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This letter presents the bulk diamond field-effect transistor (FET) with the highest current value reported at this moment. The goal was to drastically increase the current of this type of device by increasing the total gate width thanks to an interdigitated architecture and homogeneous growth properties. We report the results obtained by fabricating and characterizing an interdigitated junction FET (JFET). The device develops a total gate width of 14.7 mm, with 24 paralleled fingers and a current higher than 50 mA at VDS = -15 V, VGS = 0 V, at 450 K and under illumination which is the highest value reported for a bulk diamond FET. Its specific ON-resistance and threshold voltage are respectively 608 m$\Omega$.cm${}^2$, 50 V. From Transfer length method (TLM) measurements we extract a resistivity of 3.6 m$\Omega$.cm for a heavily boron-doped (p++)-diamond layer and 1.52 $\Omega$.cm for a 2.1017 cm-3 p-doped diamond layer at 450 K. We measured the drain current versus gate voltage characteristics at high temperature showing that it is no longer the conduction channel resistance but the device access resistance that is predominant. This study indicates that it is possible to drastically improve the ON-state of FETs by using an interdigitated architecture, while using homogeneous large size diamond layers grown by CVD.

This short study presents an opportunistic approach to a (more) reliable validation method for prediction uncertainty average calibration. Considering that variance-based calibration metrics (ZMS, NLL, RCE...) are quite sensitive to the presence of heavy tails in the uncertainty and error distributions, a shift is proposed to an interval-based metric, the Prediction Interval Coverage Probability (PICP). It is shown on a large ensemble of molecular properties datasets that (1) sets of z-scores are well represented by Student's-$t(\nu)$ distributions, $\nu$ being the number of degrees of freedom; (2) accurate estimation of 95 $\%$ prediction intervals can be obtained by the simple $2\sigma$ rule for $\nu>3$; and (3) the resulting PICPs are more quickly and reliably tested than variance-based calibration metrics. Overall, this method enables to test 20 $\%$ more datasets than ZMS testing. Conditional calibration is also assessed using the PICP approach.

Scanning Thermal Microscopy (SThM) has become an important measurement tool for characterizing the thermal properties of materials at the nanometer scale. This technique requires a SThM probe that combines an Atomic Force Microscopy (AFM) probe and a very sensitive resistive thermometry; the thermometer being located at the apex of the probe tip allows the mapping of temperature or thermal properties of nanostructured materials with very high spatial resolution. The high interest of the SThM technique in the field of thermal nanoscience currently suffers from a low temperature sensitivity despite its high spatial resolution. To address this challenge, we developed a high vacuum-based AFM system hosting a highly sensitive niobium nitride (NbN) SThM probe to demonstrate its unique performance. As a proof of concept, we utilized this custom-built system to carry out thermal measurements using the 3$\omega$ method. By measuring the $V_{3\omega}$ voltage on the NbN resistive thermometer in vacuum conditions we were able to determine the SThM probe's thermal conductance and thermal time constant. The performance of the probe is demonstrated by doing thermal measurements in-contact with a sapphire sample., Comment: 17 pages, 13 figures

Some popular Machine Learning Uncertainty Quantification (ML-UQ) calibration statistics do not have predefined reference values and are mostly used in comparative studies. In consequence, calibration is almost never validated and the diagnostic is left to the appreciation of the reader. Simulated reference values, based on synthetic calibrated datasets derived from actual uncertainties, have been proposed to palliate this problem. As the generative probability distribution for the simulation of synthetic errors is often not constrained, the sensitivity of simulated reference values to the choice of generative distribution might be problematic, shedding a doubt on the calibration diagnostic. This study explores various facets of this problem, and shows that some statistics are excessively sensitive to the choice of generative distribution to be used for validation when the generative distribution is unknown. This is the case, for instance, of the correlation coefficient between absolute errors and uncertainties (CC) and of the expected normalized calibration error (ENCE). A robust validation workflow to deal with simulated reference values is proposed.

