Employing Raman Spectroscopy Research Articles

The effect of ceramides on skin absorption by Raman spectroscopy.

Ceramides are essential epidermal constituents that play a critical role in skin moisturization treatment as a raw material in cosmetics formulation. Recently, ceramides have been known to be frequently applied in various cosmetic formulations. Despite ceramide's beneficial characteristics, academic research regarding ceramides and their skin absorption remains insufficient. Therefore, our study conducted clinical research employing Raman spectroscopy to investigate the effects of ceramides on skin absorption to enhance the understanding of ceramides' dermatological functionality and their topical application in cosmetics science. Twenty healthy individuals with dry skin have participated in this clinical trial. In this double-arm designed trial, the test group received an investigational product with ceramides (5000ppm) and a control group received an investigational product without the ceramides while all other components remained identical. The subjects visited the clinical research center and acclimatized for 30 min in constant humidity and temperature for equilibrium, subsequently conducting a measurement. Before the trial, the research subject's target site (lower arm area) was kept clean, devoid of any cosmetic administering 24h before the trial when investigational product was topically applied. Our findings with Raman spectroscopy statistically demonstrate that skin absorption amount, speed and depth for both groups improved overall (p<0.05) after administration of the investigational product. Notably, the test group received an investigational product with ceramides (5000ppm) indicating superior effectiveness across all parameters compared to a control group from comparison analysis of each parameter (p<0.05). This study concludes that ceramide-containing cosmetics provide a beneficial effect on skin absorption via visual and statistical results of Raman spectroscopy analysis.

Structural phase transition in NH₄F under extreme pressure conditions.

Ammonium fluoride (NH₄F) exhibits a variety of crystalline phases depending on temperature and pressure. By employing Raman spectroscopy and synchrotron X-ray diffraction beyond megabar pressures (up to 140 GPa), we have here observed a novel dense solid phase of NH₄F, characterised by the tetragonal P4/nmm structure also observed in other ammonium halides under less extreme pressure conditions, typically a few GPa. Using detailed ab-initio calculations and reevaluating earlier theoretical models pertaining to other ammonium halides, we examine the microscopic mechanisms underlying the transition from the low-pressure cubic phase (P-43m) to the newly identified high-pressure tetragonal phase (P4/nmm). Notably, NH₄F exhibits distinctive properties compared to its counterparts, resulting in a significantly broader pressure range over which this transition unfolds, facilitating the identification of its various stages. Our analysis points to a synergistic interplay driving the transition to the P4/nmm phase, which we name phase VIII. At intermediate pressures (around 40 GPa), a displacive transition of fluorine ions initiates a tetragonal distortion of the cubic phase. Subsequently, at higher pressures (around 115 GPa), every second ammonium ion undergoes a rotational shift, adopting an anti-tetrahedral arrangement. This coupled effect orchestrates the transition process, leading to the formation of the tetragonal phase.

The construction of an inflammation classification model for HDPCs applied in guiding pulpotomy based on single-cell Raman spectroscopy

The immune defense and the repair function of the pulp tissue serve as the biological foundation of pulpotomy. The precise evaluation of the pulp inflammation extent and determining its reversibility are essential for the success of pulpotomy. The objective of this study was to classify the molecular-level of dental pulp cell physiology and inflammatory state based on the biochemical changes obtained by single-cell Raman spectroscopy. Firstly, we differentiated the growth of HDPCs (human dental pulp cells) under physiological states by employing Raman spectroscopy with multivariate statistical analysis. Raman spectroscopy reflected the biochemical changes at different growth phases, including the lag phase, log phase, and stationary phase. Secondly, we evaluated the optimal concentration and duration of Porphyromonas gingivalis lipopolysaccharide (P.g.LPS) stimulation to establish a six-level inflammation classification model of HDPCs. Thirdly, we performed label-free characterization of biological component changes in cells of different inflammation grades by Raman spectroscopy. As a result, the differences of peaks in the region 600–1800 cm−1 demonstrated the biochemical molecular alterations in the different inflammation grades of HDPCs. As inflammation progresses in steps, protein peaks increased first and then decreased, while lipid and nucleic acid peaks gradually decreased compared to unstimulated cells. However, when the inflammatory stimulation reached grade V, the changes in the biological properties were characterized by a recovery in protein and lipid content, and a decrease in nucleic acid content. We then established the diagnostic model using the Raman spectra of HDPCs in physiological and inflammatory states, which had a prediction accuracy of 100 % and 97.4 %, respectively. Finally, we determined the reversibility threshold of HDPCs at different grades of inflammation. We observed that the inflammation of grade I and II cells had potential reversibility and could be attempted to be retained. In conclusion, Raman spectroscopy combined with multivariate statistical analysis has potential possibility to effectively distinguish the degree of inflammation in the dental pulp, thus providing new tools and perspectives on pulpotomy in clinical practice.

