MODELING,SIMULATION,AND OPTIMIZATION OF SURFACE ACOUSTIC WAVE DRIVEN MICROFLUIDIC BIOCHIPS

We will be concerned with the mathematical modeling, numerical simulation, and shape optimization of micro fluidic biochips that are used for various biomedical applications. A particular feature is that the fluid flow in the fluidic network on top of the biochips is in- duced by surface acoustic waves generated by interdigital transducers. We are thus faced with a multiphysics problem that will be modeled by coupling the equations of piezoelectricity with the compressible Navier-Stokes equations. Moreover, the fluid flow exhibits a multiscale character that will be taken care of by a homogenization approach. We will discuss and analyze the mathematical models and deal with their numerical solution by space-time discretizations featuring appropriate finite element approximations with respect to hierarchies of simplicial triangulations of the underlying computational domains. Simulation results will be given for the propagation of the surface acoustic waves on top of the piezoelectric substrate and for the induced fluid flow in the microchannels of the fluidic network. The performance of the operational behavior of the biochips can be significantly improved by shape optimization. In particular, for such purposes we present a multilevel interior point method relying on a predictor-corrector strategy with an adaptive choice of the continuation steplength along the barrier path. As a specific example, we will consider the shape optimization of pressure driven capillary barriers between microchannels and reservoirs.     

Shape functional optimization with restrictions boosted with machine learning techniques

Shape optimization is a widely used technique in the design phase of a product. Current ongoing improvement policies require a product to fulfill a series of conditions from the perspective of mechanical resistance, fatigue, natural frequency, impact resistance, etc. All these conditions are translated into equality or inequality restrictions which must be satisfied during the optimization process that is necessary in order to determine the optimal shape. This article describes a new method for shape optimization that considers any regular shape as a possible shape, thereby improving on traditional methods limited to straight profiles or profiles established a priori. Our focus is based on using functional techniques and this approach is, based on representing the shape of the object by means of functions belonging to a finite-dimension functional space. In order to resolve this problem, the article proposes an optimization method that uses machine learning techniques for functional data in order to represent the perimeter of the set of feasible functions and to speed up the process of evaluating the restrictions in each iteration of the algorithm. The results demonstrate that the functional approach produces better results in the shape optimization process and that speeding up the algorithm using machine learning techniques ensures that this approach does not negatively affect design process response times.     

Region of interest and pixel-by-pixel analysis of dynamic contrast enhanced magnetic resonance imaging parameters and time-intensity curve shapes: a comparison in chondroid tumors

Dynamic contrast enhanced (DCE) MRI is widely acknowledged to be a helpful tool in the diagnosis and differentiation of tumors. In common clinical settings, the dynamic changes described by the time-intensity curves (TICs) are evaluated to find patterns of atypical tissue behavior, i.e., areas characterized by rapid contrast wash-in and wash-out. Despite the ease of this approach, there is no consensus about the specificity of the TIC shapes in discriminating tumor grades. We explore a new way of looking at TICs, where these are not averaged over a selected region of interest (ROI), but rendered pixel-by-pixel. In this way, the characteristic of the tissue is not given as a single TIC classification but as a distribution of the different TIC patterns. We applied this method in a group of patients with chondroid tumors and compared its outcome with the outcome of the standard ROI-based averaged TIC analysis. Furthermore, we focused on the problem of ROI selection in these tumors and how this affects the outcome of the TIC analysis. Finally, we investigated what relationship exists between the "standard" DCE-MRI parameter maximum enhancement (ME) and the TIC shape. CONCLUSIONS: We demonstrate that, where the ROI approach fails to show the presence of areas of rapid contrast wash-in and wash-out, the pixel-by-pixel approach reveals the coexistence of a heterogeneous pattern of TIC shapes. Secondly, we point out the differences in the DCE MRI parameters and tumor volume that can result when selecting the tumor based on DCE parameter maps or post-contrast T1-weighted images. Finally, we show that ME maps and TIC shape maps highlight different tissue areas and, therefore, the use of the ME maps is not appropriate for the correct identification of areas of atypical TICs.     

A method for the study of surface segregation in multicomponent alloys

A simple algorithm for the determination of segregation profiles in multicomponent systems based on a mean field formalism and a quantum approximate method for the energetics is introduced. The method is described and applied to two ternary systems, concentrating on the changes in segregation patterns relative to the corresponding binary cases.     

