Electric-field switching of two-dimensional van der Waals magnets

Controlling magnetism by purely electrical means is a key challenge to better information technology1. A variety of material systems, including ferromagnetic (FM) metals2,3,4, FM semiconductors5, multiferroics6,7,8 and magnetoelectric (ME) materials9,10, have been explored for the electric-field control of magnetism. The recent discovery of two-dimensional (2D) van der Waals magnets11,12 has opened a new door for the electrical control of magnetism at the nanometre scale through a van der Waals heterostructure device platform13. Here we demonstrate the control of magnetism in bilayer CrI3, an antiferromagnetic (AFM) semiconductor in its ground state12, by the application of small gate voltages in field-effect devices and the detection of magnetization using magnetic circular dichroism (MCD) microscopy. The applied electric field creates an interlayer potential difference, which results in a large linear ME effect, whose sign depends on the interlayer AFM order. We also achieve a complete and reversible electrical switching between the interlayer AFM and FM states in the vicinity of the interlayer spin-flip transition. The effect originates from the electric-field dependence of the interlayer exchange bias.Comment: 12 pages, 4 figure

New aspects of operando Raman spectroscopy applied to electrochemical CO2 reduction on Cu foams

The mechanism of electrochemical CO2 reduction (CO2RR) on copper surfaces is still insufficiently understood. Operando Raman spectroscopy is ideally suited to elucidate the role of adsorbed reaction intermediates and products. For a Cu foam material which has been previously characterized regarding electrochemical properties and product spectrum, 129 operando spectra are reported, covering the spectral range from 250 to 3300 cm−1. (1) The dendritic foam structure facilitates surface-enhanced Raman spectroscopy (SERS) and thus electrochemical operando spectroscopy, without any further surface manipulations. (2) Both Raman enhancement and SERS background depend strongly on the electric potential and the “history” of preceding potential sequences. (3) To restore the plausible intensity dependencies of Raman bands, normalization to the SERS background intensity is proposed. (4) Two distinct types of *CO adsorption modes are resolved. (5) Hysteresis in the potential-dependent *CO desorption supports previous electrochemical analyses; saturating *CO adsorption may limit CO formation rates. (6) HCO3− likely deprotonates upon adsorption so that exclusively adsorbed carbonate is detectable, but with strong dependence on the preceding potential sequences. (7) A variety of species and adsorption modes of reaction products containing C—H bonds were detected and compared to reference solutions of likely reaction products, but further investigations are required for assignment to specific molecular species. (8) The Raman bands of adsorbed reaction products depend weakly or strongly on the preceding potential sequences. In future investigations, suitably designed potential protocols could provide valuable insights into the potential-dependent kinetics of product formation, adsorption, and desorption

Identifying and Characterizing Micro-machining Signatures on Freeform Surfaces Using Morphological Methods

Freeform surfaces are replacing traditional surfaces and have significantly reduced volume and weight and highly improved performance in modern complex optic systems, bio-systems and other disciplines [1]. These high-precision freeform components are enabled by state-of-the-art micro-machining technologies, compromising mechanical methods (diamond turning and polishing etc.), physical methods (laser beam and ion beam machining), and chemical methods (lithography, electro-chemical machining etc.). However, a fundamental pre-requisite to achieve the potential growth to these high-added value freeform components is to measure and characterize these components with the required accuracy such that their manufacturing quality can be controlled. The surface topography is a fingerprint of all process stages of the manufacturing process. Thus identifying and evaluating these topographical features on freeform surfaces left by production techniques are critically important in that they could present an indication of the manufacturing quality and offer feedback to the process control

Surge pricing on a service platform under spatial spillovers: evidence from Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price spillover. There is a debate in the management community on the ecacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.First author draf

Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy