2+ and/or H2S will shift the [ν1(CH4)] to lower wavenumber at constant pressure (with the order of C3H8 ≥ H2S > C2H6). Obviously, the P-ν1(CH4) relationship derived from pure CH4 system could not be simply applied to wet gas inclusion, otherwise the pressure would be overestimated. To avoid the overlap of the C-H vibrations of CH4, C2H6 and C3H8, the peak areas and peak heights of the overtone vibration of CH4 [2ν4(CH4), ∼2580 cm-1], C-C symmetric stretching vibrations of C2H6 [ν3(C2H6), ∼995 cm-1] and C3H8 [ν8(C3H8), ∼868 cm-1], and S-H symmetric stretching vibration of H2S [ν1(H2S), ∼2612 cm-1] were fitted using Gaussian + Lorentz functions. The obtained peak areas and peak heights were then used to calculate the Raman quantification factors (F factor and G factor, respectively) of C2H6, C3H8 and H2S relative to CH4, respectively. Both the F factor and G factor increased with rising pressure, whereas the FC2H6, FC3H8 and GH2S kept nearly constant at ∼5.69, 6.39 and 153.8, respectively in high pressure gas mixtures (e.g., >30 MPa). Therefore, for inclusions with higher internal pressure, the molar ratio of CH4, C2H6, C3H8 and H2S could be determined by the aforementioned F and G factors. This method was applied to the calcite-hosted single-phase gas inclusions in the Upper Permian Changxing Formation carbonate reservoir from the eastern Sichuan Basin (South China). Our results indicated that the trapping pressure would be obviously overestimated if the presence of heavy hydrocarbons was not taken into account.

LEGO Blocks as "Standard" Samples for Evaluation of Fluorescence Avoidance and Mitigation in Raman Spectroscopy

Fluorescence interference in Raman spectroscopy is a well-known problem and is especially significant in portable instruments where the availability of a variety of exciting wavelengths is unlikely. Several fluorescence avoidance and mitigation schemes are described in the literature, and implemented by Raman spectrometer manufacturers, but there is no standard method for evaluating the accuracy and repeatability of these schemes. Some test samples shown in instrument descriptions, such as "dark rum" and "sesame seed oil" are not reproducible. Therefore, we propose a set of colored LEGO blocks as "standard" samples for this purpose; they have the attractive properties of being very low cost, rugged, non-toxic, easy to transport and store, and appear to be manufactured using a standard process. This paper shows the Raman spectra of a set of these blocks at different excitation wavelengths, acquired on laboratory instruments, along with their visible-near-infrared spectra. The goal is to qualitatively understand the origins of the observed fluorescence and lay the groundwork for exploring the effectiveness of methods currently implemented on handheld Raman instruments.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

The role of glucose and fructose on lipid droplet metabolism in human normal bronchial and cancer lung cells by Raman spectroscopy

Fructose is one of the most important monosaccharides in the human diet that the human body needs for proper metabolism. This paper presents an approach to study biochemical changes caused by sugars in human normal bronchial cells (BEpiC) and human cancer lung cells (A549) by Raman spectroscopy and Raman imaging. Results after supplementation of human bronchial and lung cells with fructose are also discussed and compared with results obtained for pure human bronchial and lung cells. Based on Raman techniques we have proved that peaks at 750 cm-1, 1126 cm-1, 1444 cm-1, 1584 cm-1 and 2845 cm-1 can be treated as biomarkers to monitor fructose changes in cells. Results for fructose have been compared with results for glucose. Raman analysis of the bands at 750 cm-1, 1126 cm-1, 1584 cm-1 and 2845 cm-1 for pure BEpiC and A549 cells and BEpiC and A549 after supplementation with fructose and glucose are higher after supplementation with fructose in comparison to glucose. The obtained results shed light on the uninvestigated influence of glucose and fructose on lipid droplet metabolism by Raman spectroscopy methods.

Irreversible lipid phase transition detected in a porcine oocyte at chilling

Under physiological conditions, the membranes and lipid droplets of germ cells are in a conformationally disordered phase. Typically, during cooling, lipids undergo the transition to ordered phases and, upon heating, melt into a disordered phase. In this communication, we report the lipid phase transition in lipid droplets observed in porcine oocytes. Upon cooling, a sharp lipid phase transition from conformationally disordered to ordered state was detected within the temperature range between 20 and 15 °C. Subsequent heating to 45 °C does not return lipids to their original phase state. To the best of our knowledge, this is the first observation of an irreversible phase transition in lipid droplets of biological cells with native lipid composition.

Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review

BACKGROUND

People living with diabetes need constant glucose monitoring to avoid health complications. However, they do not monitor their glucose levels as often as recommended, probably because glucose measurement devices can be painful, costly, need testing strips or sensors, require lancing the finger or inserting a sensor with risk of infection, and can be inaccurate or have failures. Therefore, developing new alternatives for noninvasive glucose measurements that overcome these disadvantages is necessary, being Raman spectroscopy (RS) a solution.