Average calibration of the (variance-based) prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV); the alternative is to compare the mean squared z-scores (ZMS) to 1. The problem is that both approaches might lead to different conclusions, as illustrated in this study for an ensemble of datasets from the recent machine learning uncertainty quantification (ML-UQ) literature. It is shown that the estimation of MV, MSE and their confidence intervals becomes unreliable for heavy-tailed uncertainty and error distributions, which seems to be a frequent feature of ML-UQ datasets. By contrast, the ZMS statistic is less sensitive and offers the most reliable approach in this context, still acknowledging that datasets with heavy-tailed z-scores distributions should be considered with great care. Unfortunately, the same problem is expected to affect also conditional calibrations statistics, such as the popular ENCE, and very likely post-hoc calibration methods based on similar statistics. Several solutions to circumvent the outlined problems are proposed.

Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with a strong potential for commercialization due to their biological origin and mildness in contact with the skin and the environment compared to classical surfactants. However, their association properties in water are still poorly understood, they cannot be predicted and their behavior in solution challenges half a century of knowledge generated in the field of surfactant science. By studying forty different types of sophorolipids and sophorosides in water using small angle X-ray scattering, and optical and cryogenic transmission electron microscopy, this work provides better understanding of their structure-property relationship and identifies which chemical groups in their molecular structure have a critical influence on their self-assembly properties. Structural features like the number of sugar headgroups, acetylation, end-chain functional group, (un)saturation, lactonization and length of chain are adjusted to both rationalize their impact and understand their effect on self-assembly. The number of sugar groups, pH, (un)saturation and lactonization were found to have a critical impact on sophorolipid self-assembly. The chemical nature of the end-chain functional group and chain length were also found to have a possibly critical impact, depending on the specific type of chemical function (COOH and long chains are critical). Mono- and diacetylation, as well as the position of sophorose in the fatty acid ($\omega$, $\omega$-1), are not critical, i.e., they did not significantly influence sophorolipid self-assembly.

Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration conditionally on uncertainty, i.e. consistency. I explore here several adaptations of BVS, in particular with alternative loss functions and a binning scheme based on an input-feature (X) in order to improve adaptivity, i.e. calibration conditional on X. The performances of BVS and its proposed variants are tested on a benchmark dataset for the prediction of atomization energies and compared to the results of isotonic regression., Comment: This version corrects an error in the estimation of the Sx scores for the test set, affecting Fig. 2 and Tables I-III of the initial version. The main points of the discussion and the conclusions are unchanged

Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understood that average calibration is insufficient, and most studies implement additional methods testing the conditional calibration with respect to uncertainty, i.e. consistency. Consistency is assessed mostly by so-called reliability diagrams. There exists however another way beyond average calibration, which is conditional calibration with respect to input features, i.e. adaptivity. In practice, adaptivity is the main concern of the final users of a ML-UQ method, seeking for the reliability of predictions and uncertainties for any point in features space. This article aims to show that consistency and adaptivity are complementary validation targets, and that a good consistency does not imply a good adaptivity. Adapted validation methods are proposed and illustrated on a representative example., Comment: arXiv admin note: text overlap with arXiv:2303.07170

The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray scattering (SAXS). The self-assembly of three RLs with reasoned variation of their molecular structure (RhaC10, RhaC10C10 and RhaRhaC10C10) and a rhamnose-free C10C10 fatty acid is studied in water as a function of pH. It is found that RhaC10 and RhaRhaC10C10 form micelles in a broad pH range and RhaC10C10 undergoes a micelle-to-vesicle transition from basic to acid pH occurring at pH 6.5. Modelling coupled to fitting SAXS data allows a good estimation of the hydrophobic core radius (or length), the hydrophilic shell thickness, the aggregation number and the surface area per RL. The essentially micellar morphology found for RhaC10 and

Abstract Post hoc recalibration of prediction uncertainties of machine learning regression problems by isotonic regression might present a problem for bin-based calibration error statistics (e.g. ENCE). Isotonic regression often produces stratified uncertainties, i.e. subsets of uncertainties with identical numerical values. Partitioning of the resulting data into equal-sized bins introduces an aleatoric component to the estimation of bin-based calibration statistics. The partitioning of stratified data into bins depends on the order of the data, which is typically an uncontrolled property of calibration test/validation sets. The tie-braking method of the ordering algorithm used for binning might also introduce an aleatoric component. I show on an example how this might significantly affect the calibration diagnostics.

The Expected Normalized Calibration Error (ENCE) is a popular calibration statistic used in Machine Learning to assess the quality of prediction uncertainties for regression problems. Estimation of the ENCE is based on the binning of calibration data. In this short note, I illustrate an annoying property of the ENCE, i.e. its proportionality to the square root of the number of bins for well calibrated or nearly calibrated datasets. A similar behavior affects the calibration error based on the variance of z-scores (ZVE), and in both cases this property is a consequence of the use of a Mean Absolute Deviation (MAD) statistic to estimate calibration errors. Hence, the question arises of which number of bins to choose for a reliable estimation of calibration error statistics. A solution is proposed to infer ENCE and ZVE values that do not depend on the number of bins for datasets assumed to be calibrated, providing simultaneously a statistical calibration test. It is also shown that the ZVE is less sensitive than the ENCE to outstanding errors or uncertainties.

The assessment of myocardial fiber disarray is of major interest for the study of the progression of myocardial disease. However, time-resolved imaging of the myocardial structure remains unavailable in clinical practice. In this study, we introduce 3D Backscatter Tensor Imaging (3D-BTI), an entirely novel ultrasound-based imaging technique that can map the myocardial fibers orientation and its dynamics with a temporal resolution of 10 ms during a single cardiac cycle, non-invasively and in vivo in entire volumes. 3D-BTI is based on ultrafast volumetric ultrasound acquisitions, which are used to quantify the spatial coherence of backscattered echoes at each point of the volume. The capability of 3D-BTI to map the fibers orientation was evaluated in vitro in 5 myocardial samples. The helicoidal transmural variation of fiber angles was in good agreement with the one obtained by histological analysis. 3D-BTI was then performed to map the fiber orientation dynamics in vivo in the beating heart of an open-chest sheep at a volume rate of 90 volumes/s. Finally, the clinical feasibility of 3D-BTI was shown on a healthy volunteer. These initial results indicate that 3D-BTI could become a fully non-invasive technique to assess myocardial disarray at the bedside of patients.

Mapping blood microflows of the whole brain is crucial for early diagnosis of cerebral diseases. Ultrasound localization microscopy (ULM) was recently applied to map and quantify blood microflows in 2D in the brain of adult patients down to the micron scale. Whole brain 3D clinical ULM remains challenging due to the transcranial energy loss which significantly reduces the imaging sensitivity. Large aperture probes with a large surface can increase both resolution and sensitivity. However, a large active surface implies thousands of acoustic elements, with limited clinical translation. In this study, we investigate via simulations a new high-sensitive 3D imaging approach based on large diverging elements, combined with an adapted beamforming with corrected delay laws, to increase sensitivity. First, pressure fields from single elements with different sizes and shapes were simulated. High directivity was measured for curved element while maintaining high transmit pressure. Matrix arrays of 256 elements with a dimension of 10x10 cm with small ( $\lambda$ /2), large (4 $\lambda$ ), and curved elements (4 $\lambda$ ) were compared through point spread functions analysis. A large synthetic microvessel phantom filled with 100 microbubbles per frame was imaged using the matrix arrays in a transcranial configuration. 93% of the bubbles were detected with the proposed approach demonstrating that the multi-lens diffracting layer has a strong potential to enable 3D ULM over a large field of view through the bones.

The practice of uncertainty quantification (UQ) validation, notably in machine learning for the physico-chemical sciences, rests on several graphical methods (scattering plots, calibration curves, reliability diagrams and confidence curves) which explore complementary aspects of calibration, without covering all the desirable ones. For instance, none of these methods deals with the reliability of UQ metrics across the range of input features (adaptivity). Based on the complementary concepts of consistency and adaptivity, the toolbox of common validation methods for variance- and intervals- based UQ metrics is revisited with the aim to provide a better grasp on their capabilities. This study is conceived as an introduction to UQ validation, and all methods are derived from a few basic rules. The methods are illustrated and tested on synthetic datasets and representative examples extracted from the recent physico-chemical machine learning UQ literature.

