Now showing items 21-40 of 64910

    • NMR Chemical Shifts of Emerging Green Solvents, Acids, and Bases for Facile Trace Impurity Analysis

      Cseri, Levente; Kumar, Sushil; Palchuber, Peter; Szekely, Gyorgy (ACS Sustainable Chemistry & Engineering, American Chemical Society (ACS), 2023-03-27) [Article]
      The straightforward identification of impurity signals in nuclear magnetic resonance (NMR) spectra is imperative for the structure elucidation and signal assignment of synthetic products and intermediates. To keep pace with the emergence of novel green solvents and auxiliary compounds (e.g., acids and bases), NMR impurity tables and databases must be regularly updated. This study reports the residual 1H and 13C NMR chemical shifts of 42 green solvents, acids, and bases in eight NMR solvents, namely, dimethylsulfoxide-d6, chloroform-d, D2O, CD3OD, CD3CN, acetone-d6, tetrahydrofuran-d8, and toluene-d8. The multiplicities and coupling constants of 1H signals are also determined herein. The analysis of the recorded NMR spectra provides important information regarding the reactivity or multicomponent nature of the green solvents, acids, and bases. Herein, the results of this study are combined with earlier reports on residual NMR impurities to form a comprehensive database. This database forms the basis of an online interface ( through which users can browse solvent spectra and search for signals of unknown origins to easily identify residual impurities in NMR spectra.
    • On-chip Diamond MEMS Magnetic Sensing through Multifunctionalized Magnetostrictive Thin Film

      Zhang, Zilong; Zhao, Wen; Chen, Guo; Toda, Masaya; Koizumi, Satoshi; Koide, Yasuo; Liao, Meiyong (Advanced Functional Materials, Wiley, 2023-03-27) [Article]
      Electrically integrable, high-sensitivity, and high-reliability magnetic sensors are not yet realized at high temperatures (500 °C). In this study, an integrated on-chip single-crystal diamond (SCD) micro-electromechanical system (MEMS) magnetic transducer is demonstrated by coupling SCD with a large magnetostrictive FeGa film. The FeGa film is multifunctionalized to actuate the resonator, self-sense the external magnetic field, and electrically readout the resonance signal. The on-chip SCD MEMS transducer shows a high sensitivity of 3.2 Hz mT−1 from room temperature to 500 °C and a low noise level of 9.45 nT Hz−1/2 up to 300 °C. The minimum fluctuation of the resonance frequency is 1.9 × 10−6 at room temperature and 2.3 × 10−6 at 300 °C. An SCD MEMS resonator array with parallel electric readout is subsequently achieved, thus providing a basis for the development of magnetic image sensors. The present study facilitates the development of highly integrated on-chip MEMS resonator transducers with high performance and high thermal stability.
    • Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers’ traditional knowledge

      Gesesse, Cherinet Alem; Nigir, Bogale; de Sousa, Kauê; Gianfranceschi, Luca; Gallo, Guido Roberto; Poland, Jesse; Kidane, Yosef Gebrehawaryat; Abate Desta, Ermias; Fadda, Carlo; Pè, Mario Enrico; Dell’Acqua, Matteo (Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, 2023-03-27) [Article]
      In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers’ appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker–trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers’ traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.
    • Hybrid 2D/CMOS microchips for memristive applications

      Zhu, Kaichen; Pazos, Sebastian Matias; Aguirre, Fernando L.; Shen, Yaqing; Yuan, Yue; Zheng, Wenwen; Alharbi, Osamah; Villena, Marco Antonio; Fang, Bin; Li, Xinyi; Milozzi, Alessandro; Farronato, Matteo; Muñoz-Rojo, Miguel; Wang, Tao; Li, Ren; Fariborzi, Hossein; Roldan, Juan B.; Benstetter, Guenther; Zhang, Xixiang; Alshareef, Husam N.; Grasser, Tibor; Wu, Huaqiang; Ielmini, Daniele; Lanza, Mario (Nature, Springer Science and Business Media LLC, 2023-03-27) [Article]
      Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductors industry1-2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (>1µm2) devices on unfunctional SiO2/Si substrates. Some studies integrated monolayer graphene on silicon microchips as large-area (>500µm2) interconnection3 and as channel of large transistors (~16.5µm2)4-5, but in all cases the integration density was low, no computation was demonstrated, and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here we present the fabrication of high-integration-density 2D/CMOS hybrid microchips for memristive applications — CMOS stands for complementary metal oxide semiconductor. We transfer a sheet of multilayer hexagonal boron nitride (h-BN) onto the back-end-of-line (BEOL) interconnections of silicon microchips containing CMOS transistors of the 180nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the h-BN memristors, which allows us to achieve endurances of ~5 million cycles in memristors as small as ~0.053µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity (STDP) signals that are suitable for the implementation of spiking neural networks (SNN). The high performance and the relatively-high technology readiness level achieved represent a significant advance towards the integration of 2D materials in microelectronic products and memristive applications.
    • Integrated Nested Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling

      Gaedke-Merzhäuser, Lisa; Krainski, Elias Teixeira; Janalik, Radim; Rue, Haavard; Schenk, Olaf (arXiv, 2023-03-27) [Preprint]
      Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive Bayesian hierarchical models. In combination with the stochastic partial differential equations (SPDE) approach it gives rise to an efficient method for spatial-temporal modeling. In this work we build on the INLA-SPDE approach, by putting forward a performant distributed memory variant, INLA-DIST, for large-scale applications. To perform the arising computational kernel operations, consisting of Cholesky factorizations, solving linear systems, and selected matrix inversions, we present two numerical solver options, a sparse CPU-based library and a novel blocked GPU-accelerated approach which we propose. We leverage the recurring nonzero block structure in the arising precision (inverse covariance) matrices, which allows us to employ dense subroutines within a sparse setting. Both versions of INLA-DIST are highly scalable, capable of performing inference on models with millions of latent parameters. We demonstrate their accuracy and performance on synthetic as well as real-world climate dataset applications.
    • Towards black-box parameter estimation

      Lenzi, Amanda; Rue, Haavard (arXiv, 2023-03-27) [Preprint]
      Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. But the success of these approaches depends on simulating parameters that sufficiently reproduce the observed data, and, at present, there is a lack of efficient methods to produce these simulations. We develop new black-box procedures to estimate parameters of statistical models based only on weak parameter structure assumptions. For well-structured likelihoods with frequent occurrences, such as in time series, this is achieved by pre-training a deep neural network on an extensive simulated database that covers a wide range of data sizes. For other types of complex dependencies, an iterative algorithm guides simulations to the correct parameter region in multiple rounds. These approaches can successfully estimate and quantify the uncertainty of parameters from non-Gaussian models with complex spatial and temporal dependencies. The success of our methods is a first step towards a fully flexible automatic black-box estimation framework.
    • Nitrogen-Based Magneto-Ionic Manipulation of Exchange Bias in CoFe/MnN Heterostructures

      Jensen, Christopher; Quintana, Alberto; Quarterman, Patrick; Grutter, Alexander J.; Balakrishnan, Purnima P.; Zhang, Huairuo; Davydov, Albert V.; Zhang, Xixiang; Liu, Kai (Accepted by ACS Nano, 2023-03-27) [Article]
      Electric field control of the exchange bias effect across ferromagnet/antiferromagnet (FM/AF) interfaces has offered exciting potentials for low-energy-dissipation spintronics. In particular, the solid state magneto-ionic means is highly appealing as it may allow reconfigurable electronics by transforming the all-important FM/AF interfaces through ionic migration. In this work, we demonstrate an approach that combines the chemically induced magneto-ionic effect with the electric field driving of nitrogen in the Ta/Co0.7Fe0.3/MnN/Ta structure to electrically manipulate exchange bias. Upon field-cooling the heterostructure, ionic diffusion of nitrogen from MnN into the Ta layers occurs. A significant exchange bias of 618 Oe at 300 K and 1484 Oe at 10 K is observed, which can be further enhanced after a voltage conditioning by 5% and 19%, respectively. This enhancement can be reversed by voltage conditioning with an opposite polarity. Nitrogen migration within the MnN layer and into the Ta capping layer cause the enhancement in exchange bias, which is observed in polarized neutron reflectometry studies. These results demonstrate an effective nitrogen-ion based magneto-ionic manipulation of exchange bias in solidstate devices.