OBJECTIVE

This review aims to provide an overview of the current glucose-monitoring technologies and the uses and advantages of RS to improve noninvasive transcutaneously glucose-monitoring devices.

RESULTS

The skin has been used to assess glucose levels noninvasively because it is an accessible tissue where glucose can be measured in the interstitial fluid (ISF) in the epidermis (especially in the stratum corneum). The most selected skin sites to apply RS for noninvasive glucose measurements were the nailfold, finger, and forearm because, in these sites, the penetration depth of the excitation light can reach the stratum corneum (10-20 µm) and the ISF. Studies found that RS is a good optical technique to measure glucose noninvasively by comparing glucose levels obtained by RS with those from invasive methods such as glucose meters with testing strips during an oral glucose tolerance test (OGTT).

CONCLUSIONS

New alternatives for noninvasive glucose measurements that overcome the disadvantages of current devices is necessary, and RS is a possible solution. However, more research is needed to evaluate the stability, accuracy, costs, and acceptance.

Sediment-associated microplastics in Chilika lake, India: Highlighting their prevalence, polymer types, possible sources, and ecological risks

The primary objective of this research was to assess microplastics (MPs) in the sediments of Chilika lake. MPs were extracted from 22 sediment samples using the density separation method combined with vacuum pump filtration. A stereo-zoom microscope and Raman spectroscopy were employed to identify the sediment-associated MPs. The total MPs collected from all 22 sites was 440 ± 3.53 particles kg-1 wet sediments, with sizes ranging between 50 and 500 μm. In terms of morphology, fibers and fragments emerged as the dominant MP types, with counts of 210 ± 1.66 and 175 ± 1.76 particles kg-1 wet sediments, respectively. Raman spectroscopy verified the presence of various MP polymers in the sediments, predominantly HDPE (37 %), followed by PS (20 %), PET (18 %), PA (11 %), PP (7 %), and PC (7 %). A notable color variation was observed in MPs; black being the most prevalent (38.8 %), succeeded by blue (19.5 %), green (11.8 %), white (11.5 %), red (10.6 %), and transparent (7.5 %). ANOVA results indicated significant (p > 0.05) variations in MP abundance across the 22 sampling locations. However, principal component analysis (PCA) and multiple regression analysis indicated that water quality parameters did not significantly influence MP abundance, yet it was found that MP retention was higher in fine-grained sediments like clay and silt. The leading sources of MPs in Chilika lake were found to be aquafarming, trailed by river and sewage discharges, fishing activities, antifouling coatings and tourism. Additionally, the pollution load index (PLI) was employed to gauge the ecological risks, categorizing the lake under risk category 1, which implies a minimal level of MPs pollution. This research aims to serve as an early warning system for MPs pollution in productive brackish water habitats globally, including Chilika lake, guiding policymakers towards appropriate management strategies and preventive measures.

Bleach Cleaning of Commercially Available Gold Nanopillar Arrays for Surface-Enhanced Raman Spectroscopy (SERS)

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive technique that can assist in trace analysis for biomedical, diagnostic, and environmental applications. However, a major limitation of SERS is surface contamination of the substrates used, which can complicate the spectral reproducibility, limits of detection, and detection of unknown analytes. This is especially prevalent with commercially available substrates as shipping under a controlled and clean environment is difficult. Here we report a method using dilute bleach solutions to remove surface contamination from commercially available substrates consisting of gold-coated nanopillar arrays that maintains functionality. The results show that this method can be used to remove background signals associated with typical surface contamination in commercially available substrates as well as remove thiolated self-assembled monolayers (SAMs). Results indicate the bleach oxidizes the surface contaminants, which can then be easily washed away. Although the metallic surface also becomes oxidized in this process, the surface can be reduced without loss of SERS activity. The SERS intensity of SAMs improved following bleach treatment across all concentrations studied.

Visualization and characterisation of microplastics in aquatic environment using a home-built micro-Raman spectroscopic set up

Microplastics (MP) which are tiny plastic particles of sizes range from 1 μm (μm) to 5 mm (mm), have become a growing cause of concern due to their widespread presence in the environment and their potential impacts on ecosystems and human health. Marine organisms have the potential to consume microplastics, which could lead to physical injuries, blockages, or the transfer of harmful substances up the food chain. Humans may indirectly consume microplastics through contaminated seafood and water, although the complete scope of health risks is currently under investigation. An essential step in gaining a comprehensive understanding of microplastic pollution in waterbodies is the identification of microplastics, which is also crucial for further development of effective environmental regulations to address its adverse impacts. Majority of the researchers are accomplishing it globally using commercial platforms based on Raman spectroscopy. However, the development of indigenous Raman systems, which can enable microplastic identification, particularly in developing nations, is the need of the hour due to the outrageous cost of commercial platforms. In the current study, a custom-designed micro-Raman spectroscopy system was developed to detect and characterize microplastics from waterbodies. The developed system enabled visualization, size measurement and characterization of microplastics. Experimental parameters were fine-tuned, and a standardized Raman database was established for each type of plastic. This system exhibited high resolution which was capable of analysing microparticles of size up to 5 μm. Principal component analysis was carried out on the experimental Raman data, demonstrating good classification amongst different kinds of plastics. The performance of the developed system in analysing real samples was evaluated through experiments conducted on water samples obtained from the shore of Malpe Beach in Udupi district. The results revealed the presence of polyethylene and polyethylene terephthalate in the samples, along with the detection of pigments like copper phthalocyanine and indigo blue.

