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We have investigated the microelectrical properties of CdTe thin films using scanning Kelvin probe force microscopy (SKPFM) and scanning spreading resistance microscopy (SSRM). Two films with the configurations of substrate and superstrate were subjected to the characterization studies. The electrical potential and resistance were properly mapped with the substrate film but not with the superstrate film because the underlying CdS/CdTe junction largely impacted the characterizations. The higher SKPFM potential on grain boundaries (GBs) of the substrate film than on the grain surface indicates positively charged GBs and upward band bending around the GB; therefore, the GBs are either depleted or inverted. The SSRM resistance mapping on this film shows nonuniformities and features that are associated with the grain structure and facets. However, the GBs do not exhibit distinct characteristic resistance. Comparing the low resistance channel along the GBs of high-performance CIGS films, the SSRM mapping of CdTe supports depletion of the GBs. In SSRM measurement, it is critical to adequately indent the probe to the film, and to apply a bias voltage larger than the onset voltage of the probe/film barrier, so that the contact resistance is minimized and that the local spreading resistance of CdTe film beneath the probe is measured.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

A new type of photovoltaic system with higher generation power density has been studied in detail. The feature of the system is a V-shaped module (VSM) with two tilted monocrystalline solar cells. Compared to solar cells in a flat orientation, the VSM enhances external quantum efficiency and leads to an increase of 31% in power conversion efficiency. Due to the VSM technique, short-circuit current density was raised from 24.94 to 33.7mA/cm(2), but both fill factor and open-circuit voltage were approximately unchanged. For the VSM similar results (about 30% increase) were obtained for solar cells fabricated by using mono-crystal line silicon wafers with only conventional background impurities. (c) 2004 Elsevier B.V. All rights reserved.

The monitoring of a waste separation process in the nuclear power industry is considered. Recent advances in gamma ray emission and electrical impedance tomography mean that it is now feasible to unite these two modalities into a novel dual-modality monitoring method. This paper considers a simple model problem for the identification of a boundary between two distinct waste streams in a semi-continuous rotation separator. The simplicity of the problem affords the opportunity to demonstrate the general feasibility of the approach whilst avoiding unnecessary complications.

In the search for a nontoxic replacement of the commonly employed CdS buffer layer for Cu(In,Ga)(S,Se) $_\mathrm{2}$ based solar cells, chemically deposited Zn(O,S) thin films are a most promising choice. In this paper, we address the usually slow deposition speed of Zn(O,S) in a newly developed ammonia-free chemical bath process, resulting in a deposition of 30 nm in 3 min with good homogeneity on 30 cm × 30 cm sized substrates. Solar cells with buffer layers prepared from this process match the efficiency of CdS reference cells. In a second step, we address the light-soaking post-treatment, still needed for maximum efficiencies. By addition of aluminum to the deposition process, the initial efficiencies can be increased slightly. With the addition of boron, the light-soaking post-treatment is rendered unnecessary, while maintaining high efficiencies above 15%, surpassing reference cells with CdS buffer.

AbstractIron-acceptor (FeAc) pair association has been studied in compensated n-type silicon. A dynamic approach, based on the charge carrier recombination rates over the Fei trap level, leads to an explanation of the observed FeAc pairing reaction in compensated n-type silicon and extends the understanding of FeAc pairing kinetics. Association kinetics was used to measure a height dependent acceptor concentration profile. Even in compensated n-type silicon good agreement with expected concentrations is found.

Coupling between the UEDGE (edge fluid model), GINGRED (grid generation) and CAKE (equilibrium reconstruction) codes opens the door for automated interpretative scrape-off-layer (SOL) analysis over entire discharges, providing information that is essential in efforts to couple the SOL to core transport codes. In this work, we utilize new developments in the autoUEDGE code (Izacard et al. 2018) to investigate the behavior of the DIII-D SOL during the temporal evolution of an edge-localized mode (ELM) cycle. Modeled temperature and density profiles in UEDGE are automatically matched to experimental measurements by iteratively and self-consistently adjusting transport coefficient profiles in the plasma edge. This analysis is completed over multiple ELM cycles of a well-diagnosed discharge with long (∼100ms) inter-ELM periods. Directly after the ELM crash, a short period of high-density, low-temperature conditions is observed in Langmuir probe measurements at the outer divertor. This regime is associated with enhanced Dαemission and incident particle flux, suggesting that the divertor enters a period of high recycling after an ELM crash. After about ∼25ms, divertor conditions return to their pre-ELM conditions and remain there for several tens of milliseconds. Using the autoUEDGE code, the SOL is modeled as a function of ELM cycle using upstream profiles as input. The 2D modeling successfully reproduces both divertor Thomson scattering measurements and the experimentally observed divertor dynamics. Though the recycling is kept fixed throughout the modeling, changes in particle fluxes are consistent with local experimental recycling changes induced by ELMs. Agreement between modeling and observation suggests a strong link between upstream profiles and the high-recycling divertor conditions directly following large type-I ELMs.