Zinc-Imidazolate Films as an All-Dry Resist Technology

Motivated by the drawbacks of solution phase processing, an all-dry resist formation process is presented that utilizes amorphous zinc-imidazolate (aZnMIm) films deposited by atomic/molecular layer deposition (ALD/MLD), patterned with electron beam lithography (EBL), and developed by novel low temperature (120 °C) gas phase etching using 1,1,1,5,5,5-hexafluoroacetylacetone (hfacH) to achieve well-resolved 22 nm lines with a pitch of 30 nm. The effects of electron beam irradiation on the chemical structure and hfacH etch resistance of aZnMIm films are investigated, and it is found that electron irradiation degrades the 2-methylimidazolate ligands and transforms aZnMIm into a more dense material that is resistant to etching by hfacH and has a C:N:Zn ratio effectively identical to that of unmodified aZnMIm. These findings showcase the potential for aZnMIm films to function in a dry resist technology. Sensitivity, contrast, and critical dimensions of the patterns are determined to be 37 mC cm⁻², 0.87, and 29 nm, respectively, for aZnMIm deposited on silicon substrates and patterned at 30 keV. This work introduces a new direction for solvent-free resist processing, offering the prospect of scalable, high-resolution patterning techniques for advanced semiconductor fabrication processes.     

Screening and selective quantification of illicit drugs in wastewater by mixed-mode solid-phase extraction and quadrupole-time-of-flight liquid chromatography-mass spectrometry

For the first time, a mixed-mode solid-phase extraction with fractionation of basic analytes from neutral and acidic species during cartridge elution and liquid chromatography-quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) was combined for the quantitative determination of 24 illicit drugs and metabolites in urban sewage samples. The effects of several sample preparation and instrumental parameters in the sensitivity and selectivity of the quantitative method are thoroughly discussed. Under final working conditions, recoveries above 63% and 82% were attained for all species in raw and treated sewage, respectively; whereas, the limits of quantification of the method, defined for a signal-to-noise of 10 (S/N = 10), ranged from 2 to 50 ng L(-1). Sequential elution of mixed-mode cartridges allowed a significant reduction of matrix effects observed during electrospray ionization of basic drugs versus those measured for hydrophilic balance reversed-phase sorbents and the same mixed-mode polymer without fractionated elution. Analysis of raw wastewater samples confirmed the ubiquity of cocaine (COC), benzoylecgonine (BE), and 11-nor-9-carboxy-Δ(9)-tetrahydrocannabinol (THCCOOH) in this matrix. The capability of the above methodology to identify new illicit drugs and/or metabolites in sewage samples is also discussed. With this aim, a two step strategy is proposed. First, high-resolution MS chromatograms, acquired throughout each chromatographic run, are automatically searched against an in-house built database, a reduced list of candidate drugs is generated, and the corresponding extracted ion chromatograms are obtained. In a further LC run, the tandem mass spectrometry (MS/MS) spectra of unknown peaks are acquired using different collision energies and compared with those existing in public libraries, or interpreted, to assign the unknown peak to one of the previously selected candidates.     

UV-Visible Spectroscopy and Multivariate Classification as a Screening Tool for Determining the Adulteration of Sauces

A rapid, simple, and economic multivariate screening methodology based on UV-visible spectroscopy and multivariate classification is proposed to test for adulteration in sauces. Two classification strategies were evaluated to compare their ability to detect food fraud: untargeted modeling (class modeling) and targeted classification (discriminant analysis). As a case study, the possible adulteration of ketchups and barbecue sauces with the banned Sudan I dye was considered. The classification models were built with a new classification tool for class modeling (partial least squares-density modeling, PLS-DM) and with the classical discriminant partial least squares-discriminant analysis (PLS-DA). Very satisfactory classification results were obtained with both strategies: regarding untargeted modeling, only original samples (class 1) were modeled obtaining a 94.5 % of correct classification and regarding targeted classification, two classes were considered (class 1 original samples and class 2 adulterated samples) with an overall classification rate of 97.3 %. The two strategies are useful and adequate as screening tools for monitoring the quality of sauces especially in situations that require quick responses.     

Structural Ultrafine Grained Steels Obtained by Advanced Controlled Rolling

Steels with ultrafine grains (lower than 5 μm), which usually known as ultrafine ferrite or ultrafine grained materials, are presently the object of intense research, because of the improvement in resistance and fracture toughness they may reach compared to conventional steels (with grain sizes above this value). It is shown that the forenamed steels designated in the Euronorm EN 10149-2, which are manufactured by advanced techniques of controlled rolling and mainly used in automotive industry, have an ultrafine grain size in the range of 2.5 to 3.5 μm, and with elastic yield stresses higher than 400 MPa. Based on the Morrison-Miller criterion, it is shown that values of the strain-hardening coefficient lower than 0.08 would make the industrial application of these steels unfeasible.     

Modeling power and energy consumption of dense matrix factorizations on multicore processors

In this paper, we propose a model for the energy consumption of the concurrent execution of three key dense matrix factorizations, with task parallelism leveraged via the Symmetric Multi‐Processing Superscalar (SMPSs) runtime, on a multicore processor. Our model decomposes the power dissipation into the system, static and dynamic components, with the former two being estimated from basic, off‐line experiments. The dynamic power, on the other hand, requires significantly more care, and we introduce a contention‐aware model that accommodates for the variability of power consumption due to memory contention. Experimental results on an Intel Xeon E5504 processor with four cores, using an internal powermeter that samples the power drawn by the mainboard with a frequency of 1 KHz, show the reliability of the energy model for the Cholesky, LU, and QR factorizations on this platform. Copyright © 2013 John Wiley & Sons, Ltd.