Low-molecular weight gelators (LMWG) are small molecules (Mw < ~1 kDa), which form self-assembled fibrillar networks (SAFiN) hydrogels in water. The great majority of SAFiN gels is described by an entangled network of self-assembled fibers, in analogy to a polymer in a good solvent. Here, fibrillation of a biobased glycolipid bolaamphiphile is triggered by Ca2+ or Ag+ ions, added to its diluted micellar phase. The resulting SAFiN, which forms hydrogel above 0.5 wt%, has a ``nano-fishnet'' structure, characterized by a fibrous network of both entangled fibers and $\beta$-sheets-like rafts, generally observed for silk fibroin, actin hydrogels or mineral imogolite nanotubes, but generally not known for SAFiN. This work focuses on the strength of the SAFIN gels, their fast recovery after applying a mechanical stimulus (strain) and their unusual resistance to temperature, studied by coupling rheology to small angle X-ray scattering (rheo-SAXS) using synchrotron radiation. The Ca2+-based hydrogel keeps its properties up to 55{\textdegree}C, while the Ag+-based gel shows a constant elastic modulus up to 70{\textdegree}C, without appearance of any gel-to-sol transition temperature. Furthermore, the glycolipid is obtained by fermentation from natural resources (glucose, rapeseed oil), thus showing that naturally-engineered compounds can have unprecedented properties, when compared to the wide range of chemically derived amphiphiles.

The space mission MICROSCOPE dedicated to the test of the Equivalence Principle (EP) operated from April 25, 2016 until the deactivation of the satellite on October 16, 2018. In this analysis we compare the free-fall accelerations ($a_{\rm A}$ and $a_{\rm B}$) of two test masses in terms of the E\"otv\"os parameter $\eta({\rm{A, B}}) = 2 \frac{a_{\rm A}- a_{\rm B}}{a_{\rm A}+ a_{\rm B}}$. No EP violation has been detected for two test masses, made from platinum and titanium alloys, in a sequence of 19 segments lasting from 13 to 198 hours down to the limit of the statistical error which is smaller than $10^{-14}$ for $ \eta({\rm{Ti, Pt}})$. Accumulating data from all segments leads to $\eta({\rm{Ti, Pt}}) =[-1.5\pm{}2.3{\rm (stat)}\pm{}1.5{\rm (syst)}] \times{}10^{-15}$ showing no EP violation at the level of $2.7\times{}10^{-15}$ if we combine stochastic and systematic errors quadratically. This represents an improvement of almost two orders of magnitude with respect to the previous best such test performed by the E\"ot-Wash group. The reliability of this limit has been verified by comparing the free falls of two test masses of the same composition (platinum) leading to a null E\"otv\"os parameter with a statistical uncertainty of $1.1\times{}10^{-15}$., Comment: Class. Quantum Grav. 39 204009

The MICROSCOPE mission was designed to test the Weak Equivalence Principle (WEP), stating the equality between the inertial and the gravitational masses, with a precision of $10^{-15}$ in terms of the E\"otv\"os ratio $\eta$. Its experimental test consisted of comparing the accelerations undergone by two collocated test masses of different compositions as they orbited the Earth, by measuring the electrostatic forces required to keep them in equilibrium. This was done with ultra-sensitive differential electrostatic accelerometers onboard a drag-free satellite. The mission lasted two and a half years, cumulating five-months-worth of science free-fall data, two thirds with a pair of test masses of different compositions -- Titanium and Platinum alloys -- and the last third with a reference pair of test masses of the same composition -- Platinum. We summarize the data analysis, with an emphasis on the characterization of the systematic uncertainties due to thermal instabilities and on the correction of short-lived events which could mimic a WEP violation signal. We found no violation of the WEP, with the E\"otv\"os parameter of the Titanium and Platinum pair constrained to $\eta({\rm Ti, Pt})~=~ [-1.5 \pm 2.3~{\rm (stat)} \pm 1.5~{\rm (syst)}]~\times 10^{-15}$ at $1\sigma$ in statistical errors., Comment: Phys. Rev. Lett. 129, 121102

We review the alternative proposals introduced recently in the literature to update the standard formula to estimate the uncertainty on the mean of repeated measurements, and we compare their performances on synthetic examples with normal and non-normal error distributions.

