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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.