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Ion selective membrane (ISM) is widely used in electrochemical engineering and micro-fluidic processes, yet accurate modeling of the ISM is still challenging due to many scientific issues. So far, assumptions on the "ideal ISM" have been used in most simulation studies involving ion transport and electrokinetic flow in ion concentration polarization systems, but the validity or accuracy of those assumptions has never been investigated. In this paper, using a two-dimensional nanochannel system with practical significance, we verify the validity of the ideal ISM model by making comparisons between the idealized ISM system and a more realistic permselective nanochannel system in terms of simplifications over the electrical potential, counter-ion concentration, and zero co-ion flux. Our results show that the simplifications of fixed voltage and fixed counter-ion concentration in the ideal ISM model are largely accurate in most situations, especially under high applied voltage and/or with high charge density inside the ISM. However, zero co-ion flux simplification is not exactly accurate in most occasions. Significant errors may be incurred by the zero co-ion flux assumption when steady state solutions are sought using the ISM model. Some discussions over the influences of structures of the nanochannel system are also added. The obtained results will help in obtaining detailed understanding of the transport features inside the nanoporous ISM, especially when the comparison between simulation and experimental data is necessary. [ABSTRACT FROM AUTHOR]

Imperfections during the manufacturing process can cause significant variations in the noise and vibration levels exhibited by nominally identical structures. Any response calculations employed during the design process should ideally take account of these uncertainties and predict the expected range in performance. Recently a hybrid method has been developed to predict the ensemble average response of a built-up system by combining a deterministic model of parts of the system with a statistical model of other components [Shorter, P. J., and Langley, R. S. (2005) J. Sound. Vib., 288, 669–700]. In this paper the method is extended to predict the ensemble variance of the response. Expressions are derived for the variance of the vibrational energies in the statistical components, and for the variance of the cross spectrum of the response of the deterministic components, which augment the mean values of these quantities predicted by the original theory. The method employs a nonparametric model of uncertainty, in the sense that the statistical components are taken to carry diffuse wave fields, and this obviates the requirement for a detailed description of the system uncertainties. The method is validated by application to a range of coupled plate structures, and good agreement with detailed Monte Carlo simulations is found. [ABSTRACT FROM AUTHOR]

Trends in device miniaturization have driven the adoption of new materials that, in turn, have enabled significant advancements in the field of process engineering and integration for semiconductor technology. Continued progress for device scaling is necessary and can be enabled by advances in lithographic techniques and deposition schemes. Thin-film deposition for spacers and etch stop layers has become a mainstay to enable and extend traditional 2D scaling into the 3D realm for fabricating advanced semiconductor devices. For processing 3D structures, controlled film deposition with subnanometer resolution in high aspect ratio features is desired. Area selective deposition (ASD) can be a powerful response to such a challenge. ASD is a type of thin-film deposition technique scheme that can be used to eliminate the need for several expensive and time-consuming lithography steps with fewer performance penalties. In this work, we show ASD of ruthenium (Ru) on 3D molybdenum (Mo)–silicon oxide (SiO2) stacks by utilizing the inherent substrate preference of the Ru precursor to a H-terminated surface. In the best selectivity condition, our results show growth of ∼5 nm Ru on Mo, with no film growth on SiO2. Changes in Ru growth kinetics were observed after dilute hydrofluoric acid (DHF) treatment for both surfaces. Post-DHF treatment, the Ru growth rate on Mo was reduced by 5%. However, on SiO2 (after incubation delay), the growth rate was reduced by 94% compared to untreated surfaces. This translates to a very high difference in the growth rate of Ru on Mo vs SiO2, even after considering the incubation delay. Finally, by using 3D topologies with high aspect ratio holes, we have highlighted that it is important to deconvolute the effects of precursor depletion and selectivity. To the best of our knowledge, this is the first demonstration of ASD of Ru on 3D structures without the use of any blocking layers. Therefore, these results demonstrate a new paradigm for ASD in 3D features. [ABSTRACT FROM AUTHOR]

Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) is a sophisticated technique to synthesize high-temperature superconductor (HTS) materials. ALL-MBE produces single-crystal HTS films with atomically smooth surfaces and interfaces, as well as precise multilayer heterostructures engineered down to a single atomic layer level. This enables the fabrication of tunnel junctions, nanowires, nanorings, and other HTS devices of interest. Our group has focused on ALL-MBE synthesis and materials science of La2−xSrxCuO4 (LSCO), a representative HTS cuprate. In the past two decades, we have synthesized over three thousand LSCO thin films and characterized them by a range of analytical techniques. Here, we present in full detail a systematic process for the synthesis and engineering of atomically perfect LSCO films. The procedure includes the preparation of substrates, calibration of the elemental sources, the recipe for ALL growth of LSCO films without any secondary-phase precipitates, post-growth annealing of the films, and ex situ film characterization. This report should aid replication and dissemination of this technique of synthesizing single-crystal LSCO films for basic research as well as for HTS electronic applications. [ABSTRACT FROM AUTHOR]

Plasma is found almost everywhere in the universe. Yet most people are not conscious of its presence, nor the role it plays in creating the electronic devices in our everyday lives. Virtually every semiconductor chip has been touched by plasma, which is used in nearly half of all semiconductor manufacturing steps today. These chips power our mobile phones, computers, automobiles, and are vital for making artificial intelligence a reality. With a reputation of mystery, the world of plasma processing has largely remained hidden from the consumer's view. What does plasma do in chipmaking? Why is it so critical? What does a process engineer do? To give the reader a sense of the importance that plasma plays in the electronics industry and how its energy has been harnessed into producing chips, this perspective aims to make this technology more relatable as it continues to change our everyday lives. [ABSTRACT FROM AUTHOR]

A new lab activity for first-year introductory physics teaches students to use data to drive decision-making in science and engineering processes. Using the popular PDCA (plan-do-check-act) cycle, students manufacture a small sample of ball bearings out of modeling clay. By statistically analyzing their sample, they determine whether a larger shipment will meet tolerance levels specified by the lab TA. They then make decisions on ways to change their manufacturing process to improve results, employing another round of data analysis to confirm whether the change improved their process. Judging by student comments, such an activity reinforces the conceptual basis for numerous statistical properties, helps distinguish many commonly confused statistical concepts, and reinforces the use of data in process management. This activity can be incorporated into either algebra-based or calculus-based physics labs and, because it does not rely on background knowledge of physics concepts, should prove ideal for the early weeks of lab instruction. [ABSTRACT FROM AUTHOR]