Drug content determination of low-dosed hot-melt extruded filaments using Raman spectroscopy

The aim of this study was to evaluate the suitability of a non-disruptive Raman spectroscopic method to quantify drug concentrations below 5 w% within a polymer matrix produced by hot-melt extrusion (HME). For calibration, praziquantel (PZQ)-polyvinylpyrrolidone-vinylacetat-copolymer (PVP-VA) mixtures were extruded. By focusing the laser light of the Raman probe to a diameter of 1 mm and implementing a self-constructed filament holder, the signal-to-noise (S/N) ratio could be reduced considerably. The obtained Raman spectra show quite high fluorescence, which is likely to be caused by dissolved pharmaceutical active ingredient (API) in the polymer matrix. For content determination, HPLC analysis was conducted as a reference method using the same filament segments. A partial least squares (PLS) model, regressing the PZQ concentrations from HPLC method analysis versus the off-line collected Raman spectra, was developed. The linear correlation for a suitable extrusion run for the production of low-dosed filaments (extrusion 1, two kneading zones) is acceptable (R2 = 0.9915) while the correlation for a extrusion set-up with low miscibility (extrusion 2; without kneading zone) is unacceptable (R2 = 0.5349). The predictive performance of the calibration model from extrusion 1 is rated by the root mean square error of estimation (RMSEE), which was 0.08%. This calibration can now be used to validate the content of low-dosed filaments during HME.

Quantitative prediction of rice starch digestibility using Raman spectroscopy and multivariate calibration analysis

Digestibility is an important characteristic of rice starch. It is affected by the growing environment, such as temperature and soil, so that even in the same genetic cultivar the digestibility of each product will be different. Here, we predicted rice starch digestibility by Raman scattering spectroscopy. A partial least squares (PLS) regression analysis was performed between biochemically quantified digestibility index values and Raman scattering spectra of purified starch from rice samples of different cultivars and growing conditions. The prediction model obtained by analyzing the individual cultivars was able to predict digestibility with a high accuracy, with an R2 of 0.95 and RMSEP of 0.43, whereas a mixture of all cultivars resulted in more than two times worse accuracy. Our finding suggests that the molecular structures affecting digestibility fluctuate depending on the growing environment while maintaining a unique balance regulated by cultivar-specific starch synthesis mechanisms.

Raman spectroscopy-based microfluidic platforms: A promising tool for detection of foodborne pathogens in food products

Rapid and sensitive detection of foodborne pathogens in food products is paramount for ensuring food safety and public health. In the ongoing effort to tackle this issue, detection methods are continually researched and upgraded to achieve rapidity, sensitivity, portability, and cost-effectiveness. This review addresses the critical need for improved technique by focusing on Raman spectroscopy-based microfluidic platforms, which have shown potential in revolutionizing the field of foodborne pathogen analysis offering point-of-care diagnosis and multiplex detection. The key problem lies in the persistent threat of compromised food quality and public health due to inadequate pathogen detection. The review elucidates the various trapping strategies employed in a microfluidic platform, including optical trapping, electrical trapping, mechanical trapping, and acoustic trapping for the capture of microbial cells. Subsequently, the review delves into the key aspects of the application of microbial detection in food products, highlighting recent advances and challenges in the field. The integrated technique allows point-of-care application assessment, which is an attractive quality for in-line and real-time detection of foodborne pathogens. However, the application of the technique in food products is limited and requires further research to combat the complexity of the food matrix, reduced costs of production, and ensure real-time use for diverse pathogens. Ultimately, this review aims to propel advancements in microbial detection, thus promoting enhanced food safety through state-of-the-art technologies.

Dual-Mode Nanoprobes Based on Lanthanide Doped Fluoride Nanoparticles Functionalized by Aryl Diazonium Salts for Fluorescence and SERS Bioimaging

The design of dual-mode fluorescence and Raman tags stimulates a growing interest in biomedical imaging and sensing applications as they offer the possibility to synergistically combine the versatility and velocity of fluorescence imaging with the specificity of Raman spectroscopy. Although lanthanide-doped fluoride nanoparticles (NPs) are among the most studied fluorescent nanoprobes, their use for the development of bimodal fluorescent-Raman probes has never been reported yet, to the best of the authors knowledge, probably due to the difficulty to functionalize them with Raman reporter groups. This gap is filled herein by proposing a fast and straightforward approach based on aryl diazonium salt chemistry to functionalize Eu3+ or Tb3+ doped CaF2 and LaF3 NPs by Raman scatters. The resulting surface-enhanced Raman spectroscopy (SERS)-encoded lanthanide-doped fluoride NPs retain their fluorescence labeling capacity and display efficient SERS activity for cell bioimaging. The potential of this new generation of bimodal nanoprobes is assessed through cell viability assays and intracellular fluorescence and Raman imaging, opening up unprecedented opportunities for biomedical applications.

