In this letter, we report a quasi-vertical GaN Schottky barrier diode (SBD) fabricated on a hetero-epitaxial layer on silicon with low dislocation density and high carrier mobility. The reduction of dislocation is realized by inserting a thin layer with high density of Ga vacancies to promote the dislocation bending. The dislocation density is $1.6\times 10^8$ cm?2 with a GaN drift layer thickness of $4.5 μ \textm$ . The fabricated prototype GaN SBD delivers a high on/off current ratio of $10^10$ , a high forward current density of 1.6 kA/cm2@3 V, a low specific on-resistance of 1.1 $\textmØmega \cdot \text cm^2$ , and a low ideality factor of 1.23.
Degradation of phenols with different substituent groups (including –OCH3, –CHO, –NHCOCH3, –NO2, and −Cl) at boron-doped diamond (BDD) anodes has been studied previously based on the removal efficiency and •OH detection. Innovatively, formations of CO2 gas and various inorganic ions were examined to probe the mineralization process combined with quantitative structure-activity relationship (QSAR) analysis. As results, all phenols were efficiently degraded within 8 h with high COD removal efficiency. Three primary intermediates (hydroquinone, 1,4-benzoquinone and catechol) were identified during electrochemical oxidation and degradation pathway was proposed. More importantly, CO2 transformation efficiency ranked as: no N or Cl contained phenols (p-CHO, p-OCH3 and Ph) > N-contained phenols (p-NHCOCH3 and p-NO2) > Cl-contained phenols (p-Cl and o,p-Cl). Carbon mass balance study suggested formation of inorganic carbon (H2CO3, CO32− and HCO3−) and CO2 after organic carbon elimination. Inorganic nitrogen species (NH4+, NO3− and NO2−) and chlorine species (Cl−, ClO3− and ClO4−) were also formed after N- and Cl-contained phenols mineralization, while no volatile nitrogen species were detected. The phenols with electron-withdrawing substituents were easier to be oxidized than those with electron-donating substituents. QSAR analysis indicated that the reaction rate constant (k1) for phenols degradation was highly related to Hammett constant (∑σo,m,p) and energy gap (ELUMO - EHOMO) of the compound (R2 = 0.908), which were key parameters on evaluating the effect of structural moieties on electronic character and the chemical stability upon radical attack for a specific compound. This study presents clear evidence on mineralization mechanisms of phenols degradation at BDD anodes.
This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method Weatheritt and Sandberg (2016) [8], but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited. Furthermore, the resulting model, which is the one providing the most accurate CFD results at the end of the training, inherently shows good performance in RANS calculations. To demonstrate the potential of this new method, the CFD-driven machine learning approach is applied to model development for wake mixing in turbomachines. A new model is trained based on a high-pressure turbine case and then tested for three additional cases, all representative of modern turbine nozzles. Despite the geometric configurations and operating conditions being different among the cases, the predicted wake mixing profiles are significantly improved in all of these a posteriori tests. Moreover, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is predominantly due to the extra diffusion introduced by the CFD-driven model.
Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain–machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.
Uranium is one of the most commonly detected radionuclides in the environment. Of the two most predominant oxidation states, U(VI) is much more soluble, mobile and toxic than U(IV). Consequently, converting U(VI) to U(IV) can facilitate the removal of U from water and reduce its mobility and biological exposure. In this work, stabilized zero-valent iron (ZVI) nanoparticles were prepared using starch or carboxymethyl cellulose (CMC) as stabilizers and then tested for reductive removal of U(VI) from simulated groundwater. Nearly 100% removal of U(VI) (initial U = 25 mg/L) was achieved using CMC-stabilized ZVI (Fe = 35 mg/L) at pH 6. In pH range of 6–9, the lower pH favored the reaction. CMC-ZVI nanoparticles presented better deliverability than starch-ZVI, while bare ZVI nanoparticles was almost trapped in the soil column. CMC-ZVI worked effectively in the presence of a model humic acid (up to 10 mg/L as TOC) and bicarbonate (1 mM), though higher dosages of the ligands inhibited U(VI) removal. After treatment, no re-mobilization of U was detected when aged for 6 months under anoxic conditions and the addition of strong ligands only remobilized U(VI). When exposed to oxic conditions, the immobilized U will be partially oxidized and remobilized due to the ingress of atmospheric O2 and CO2. In terms of toxicity reduction, the ZVI treated U had almost no inhibition for natural bacteria activity, while dissolved U(VI) showed significant inhibitive effects. The CMC-ZVI nanoparticles may serve as effective reactive materials to facilitate immobilization of U(VI) in groundwater, which in turn can greatly mitigate the human exposure and toxic effects of U on biota.