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

Label-free 3D molecular imaging of living tissues using Raman spectral projection tomography

The ability to image tissues in three dimensions (3D) with label-free molecular contrast at the mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with optical sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3D molecular contrast in transparent and semi-transparent samples. We also created a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data. Using these tools, we demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and living tissue-engineered constructs and explanted native cartilage tissues. We show that there exists a favorable balance wherein employing Raman spectroscopy, with its advantages in live cell imaging and label-free molecular contrast, outweighs the reduction in imaging resolution and blurring caused by diffuse photon propagation. Thus, RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

Tackling the plastisphere: the single-cell Raman spectroscopy framework

Conventional bulk molecular approaches, often limited by their destructive nature and low spatial resolution, face challenges when probing the intricate dynamics of the plastisphere. Here, we outline a framework employing Raman spectroscopy combined with stable isotope profiling (SIP) to interrogate the physiological function of the plastisphere microbiome and track its evolutionary trajectories.

Solid-mechanics analysis and modeling of the alloyed ohmic contact proximity in GaN HEMTs using µRaman spectroscopy

This study explores the impact of alloyed ohmic contact separation on ungated GaN high electron mobility transistors (HEMTs) lattice stress by employing Raman spectroscopy and solid mechanics simulations for comprehensive analysis. Focusing on the substantial stresses exerted by ohmic contacts, our research introduces a novel mechanical calibration procedure. The proposed procedure demonstrates that the stress in the GaN buffer can be precisely modelled using Raman measurements taken from patterns of varying length, which in return reveals the impact of ohmic contacts on stress. We show that this technique shows a good alignment to the Raman measurement results. Moreover, we identify ohmic contact edges as potential sites for defect generation due to the accumulation of substantial elastic energy, a finding supported by experimental observations of crack formations in related studies. Our calibrated mechanical model not only enhances the understanding of stress distributions within GaN HEMTs but also lays the groundwork for future improvements in electro-thermo-mechanical simulations.

Detection of sheep butter adulteration with cow butter and margarine by employing Raman spectroscopy and multivariate data analysis

The adulteration of sheep butter with cow butter and margarine is a significant challenge in the food industry, impacting product authenticity and consumer confidence. In this research article, a comprehensive and accurate approach is proposed for detecting sheep butter adulteration by employing Raman spectroscopy in conjunction with the combined method of DD-SIMCA and PLS-DA.Initially, Raman spectroscopy employed to collect spectral data from pure butter and margarine samples, enabling the modeling of target objects by DD-SIMCA to exclude adulterated samples. Subsequently, PLS-DA was utilized for the discrimination of sample classes.Raman spectroscopy, combined with the classification and discrimination strategy, is a powerful tool for quickly and non-destructively detecting sheep butter adulteration, ensuring food quality control. The combined strategy successfully discriminated between pure samples, achieving high performances based on the figures of merit (sensitivity: 77.78–100% and specificity: 88.23–100%) for detecting and identifying the type of adulteration in sheep butter. These outcomes underscore the efficacy of the proposed approach in not only detecting but also identifying the specific type of adulteration present in sheep butter, thereby addressing a critical need in the food industry. This research contributes significantly to advancing the field of food quality assurance and holds great implications for support consumer confidence in product authenticity.

Modifying single-crystal silicon and trimming silicon microring devices by femtosecond laser irradiation

Single-crystal silicon (c-Si) is a vital component of photonic devices and has obvious advantages. Moreover, femtosecond-pulsed laser interactions with matter have been widely applied in micro/nanoscale processing. In this paper, we report the modification mechanisms of c-Si induced by a femtosecond laser (350 fs, 520 nm) at different pulse fluences, along with the mechanism of this technique to trim the phase error of c-Si-based devices. In this study, several distinct types of final micro/nanostructures, such as amorphization and ablation, were analyzed. The near-surface morphology was characterized using optical microscopy, scanning electron microscopy, and atomic force microscopy. The main physical modification processes were further analyzed using a two-temperature model. By employing Raman spectroscopy, we demonstrated that a higher laser fluence significantly contributes to the formation of more amorphous silicon components. The thickness of the amorphous layer was almost uniform (approximately 30 nm) at different induced fluences, as determined using transmission electron microscopy. From the ellipsometry measurements, we demonstrated that the refractive index increases for amorphization while the ablation decreases. In addition, we investigated the ability of the femtosecond laser to modify the effective index of c-Si microring waveguides by either amorphization or ablation. Both blue and red shifts of resonance spectra were achieved in the microring devices, resulting in double-direction trimming. Our results provide further insight into the femtosecond laser modification mechanism of c-Si and may be a practical method for dealing with the fabrication errors of c-Si-based photonic devices.

Identification of spermatogenesis in individual seminiferous tubules and testicular tissue of adult normal and busulfan-treated mice employing Raman spectroscopy and principal component analysis

The present study aims to identify spermatogenesis in testicular seminiferous tubules (ST) and testicular tissue of adult normal and busulfan-treated mice utilizing PCA and Raman spectroscopy. Raman measurements were conducted on single tubules and testes samples from adult and immature mice, comparing them with those from busulfan-treated adult mice, with validation through histological examination. The analysis revealed a higher signal variability (30 %–40 % at the peaks), prompting scrutiny of individual Raman spectra as a means of spermatogenesis measurement. However, principal component analysis (PCA) demonstrated significant cluster separation between the ST of mature and immature mice. Similar investigations were performed to compare ST from normal mature mice and those from busulfan-treated (BS-treated) mature mice, revealing substantial separation along PC1 and PC2 for all comparison sets. Additionally, comparing testicular samples from mature and immature mice revealed distinct separation in PCA. The study concludes that the combined approach of PCA and Raman spectroscopy proves to be a noninvasive and potentially valuable method for identifying spermatogenesis in seminiferous tubules and testicular samples.

Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy.

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.

Micropatterned Strain Domain in Graphene on Cu Substrates: Correlation with Facet Structures

Metallic catalysts have emerged as prominent materials for graphene growth because the choice of the substrate significantly influences the properties of graphene. Among metallic catalysts, Cu is a highly promising catalytic substrate because it facilitates the growth of monolayer graphene. However, the growth process often induces strain and structural deformations that affect the electronic properties and device performance of graphene. In this study, we present the first observation of micropatterned strain domains in graphene grown on Cu substrates using chemical vapor deposition. Distinct strain‐induced shifts and broadening in the Raman peaks by employing Raman spectroscopy and atomic force microscopy are revealed, demonstrating the unique structural characteristics within the patterned domains. Furthermore, a strong correlation between the strain patterns in graphene and the facet structures of the underlying Cu surface is established. Our findings reveal intriguing variations in strain within the patterned domains and along the line boundaries, offering valuable insights into the intricate interactions between graphene and the Cu surface. These observed strain patterns and their spatial correlations with the Cu facet structures provide crucial guidance for designing graphene‐based devices with tailored strain engineering.

Monitoring the dynamic process of non-thermal plasma decontaminated water with Raman spectroscopy real-time analysis system

This study focuses on the target pollutant, indigo carmine (E132), employing Raman spectroscopy in conjunction with machine learning techniques. An accurate quantitative analysis model was developed to monitor the dynamic plasma decontamination process, utilizing the partial least squares (PLS). The PLS model exhibited excellent predictive performance, achieving adjusted determination coefficients (R2) of 0.9990 and root mean square error (RMSE) values of 0.0058 g/L for the training set, and 0.9730 and 0.0178 g/L for the test set. Real-time monitoring using Raman spectroscopy investigated the impact of discharge voltages, gas flow rates, and solution pH values on E132 degradation. Feedback control based on real-time data led to a 75 % improvement in degradation efficiency by adjusting discharge gas velocity and enhanced energy utilization through discharge time control. Furthermore, the study provided insights into the mechanisms of air plasma-based wastewater treatment. This research presents a convenient and efficient method for online pollutant analysis, optimizing plasma-based water treatment parameters, improving treatment effectiveness, and conserving energy. It lays the groundwork for integrating advanced control algorithms to achieve automated decontamination processes.

Non-Destructive Discrimination of Blue Inks on Suspected Documents through the Combination of Raman Spectroscopy and Chemometric Analysis

Increasingly sophisticated techniques for falsifying and forging legal documents demand non-destructive and accurate analysis methods. Researchers have extensively investigated ink discrimination through an interdisciplinary analysis involving Raman spectroscopy and chemometrics, which is now regarded as a leading forensic document analysis approach. In this study, a groundbreaking method was developed to identify the specific origin of blue-ink pens used in written documents. By employing Raman spectroscopy in conjunction with principal component analysis (PCA), we successfully differentiated between 45 different blue-ink pens used on various documents. The Raman spectroscopy analysis provided a visual examination of each blue ink’s unique Raman signature, and PCA was then applied to the processed spectral data. Moreover, we successfully distinguished highly similar ink types in documents through the combined use of Raman spectroscopy, Pearson’s correlation analysis, and a statistical approach (PCA).