Uniform, Strain-Free, Large-Scale Graphene and h-BN Monolayers Enabled by Hydrogel Substrates

Translation of the unique properties of 2D monolayers from non-scalable micron-sized samples to macroscopic scale is a longstanding challenge obstructed by the substrate-induced strains, interface nonuniformities, and sample-to-sample variations inherent to the scalable fabrication methods. So far, the most successful strategies to reduce strain in graphene are the reduction of the interface roughness and lattice mismatch by using hexagonal boron nitride (h-BN), with the drawback of limited uniformity and applicability to other 2D monolayers, and liquid water, which is not compatible with electronic devices. This work demonstrates a new class of substrates based on hydrogels that overcome these limitations and excel h-BN and water substrates at strain relaxation enabling superiorly uniform and reproducible centimeter-sized sheets of unstrained monolayers. The ultimate strain relaxation and uniformity are rationalized by the extreme structural adaptability of the hydrogel surface owing to its high liquid content and low Young's modulus, and are universal to all 2D materials irrespective of their crystalline structure. Such platforms can be integrated into field effect transistors and demonstrate enhanced charge carrier mobilities in graphene. These results present a universal strategy for attaining uniform and strain-free sheets of 2D materials and underline the opportunities enabled by interfacing them with soft matter.

Stimulated Raman Histology Interpretation by Artificial Intelligence Provides Near-Real-Time Pathologic Feedback for Unprocessed Prostate Biopsies

PURPOSE

Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology.

MATERIALS AND METHODS

Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set.

RESULTS

Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%.

CONCLUSIONS

Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

Analysis of friction ridge evidence for trace amounts of paracetamol in various pharmaceutical industries by Raman spectroscopy

The detection of potentially harmful substances presents a multifaceted challenge. On one hand, it can directly save lives, on the other, it can significantly aid and enhance police work, thereby increasing the effectiveness of investigations. The research conducted in this study primarily aims to identify paracetamol in fingerprints, considering situations involving direct contact of a person with paracetamol either chronically or in a single dose. The identification procedure presented, utilizing Raman spectroscopy, aims to rapidly detect the xenobiotic following ingestion by an individual, which involves touching the tablet with their fingers-this can be termed as touch evidence in forensic science investigations. Additionally, the authors focus on assessing the impact of additives present in drugs containing paracetamol as the main active ingredient. The screening results obtained will enable us to analyze the composition of drugs in terms of potentially toxic substances, and their influence on the physicochemical activity of the active substance. We successfully identified the paracetamol molecule using a noninvasive forensic trace detection method. Samples in the form of common drugs containing 500 mg of paracetamol were studied. Throughout the study, comprehensive validation of the method was ensured through the utilization of a statistical model, which excluded sensitivity to the presence of other substances, whether additives or from the external environment. The proposed approach to trace the content of substances in fingerprint using Raman scattering analysis provides a useful starting point to enhance current analytical methods not only in forensic science but also in toxicology.

Label-free analysis of the β-hydroxybutyricacid drug on mitochondrial redox states repairment in type 2 diabetic mice by resonance raman scattering

BACKGROUND

Mitochondrial redox imbalance underlies the pathophysiology of type2 diabetes mellitus (T2DM), and is closely related to tissue damage and dysfunction. Studies have shown the beneficial effects of dietary strategies that elevate β-hydroxybutyrate (BHB) levels in alleviating T2DM. Nevertheless, the role of BHB has not been clearly elucidated.

METHODS

We performed a spectral study to visualize the preventive effects of BHB on blood and multiorgan mitochondrial redox imbalance in T2DM mice via using label-free resonance Raman spectroscopy (RRS), and further explored the impact of BHB therapy on the pathology of T2DM mice by histological and biochemical analyses.

FINDINGS

Our data revealed that RRS-based mitochondrial redox states assay enabled clear and reliable identification of the improvement of mitochondrial redox imbalance by BHB, evidenced by the reduction of Raman peak intensity at 750 cm-1, 1128 cm-1 and 1585 cm-1 in blood, tissue as well as purified mitochondria of db/db mice and the increase of tissue mitochondrial succinic dehydrogenase (SDH) staining after BHB treatment. Exogenous supplementation of BHB was also found to attenuate T2DM pathology related to mitochondrial redox states, involving organ injury, blood glucose control, insulin resistance and systemic inflammation.

INTERPRETATION

Our findings provide strong evidence for BHB as a potential therapeutic strategy targeting mitochondria for T2DM.

A novel method for detection of pancreatic Ductal Adenocarcinoma using explainable machine learning

BACKGROUND AND OBJECTIVE

Pancreatic Ductal Adenocarcinoma (PDAC) is a form of pancreatic cancer that is one of the primary causes of cancer-related deaths globally, with less than 10 % of the five years survival rate. The prognosis of pancreatic cancer has remained poor in the last four decades, mainly due to the lack of early diagnostic mechanisms. This study proposes a novel method for detecting PDAC using explainable and supervised machine learning from Raman spectroscopic signals.

METHODS

An insightful feature set consisting of statistical, peak, and extended empirical mode decomposition features is selected using the support vector machine recursive feature elimination method integrated with a correlation bias reduction. Explicable features successfully identified mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumor suppressor protein53 (TP53) in the fingerprint region for the first time in the literature. PDAC and normal pancreas are classified using K-nearest neighbor, linear discriminant analysis, and support vector machine classifiers.

RESULTS

This study achieved a classification accuracy of 98.5% using a nonlinear support vector machine. Our proposed method reduced test time by 28.5 % and saved 85.6 % memory utilization, which reduces complexity significantly and is more accurate than the state-of-the-art method. The generalization of the proposed method is assessed by fifteen-fold cross-validation, and its performance is evaluated using accuracy, specificity, sensitivity, and receiver operating characteristic curves.

CONCLUSIONS

In this study, we proposed a method to detect and define the fingerprint region for PDAC using explainable machine learning. This simple, accurate, and efficient method for PDAC detection in mice could be generalized to examine human pancreatic cancer and provide a basis for precise chemotherapy for early cancer treatment.

Use of Time-Domain NMR for 1H T1 Relaxation Measurement and Fitting Analysis in Homogeneity Evaluation of Amorphous Solid Dispersion

This study examined the usefulness of 1H T1 relaxation measurements for evaluating the homogeneity of amorphous solid dispersion (ASD). Indomethacin and polyvinylpyrrolidone were used to prepare two kinds of ASDs. One was inhomogeneous ASD (ASDmelt) prepared by a melt-quenching method, and the other was homogeneous ASD (ASDsolvent) prepared by a solvent evaporation method. The T1 relaxation was measured by the time-domain NMR (TD-NMR) technique using a low-field NMR system. Curve-fitting analysis of T1 relaxation plots was conducted using the Akaike information criterion. This fitting analysis revealed that the T1 relaxation of ASDmelt and ASDsolvent was biphasic and monophasic, respectively. ASDmelt and ASDsolvent were inhomogeneous and homogeneous on a nanometer scale, respectively, considering the spin diffusion of 1H nuclei. These T1 results were consistent with the Raman mapping of ASDs. From the fitting analysis of 1H T1 relaxation, we conclude that TD-NMR is a promising technique for evaluating ASD homogeneity.

Characterization of the mechanical properties of the mouse Achilles tendon enthesis by microindentation. Effects of unloading and subsequent reloading

The fibrocartilaginous tendon enthesis, i.e. the site where a tendon is attached to bone through a fibrocartilaginous tissue, is considered as a functionally graded interface. However, at local scale, a very limited number of studies have characterized micromechanical properties of this transitional tissue. The first goal of this work was to characterize the micromechanical properties of the mineralized part of the healthy Achilles tendon enthesis (ATE) through microindentation testing and to assess the degree of mineralization and of carbonation of mineral crystals by Raman spectroscopy. Since little is known about enthesis biological plasticity, our second objective was to examine the effects of unloading and reloading, using a mouse hindlimb-unloading model, on both the micromechanical properties and the mineral phase of the ATE. Elastic modulus, hardness, degree of mineralization, and degree of carbonation were assessed after 14 days of hindlimb suspension and again after a subsequent 6 days of reloading. The elastic modulus gradually increased along the mineralized part of the ATE from the tidemark to the subchondral bone, with the same trend being found for hardness. Whereas the degree of carbonation did not differ according to zone of measurement, the degree of mineralization increased by >70 % from tidemark to subchondral bone. Thus, the gradient in micromechanical properties is in part explained by a mineralization gradient. A 14-day unloading period did not appear to affect the gradient of micromechanical properties of the ATE, nor the degree of mineralization or carbonation. However, contrary to a short period of unloading, early return to normal mechanical load reduced the micromechanical properties gradient, regardless of carbonate-to-phosphate ratios, likely due to the more homogeneous degree of mineralization. These findings provide valuable data not only for tissue bioengineering, but also for musculoskeletal clinical studies and microgravity studies focusing on long-term space travel by astronauts.

On the Role of a Conserved Tryptophan in the Chromophore Pocket of Cyanobacteriochrome

The cyanobacteriochrome Slr1393 can be photoconverted between a red (Pr) and green absorbing form (Pg). The recently determined crystal structures of both states suggest a major movement of Trp496 from a stacking interaction with ring D of the phycocyanobilin (PCB) chromophore in Pr to a position outside the chromophore pocket in Pg. Here, we investigated the role of this amino acid during photoconversion in solution using engineered protein variants in which Trp496 was substituted by natural and non-natural amino acids. These variants and the native protein were studied by various spectroscopic techniques (UV-vis absorption, fluorescence, IR, NIR and UV resonance Raman) complemented by theoretical approaches. Trp496 is shown to affect the electronic transition of PCB and to be essential for the thermal equilibrium between Pr and an intermediate state O600. However, Trp496 is not required to stabilize the tilted orientation of ring D in Pr, and does not play a role in the secondary structure changes of Slr1393 during the Pr/Pg transition. The present results confirm the re-orientation of Trp496 upon Pr → Pg conversion, but do not provide evidence of a major change in the microenvironment of this residue. Structural models indicate the penetration of water molecules into the chromophore pocket in both Pr and Pg states and thus water-Trp contacts, which can readily account for the subtle spectral changes between Pr and Pg. Thus, we conclude that reorientation of Trp496 during the Pr-to-Pg photoconversion in solution is not associated with a major change in the dielectric environment in the two states.

An automated high inoculation density fed-batch bioreactor, enabled through N-1 perfusion, accommodates clonal diversity and doubles titers

An important consideration for biopharmaceutical processes is the cost of goods (CoGs) of biotherapeutics manufacturing. CoGs can be reduced by dramatically increasing the productivity of the bioreactor process. In this study, we demonstrate that an intensified process which couples a perfused N-1 seed reactor and a fully automated high inoculation density (HID) N stage reactor substantially increases the bioreactor productivity as compared to a low inoculation density (LID) control fed-batch process. A panel of six CHOK1SV GS-KO® CHO cell lines expressing three different monoclonal antibodies was evaluated in this intensified process, achieving an average 85% titer increase and 132% space-time yield (STY) increase was demonstrated when comparing the 12-day HID process to a 15-day LID control process. These productivity increases were enabled by automated nutrient feeding in both the N-1 and N stage bioreactors using in-line process analytical technologies (PAT) and feedback control. The N-1 bioreactor utilized in-line capacitance to automatically feed the bioreactor based on a capacitance-specific perfusion rate (CapSPR). The N-stage bioreactor utilized in-line Raman spectroscopy to estimate real-time concentrations of glucose, phenylalanine, and methionine, which are held to target set points using automatic feed additions. These automated feeding methodologies were shown to be generalizable across six cell lines with diverse feed requirements. We show this new process can accommodate clonal diversity and reproducibly achieve substantial titer uplifts compared to traditional cell culture processes, thereby establishing a baseline technology platform upon which further increases bioreactor productivity and CoGs reduction can be achieved.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid

Macromolecules and their complexes remain interesting topics in various fields, such as targeted drug delivery and tissue regeneration. The complex chemical structure of such substances can be studied with a combination of Raman spectroscopy and machine learning. The complex of whey protein isolate (WPI) and hyaluronic acid (HA) is beneficial in terms of drug delivery. It provides HA properties with the stability obtained from WPI. However, differences between WPI-HA and WPI solutions can be difficult to detect by Raman spectroscopy. Especially when the low HA (0.1, 0.25, 0.5% w/v) and the constant WPI (5% w/v) concentrations are used. Before applying the machine learning techniques, all the collected data were divided into training and test sets in a ratio of 3:1. The performances of two ensemble methods, random forest (RF) and gradient boosting (GB), were evaluated on the Raman data, depending on the type of problem (regression or classification). The impact of noise reduction using principal component analysis (PCA) on the performance of the two machine learning methods was assessed. This procedure allowed us to reduce the number of features while retaining 95% of the explained variance in the data. Another application of these machine learning methods was to identify the WPI Raman bands that changed the most with the addition of HA. Both the RF and GB could provide feature importance data that could be plotted in conjunction with the actual Raman spectra of the samples. The results show that the addition of HA to WPI led to changes mainly around 1003 cm-1 (correspond to ring breath of phenylalanine) and 1400 cm-1, as demonstrated by the regression and classification models. For selected Raman bands, where the feature importance was greater than 1%, a direct evaluation of the effect of the amount of HA on the Raman intensities was performed but was found not to be informative. Thus, applying the RF or GB estimators to the Raman data with feature importance evaluation could detect and highlight small differences in the spectra of substances that arose from changes in the chemical structure; using PCA to filter out noise in the Raman data could improve the performance of both the RF and GB. The demonstrated results will make it possible to analyze changes in chemical bonds during various processes, for example, conjugation, to study complex mixtures of substances, even with small additions of the components of interest.

Synthesis of PEDOT/CNTs Thermoelectric Thin Films with a High Power Factor

In this study, we have improved the power factor of conductive polymer nanocomposites by combining layer-by-layer assembly with electrochemical deposition to produce flexible thermoelectric materials based on PEDOT/carbon nanotubes (CNTs)-films. To produce films based on CNTs and PEDOT, a dual approach has been employed: (i) the layer-by-layer method has been utilized for constructing the CNTs layer and (ii) electrochemical polymerization has been used in the synthesis of the conducting polymer. Moreover, the thermoelectric properties were optimized by controlling the experimental conditions including the number of deposition cycles and electropolymerizing time. The electrical characterization of the samples was carried out by measuring the Seebeck voltage produced under a small temperature difference and by measuring the electrical conductivity using the four-point probe method. The resulting values of the Seebeck coefficient S and σ were used to determine the power factor. The structural and morphological analyses of CNTs/PEDOT samples were carried out using scanning electron microscopy (SEM) and Raman spectroscopy. The best power factor achieved was 131.1 (μWm-1K-2), a competitive value comparable to some inorganic thermoelectric materials. Since the synthesis of the CNT/PEDOT layers is rather simple and the ingredients used are relatively inexpensive and environmentally friendly, the proposed nanocomposites are a very interesting approach as an application for recycling heat waste.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.

Fingerprint Raman spectroscopy for two-dimensional MoS2xSe2(1-x)alloys

Two-dimensional transition metal dichalcogenides (TMDs) and alloys based on them, is a promising platform for creating opto- and nanoelectronic devices. For layered alloys, there is a strong need to theoretically determine the frequencies of vibrational modes and dependences of their energies on the stoichiometric composition. By comparing experimentally measured Raman modes with theoretical predictions, it becomes possible to determine the stoichiometric composition of the manufactured alloys. In this work, we investigated the vibrational properties of monolayer MoS2xSe2(1-x)alloys utilizing DFT method and confirmed them experimentally by Raman spectroscopy. The dependence of A1' and E12g"dactylographic" modes on the stoichiometric composition of alloys has been calculated. When in pure MoSe2 structure Se atoms are substituted by S atoms, the in-plane E12g(S-Mo), E12g(Se-Mo) and out-plane A1' (S-Mo) modes shifted linearly in x to higher frequencies, while the out-plane A1' (Se-Mo) mode did the same in nonlinear way. We also identified the E12g(Se-Mo-S) mode, which specific for the MoS2xSe2(1-x)alloys and does not appear in pure two-component TMDs.

Effects of Hydrogenation on the Corrosion Behavior of Zircaloy-4

Hydrogen plays an important role in the corrosion of zirconium alloys, and the degree of influence highly depends on the alloy composition and conditions. In this work, the effects of hydrogenation on the corrosion behavior of Zircaloy-4 in water containing 3.5 ppm Li + 1000 ppm B at 360 °C/18.6 MPa were investigated. The results revealed that hydrogenation can shorten the corrosion transition time and increase the corrosion rates of Zircaloy-4. The higher corrosion rates can be ascribed to the larger stress in the oxide film of hydrogenated samples, which can accelerate the evolution of the microstructure of the oxide film. In addition, we also found that hydrogenation has little effect on the t-ZrO2 content in the oxide film and there is no direct correspondence between the t-ZrO2 content and the corrosion resistance of the Zircaloy-4.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

Low temperature Raman spectroscopic study of anharmonic and spin-phonon coupled quasi-two dimensional rare earth based francisites

Mineral francisites Cu3Bi(SeO3)2O2Cl are unique compounds with interesting quasi two-dimensional structure along with fascinating magnetic properties. The magnetic properties can be fine-tuned when non-magnetic Bi is replaced by a suitable rare earth (RE) metal. It is because of the inclusion of additional magnetic sub-centre RE apart from Cu. Temperature dependent Raman spectroscopy measurements in RE based francisites [Cu3RE(SeO3)2O2Cl, shortly RECufr] were performed in the range of 11 K-295 K. Among the three studied RECufr (LaCufr, NdCufr, and DyCufr) compounds, the properties of phonon vibration vary from moderate (in DyCufr) to weak (in LaCufr) spin phonon coupled and the absence of spin phonon coupling (SPC) (i.e. strictly anharmonic in nature) was observed in NdCufr and the reason for this observation has been provided. More specifically, two Raman-active phonons soften below the antiferromagnetic ordering temperature ofTN≈ 39 K in DyCufr compound, indicating the existence of moderate SPC. This trend of phonon vibration is correlated with magnetic properties, particularly field induced metamagnetic transition (MMT). Strong MMT enabled DyCufr develops SPC, while weak MMT enabled NdCufr is unable to develop SPC.

Amelogenin-inspired peptide, calcium phosphate solution, fluoride and their synergistic effect on enamel biomimetic remineralization: an in vitro pH-cycling model

BACKGROUND

Several methods were introduced for enamel biomimetic remineralization that utilize a biomimetic analogue to interact and absorb bioavailable calcium and phosphate ions and induce crystal nucleation on demineralized enamel. Amelogenin is the most predominant enamel matrix protein that is involved in enamel biomineralization. It plays a major role in developing the enamel's hierarchical microstructure. Therefore, this study was conducted to evaluate the ability of an amelogenin-inspired peptide to promote the remineralization potential of fluoride and a supersaturated calcium phosphate solution in treating artificially induced enamel carious lesions under pH-cycling regimen.