Confidence curves are used in uncertainty validation to assess how large uncertainties ($u_{E}$) are associated with large errors ($E$). An oracle curve is commonly used as reference to estimate the quality of the tested datasets. The oracle is a perfect, deterministic, error predictor, such as $|E|=\pm u_{E}$, which corresponds to a very unlikely error distribution in a probabilistic framework and is unable unable to inform us on the calibration of $u_{E}$. I propose here to replace the oracle by a probabilistic reference curve, deriving from the more realistic scenario where errors should be random draws from a distribution with standard deviation $u_{E}$. The probabilistic curve and its confidence interval enable a direct test of the quality of a confidence curve. Paired with the probabilistic reference, a confidence curve can be used to check the calibration and tightness of prediction uncertainties.

Self-assembled fibrillar network (SAFIN) hydrogels and organogels are commonly obtained by a crystallization process into fibers induced by external stimuli like temperature or pH. The gel-to-sol-to-gel transition is generally readily reversible and the change rate of the stimulus determines the fiber homogeneity and eventual elastic properties of the gels. However, recent work shows that in some specific cases, fibrillation occurs for a given molecular conformation and the sol-to-gel transition depends on the relative energetic stability of one conformation over the other, and not on the rate of change of the stimuli. We observe such a phenomenon on a class of bolaform glycolipids, sophorosides, similar to the well-known sophorolipid biosurfactants, but composed of two symmetric sophorose units. A combination of oscillatory rheology, small-angle X-ray scattering (SAXS) cryogenic transmission electron microscopy (cryo-TEM) and in situ rheo-SAXS using synchrotron radiation shows that below 14$^\circ$C, twisted nanofibers are the thermodynamic phase. Between 14$^\circ$C and about 33$^\circ$C, nanofibers coexist with micelles and a strong hydrogel forms, the sol-to-gel transition being readily reversible in this temperature range. However, above the annealing temperature of about 40$^\circ$C, the micelle morphology becomes kinetically-trapped over hours, even upon cooling, whichever the rate, to 4$^\circ$C. A combination of solution and solid-state nuclear magnetic resonance (NMR) suggests two different conformations of the 1'', 1' and 2' carbon stereocenters of sophorose, precisely at the $\beta(1,2)$ glycosidic bond, for which several combinations of the dihedral angles are known to provide at least three energetic minima of comparable magnitude and each corresponding to a given sophorose conformation.

Validation of prediction uncertainty (PU) is becoming an essential task for modern computational chemistry. Designed to quantify the reliability of predictions in meteorology, the calibration-sharpness (CS) framework is now widely used to optimize and validate uncertainty-aware machine learning (ML) methods. However, its application is not limited to ML and it can serve as a principled framework for any PU validation. The present article is intended as a step-by-step introduction to the concepts and techniques of PU validation in the CS framework, adapted to the specifics of computational chemistry. The presented methods range from elementary graphical checks to more sophisticated ones based on local calibration statistics. The concept of tightness, is introduced. The methods are illustrated on synthetic datasets and applied to uncertainty quantification data extracted from the computational chemistry literature., Comment: Accepted for publication in J. Chem. Phys

Context. The chemical building blocks of life contain a large proportion of nitrogen, an essential element. Titan, the largest moon of Saturn, with its dense atmosphere of molecular nitrogen and methane, offers an exceptional opportunity to explore how this element is incorporated into carbon chains through atmospheric chemistry in our Solar System. A brownish dense haze is consistently produced in the atmosphere and accumulates on the surface on the moon. This solid material is nitrogen-rich and may contain prebiotic molecules carrying nitrogen. Aims. To date, our knowledge of the processes leading to the incorporation of nitrogen into organic chains has been rather limited. In the present work, we investigate the formation of nitrogen-bearing ions in an experiment simulating Titan s upper atmosphere, with strong implications for the incorporation of nitrogen into organic matter on Titan. Methods. By combining experiments and theoretical calculations, we show that the abundant N2+ ion, produced at high altitude by extreme-ultraviolet solar radiation, is able to form nitrogen-rich organic species. Results. An unexpected and important formation of CH3N2+ and CH2N2+ diazo-ions is experimentally observed when exposing a gas mixture composed of molecular nitrogen and methane to extreme-ultraviolet radiation. Our theoretical calculations show that these diazo-ions are mainly produced by the reaction of N2+ with CH3 radicals. These small nitrogen-rich diazo-ions, with a N/C ratio of two, appear to be a missing link that could explain the high nitrogen content in Titan s organic matter. More generally, this work highlights the importance of reactions between ions and radicals, which have rarely been studied thus far, opening up new perspectives in astrochemistry.

G-quadruplexes (G4s) are helical four-stranded structures forming from guanine-rich nucleic acid sequences, which are thought to play a role in cancer development and malignant transformation. Most current studies focus on G4 monomers, yet under suitable and biologically relevant conditions G4s undergo multimerization. Here, we address the structural, conformational and energetic features of G4 multimers formed in solutions by the human telomere sequence. A novel multi-technique approach is used combining Small Angle X-ray Scattering (SAXS) and circular dichroism experiments with coarse-grained simulations, to provide quantitative information about large-scale structural features and the stability of G4 multimers. The latter show a significant polydispersity with an exponential distribution of contour lengths, suggesting a step-growth polymerization. On increasing DNA concentration, the strength of G4 stacking interaction increases, as well as the number of the units in the aggregates, with dimers and trimers as the most probable forms. At the same time, a variation of G4 conformation is observed. Our findings provide a quantitative picture of human telomere G4 multimers, which must be accounted for to achieve a rational design of anticancer drugs targeting DNA structures.

Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable uncertainty quantification methods is essential, notably for computational chemistry to be used confidently in industrial processes. A review of the CC-UQ literature shows that there is no common standard procedure to report nor validate prediction uncertainty. I consider here analysis tools using concepts (calibration and sharpness) developed in meteorology and machine learning for the validation of probabilistic forecasters. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian Ensembles methods, machine learning and a posteriori statistical methods.

PURPOSE: To develop an automated algorithm allowing extraction of quantitative corneal transparency parameters from clinical spectral-domain OCT images. To establish a representative dataset of normative transparency values from healthy corneas. METHODS: SD-OCT images of 83 normal corneas (ages 22-50 years) from a standard clinical device (RTVue-XR Avanti, Optovue Inc.) were processed. A pre-processing procedure is applied first, including a derivative approach and a PCA-based correction mask, to eliminate common central artifacts (i.e., apex-centered column saturation artifact and posterior stromal artifact) and enable standardized analysis. The mean intensity stromal-depth profile is then extracted over a 6-mm-wide corneal area and analyzed according to our previously developed method deriving quantitative transparency parameters related to the physics of light propagation in tissues, notably tissular heterogeneity (Birge ratio; $B_r$), followed by the photon mean-free path ($l_s$) in homogeneous tissues (i.e., $B_r \sim 1$). RESULTS: After confirming stromal homogeneity ($B_r < 10$, IDR: 1.9-5.1), we measured a median $l_s$ of 570 $\mu$m (IDR: 270-2400 $\mu$m). Considering corneal thicknesses, this may be translated into a median fraction of transmitted (coherent) light $T_{coh(stroma)}$ of 51$\%$ (IDR: 22-83$\%$). No statistically significant correlation between transparency and age or thickness was found. CONCLUSIONS: Our algorithm provides robust and quantitative measurement of corneal transparency from standard clinical SD-OCT images. It yields lower transparency values than previously reported, which may be attributed to our method being exclusively sensitive to spatially coherent light. Excluding images with central artifacts wider than 300 $\mu$m also raises our median $T_{coh(stroma)}$ to 70$\%$ (IDR: 34-87$\%$)., Comment: 15 pages, 7 figures, 1 table

Understandings heat transfer across a solid/liquid interface is important to develop new pathways to improve thermal management in various energy applications. One of the important questions that arises in this context is the impact of three-phase contact line between solid, liquid and gas on the perturbations of the heat fluxes at the nanoscale. Therefore, this paper is devoted to the investigations of features of thermal transport across nanosized meniscus constrained between two solid walls. Different wetting states of the meniscus were considered with molecular dynamics approach by the variation of the interactional potential between atoms of the substrate and the liquid. The effect of the size of the meniscus on the exchange of energy between two solid walls was also investigated. It was shown that the presence of a three phase contact line leads to a decrease of the interfacial boundary resistance between solid and liquid. Further, investigations with the finite element method were used to link atomistic simulations with the continuum mechanics. We demonstrate that the wetting angle and the interfacial boundary resistance are the required key-parameters to perform multiscale simulations of such engineering problems with an accurate microscale parametrization.