Temperature dependence of electrical conductivity and variable hopping range mechanism on graphene oxide films

The rapid development of optoelectronic applications for optical-to-electrical conversion has increased the interest in graphene oxide material. Here, graphene oxide films (GOF) were used as source material in an infrared photodetector configuration and the temperature dependence of the electrical conductivity was studied. GOF were prepared by the double-thermal decomposition (DTD) method at 973 K, with a fixed carbonization temperature, in a pyrolysis system, under a controlled nitrogen atmosphere, over quartz substrates. Graphene oxide films were mechanically supported in a photodetector configuration on Bakelite substrates and electrically contacted with copper wires and high-purity silver paint. Morphological images from the GOF’s surface were taken employing a scanning electron microscope and observed a homogeneous surface which favored the electrical contacts deposition. Vibrational characteristics were studied employing Raman spectroscopy and determined the typical graphene oxide bands. GOF were used to discuss the effect of temperature on the film’s electrical conductivity. Current–voltage (I–V) curves were taken for several temperatures varying from 20 to 300 K and the electrical resistance values were obtained from 142.86 to 2.14 kΩ. The GOF electrical conductivity and bandgap energy (Eg) were calculated, and it was found that when increasing temperature, the electrical conductivity increased from 30.33 to 2023.97 S/m, similar to a semiconductor material, and Eg shows a nonlinear change from 0.33 to 0.12 eV, with the increasing temperature. Conduction mechanism was described mainly by three-dimensional variable range hopping (3D VRH). Additionally, measurements of voltage and electrical resistance, as a function of wavelength were considered, for a spectral range between 1300 and 3000 nm. It was evidenced that as the wavelength becomes longer, a greater number of free electrons are generated, which contributes to the electrical current. The external quantum efficiency (EQE) was determined for this proposed photodetector prototype, obtaining a value of 40%, similar to those reported for commercial semiconductor photodetectors. This study provides a groundwork for further development of graphene oxide films with high conductivity in large-scale preparation.

Refractive Index of Supercooled Water Down to 230.3 K in the Wavelength Range between 534 and 675 nm

Knowledge of the refractive index of water in the deeply supercooled metastable liquid state is important, for example, for an accurate description of optical reflection and refraction processes occurring in clouds. However, a measurement of both the temperature and wavelength dependence of the refractive index under such extreme conditions is challenging. Here, we employ Raman spectroscopy in combination with microscopic water jets in vacuum to obtain the refractive index of supercooled water to a lowest temperature of 230.3 K. While our approach is based on the analysis of Mie resonances in Raman spectra measured by using a single excitation wavelength at 532 nm, it allows us to obtain the refractive index in a wide visible wavelength range from 534 to 675 nm. Because of a direct link between the refractive index and density of water, our results provide a promising approach to help improve our understanding of water's anomalous behavior.

Quantification of the Mesoscale Spatiotemporal Heterogeneities in Nickel-Rich Layered Oxide Cathodes By Raman Spectroscopy

Lithium-ion batteries consisting of the particulate porous electrodes, are the dominant energy storage devices, however, they suffer from random failures and safety-related accidents. Such arbitrary incidents arise from the spatiotemporal heterogeneities occurring due to the material thermodynamics[1] which results to local hot and cold spots and early battery failures. The past investigations of the heterogeneities in the battery materials rely on the sophisticated synchrotron X-ray methods[2]–[4], which are not easily accessible. Instead, in this study, we employ Raman spectroscopy on an in situ benchtop setup which provides several advantages while investigating the true electrochemical kinetics in graphite electrodes, such as: (i) the setup houses practical porous NMC532 cathodes under realistic electrochemical surroundings; (ii) the high resolution of 1-2 µm provides sufficient information on the larger cathode particles; (iii) the method can analyze hundreds of particles, statistically representing the entire composite electrode; and (iv) the spectra can be quantified to reveal the actual reaction area, thus revealing high-fidelity real-time electrochemical performance. We use this simple but precision method to track and analyze the active reaction regions at the mesoscale in the NMC532 electrode to obtain the true local current density, which was found to be an order of magnitude higher than the globally-averaged current density adopted by the existing studies.Raman spectroscopy can distinguish the vibrational modes of NMC532 with varying Li ion concentration. Since NMC532 is a solid-solution material, the Raman spectrum at each selected location can be deconvoluted into three distinct phases: empty, completely lithiated and partially lithiated. The partially lithiated regions participate in the surface reaction and can be identified to reveal the active reaction area. Thus, we quantified the active area fraction under slow galvanostatic conditions and generated a map of active regions with varying global Li ion fractions. Our results show that only 18% – 50% of the total particle area is active at any instant leading to higher local current densities than the globally-averaged electrode responses. The methods developed in this study are critical to estimate the kinetic parameters using true electrochemical responses and further aid in the design of the particulate porous electrodes.