METHODS

Fifty enamel slices were prepared with a window (4*4 mm2 ) on the surface. Five samples were set as control healthy enamel and 45 samples were subjected to demineralization for 3 days. Another 5 samples were set as control demineralized enamel and 40 enamel samples were assigned into 8 experimental groups (n=5) (P/I, P/II, P/III, P/AS, NP/I, NP/II, NP/III and NP/AS) according to peptide treatment (peptide P or non-peptide NP) and remineralizing solution used (I; calcium phosphate solution, II; calcium phosphate fluoride solution, III; fluoride solution and AS; artificial saliva). Samples were then subjected to demineralization/remineralization cycles for 9 days. Samples in all experimental groups were evaluated using Raman spectroscopy for mineral content recovery percentage, microhardness and nanoindentation as healthy, demineralized enamel and after pH-cycling. Data were statistically analysed using two-way repeated measures Anova followed by Bonferroni-corrected post hoc test for pairwise multiple comparisons between groups. Statistical significance was set at p= 0.05. Additionally, XRD, FESEM and EDXS were used for crystal orientation, surface morphology and elemental analysis after pH-cycling.

RESULTS

Nanocrystals clumped in a directional manner were detected in peptide-treated groups. P/II showed the highest significant mean values in mineral content recovery (63.31%), microhardness (268.81±6.52 VHN), elastic modulus (88.74±2.71 GPa), nanohardness (3.08±0.59 GPa) and the best crystal orientation with I002/I300 (1.87±0.08).

CONCLUSION

Despite pH changes, the tested peptide was capable of remineralizing enamel with ordered crystals. Moreover, the supplementary use of calcium phosphate fluoride solution with peptide granted an enhancement in enamel mechanical properties after remineralization.

Extreme Thermal Insulation and Tradeoff of Thermal Transport Mechanisms between Graphene and WS2 Monolayers

Controlling and understanding the heat flow at a nanometer scale are challenging, but important for fundamental science and applications. Two-dimensional (2D) layered materials provide perhaps the ultimate solution for meeting these challenges. While there have been reports of low thermal conductivities (several mW m-1 K-1 ) across the 2D heterostructures, phonon-dominant thermal transport remains strong due to the nearly-ideal contact between the layers. Here, this work experimentally explores the heat transport mechanisms by increasing the interlayer distance from perfect contact to a few nanometers and demonstrates that the phonon-dominated thermal conductivity across the WS2 /graphene interface decreases further with the increasing interlayer distance until the air-dominated thermal conductivity increases again. This work finds that the resulting tradeoff of the two heat conduction mechanisms leads to the existence of a minimum thermal conductivity at 2.11 nm of 1.41 × 10-5  W m-1 K-1 , which is two thousandths of the smallest value reported previously. This work provides an effective methodology for engineering thermal insulation structures and understanding heat transport at the ultimate small scales.

Resonance Raman spectroscopy of twisted interfaces in turbostratic multilayer graphene

Turbostratic multilayer graphene presents a unique system with a large number of twisted interfaces with variable twist angles. In this work, we have systematically studied the laser excitation energy dependence of the Raman modes of turbostratic graphene. The combination of 4 different laser energies is shown to be important to reveal the twist angles ranging from 5∘to 30∘present at the same lateral position of the sample. Rotational or R-modes and D-like modes are observed, which directly arise from additional momentum transfer from the potential of corresponding superlattices. Trends in their dispersion and intensity are discussed. The resonant window for laser excitation indicates lowered positions of the van Hove singularities. Furthermore, an anomalous broadening factor of 0.17-0.265 eV is estimated for the resonance window when compared to the literature on isolated twisted bilayer graphene. Interestingly, a weak dependence of the R-modes on the laser wavelength is also observed. Finally, the dispersion of the 2D modes is also presented.

Investigation of solute-solvent interactions of 4'-alkyl-4-cyanobiphenyl liquid crystals using Raman spectroscopy

Liquid crystal materials possess hybrid liquid and solid-like properties with high response to stimuli. The 4'-alkyl-4-cyanobiphenyls (nCB) are an important class of liquid crystals that are widely used for various applications. In this study, six alkylcyanobiphenyl liquid crystal samples (5CB to 10CB) were examined using Raman spectroscopy in a total of twelve solvents of various polarities. The distinctive bands contributed by the LC sample are from C≡N stretch, C-C aromatic ring breathing, C-C biphenyl linking stretch, and C-H in-plane deformation. These modes are found to be responsive to different solvent environments by shifting positions. For instance, the cyano stretching mode of 5CB is blue-shifted from 2229 cm-1 for the pure sample to 2233 cm-1 when measured in hexane due to the repulsive interaction between the mode and the solvent bath. On the other hand, this mode undergoes a red-shift to 2220 cm-1 in methanol due to hydrogen bond interaction between the mode and solvent molecules. Overall, the shift in the position of the vibrational modes of nCB molecules was correlated with solvent properties such as acceptor number, donor number, and Kamlet-Taft dipolarity constants, which are a measure of solvent polarity. In samples with longer alkyl chains (8CB, 9CB, and 10CB), the wavenumber of the stretching modes was generally shifted to around the same position in all the solvents. Such observations are related to the folding back effect and enhanced dimerization constant with an increase in the chain length.