At Least 10-fold Higher Lubricity of Molecularly Thin D2O vs H2O Films at Single-Layer Graphene-Mica Interfaces.

Interfacial water is a widespread lubricant down to the nanometer scale. We investigate the lubricities of molecularly thin H2O and D2O films confined between mica and graphene, via the relaxation of initially applied strain in graphene employing Raman spectroscopy. Surprisingly, the D2O films are at least 1 order of magnitude more lubricant than H2O films, despite the similar bulk viscosities of the two liquids. We propose a mechanism based on the known selective permeation of protons vs deuterons through graphene. Permeated protons and left behind hydroxides may form ion pairs clamping across the graphene sheet and thereby hindering the graphene from sliding on the water layer. This explains the lower lubricity but also the hindering diffusivity of the water layer, which yields a high effective viscosity in accordance with findings in dewetting experiments. Our work elucidates an unexpected effect and provides clues to the behavior of graphene on hydrous surfaces.

Unravelling pine response to Fusarium circinatum through Raman spectroscopy

AbstractPine Pitch Canker (PPC), caused by the fungus Fusarium circinatum, is associated to significant economic and ecological losses worldwide. The effectiveness of PPC monitoring, early detection in nurseries and plantations, and the identification of resistant plant material relies on the development of objective, non‐destructive, and cost‐effective tools. This study analyzed the potential of employing Raman Spectroscopy (RS) for the early detection of biochemical changes associated with PPC in Pinus spp. with different susceptibilities to F. circinatum (highly susceptible Pinus radiata vs relatively resistant Pinus pinea), while unveiling possible mechanisms of action on these pathosystems. Our results indicate lignin as a key constitutive component of pine resistance against PPC and thus the potential of using this technology for the selection of PPC resistant trees. Moreover, we demonstrate the power of RS‐based approaches for the rapid detection of the disease in susceptible species. Early spectral variations were found in P. radiata upon inoculation with F. circinatum from 3 days post‐inoculation (dpi) onwards, whereas changes in histological analysis, relative internal stem necrosis measurements, and visual disease symptoms were only visible at 6, 8, and 9 dpi, respectively. These spectral changes have been associated to cell wall degradation and induction of phenolic compounds synthesis upon infection in P. radiata. Altogether, we believe that RS is an innovative promising tool able to reduce disease detection time in pine species and providing an appealing alternative for the development of new and eco‐friendly disease control measures.

Non-thermal and thermal effects on mechanical strain in substrate-transferred wafer-scale hBN films

Wafer-scale thin films of hexagonal boron nitride have exceptional thermal and mechanical properties, which harness the potential use of these materials in two-dimensional electronic, device applications. Along with unavoidable defects, grains, and wrinkles, which develop during the growth process, underlying substrates influence the physical and mechanical properties of these films. Understanding the interactions of these large-scale films with different substrates is, thus, important for the implementation of this 2D system in device fabrication. MOVPE-grown 2 and 30 nm hBN/sapphire films of size 2 in. diameter are delaminated chemically and transferred on quartz, SiO2/Si, and sapphire substrates. The structural characteristics of these films are investigated by employing Raman spectroscopy. Our results suggest that not only the roughness but also the height modulation at the surface of the substrates play a pivotal role in determining substrate-mediated mechanical strain inhomogeneity in these films. The statistical analysis of the spectral parameters provides us with the overall characteristics of the films. Furthermore, a Stark difference in the thermal evolution of strain in these films depending on substrate materials is observed. It has been demonstrated that not only the differential thermal expansion coefficient of the substrates and the films, but also slippage of the latter during the thermal treatment determines the net strain in the films. The role of the slippage is significantly higher in 2 nm films than in 30 nm films. We believe that the observations provide crucial information on the structural characteristics of the substrate-coupled wafer-scale hBN films for their future use in technology.