Density-functional study of the CO chemisorption on bimetallic Pd–Sn(1 1 0) surfaces

We study the ordered PdSn c(2 × 2), (2 × 1), and PdSn₂ (3 × 1) overlayers deposited on Pd(1 1 0) by using first-principles density-functional calculations. It appears that the two PdSn structures are almost degenerate in the energy. Pd–Sn surfaces we consider do not display the marked buckling with Sn atoms displaced towards vacuum that is common for Pt–Sn surfaces. Low-coverage CO chemisorption at these overlayers and on analogous surface structures on Pd₃Sn is considered. It is shown that inclusion of an empirical correction to the CO adsorption energy changes the stable adsorption site from the long-bridge to the top one in most cases. The adsorption energy decreases with the number of Sn atoms in the vicinity of the adsorption site, and this property correlates well with the position of the centre of gravity of the local Pd d-electron band, and also with the variation of the local density of d-electron states at the Fermi level. The centre-of-gravity value is used to assess the core-level shifts for Pd atoms in various geometries. Most of the calculated data compare rather well with the recent measurements on Pd–Sn overlayers at Pd(1 1 0) as well as with other data on related bimetallic systems.     

Equilibrium ordering properties of Au–Pd alloys and nanoalloys

Equilibrium configurations of bulk and surface Au–Pd alloys as well as of nanoalloy clusters are studied using Metropolis Monte Carlo importance sampling and the embedded atom method. The clusters contain about 1000 atoms. Three ordered bulk phases are predicted at low temperature, centred on compositions around 25%, 50%, and 75% Pd. The predicted order–disorder transition temperatures partially disagree with the available experimental results, but they are in good agreement with ab initio calculations. Surface enrichment in Au is systematically predicted, accompanied by partial subsurface enrichment in Pd, best enhanced around the equiatomic overall composition. The subsurface enrichment in Pd is suggested to play a decoupling role between surface and bulk conditions and, subsequently, ordered surface structures not induced by the order in the bulk are predicted at low temperatures. Clusters display similar segregation and ordering properties as flat infinite surfaces. However, the stability of the ordering at cluster surfaces is not globally characterized. The order–disorder transitions in the clusters occur at temperatures between 50 and 100 K lower than in the bulk. The disorder appears at the surface and proceeds to the core as the temperature is increased.     

Alloy formation in the Au{1 1 1}/Ni system - An investigation with scanning tunnelling microscopy and medium energy ion scattering

Using the techniques of scanning tunnelling microscopy (STM) and medium energy ion scattering (MEIS), we examine the growth and annealing behaviour of ultrathin Ni films on Au{1 1 1} at 300 K. As has been shown previously, submonolayer growth of Ni on Au{1 1 1} is strongly influenced by the presence of the herringbone reconstruction with two-dimensional clusters nucleating at herringbone elbows. Second layer growth commences prior to the completion of the monolayer. After multiple layers have been deposited, the surface morphology retains a similar cluster-like appearance. Annealing produces surfaces exhibiting long range Moiré structures and, at higher temperature, triangular misfit dislocations. We use MEIS to examine the composition and structure of these surface alloy phases and conclude that in each case, they consist of an essentially pure Au surface layer on a bimetallic second layer.     

Synthesis of nearly monodisperse nanoparticles in alcohol: A pressure and solvent-induced low-temperature strategy

Nearly monodisperse nanoparticles were synthesized by an environmentally benign low-temperature (140-180 °C) method involving pressure-induced decomposition of metal oleates in alcohol. XRD and TEM were employed in the characterization of the samples. In this study, Fe₃O₄, CoO, MnO, CoFe₂O₄, MnFe₂O₄, and a mixture of Ni, NiO, Ni₂O₃ nanoparticles, exhibiting various shapes and assemblies, were obtained.     

多溶剂响应自折叠水凝胶

本文基于寡聚乙二醇甲醚甲基丙烯酸酯(OEGMA)和2-甲基-2-丙烯酸-2-(2-甲氧基乙氧基)乙酯(MEO₂MA)设计了一种自折叠水凝胶. 在室温下,P(MEO₂MA-co-OEGMA)凝胶能够在多种溶剂中自折叠形成三维结构. 凝胶不均匀膨胀导致内应力增强,从而使得片状凝胶可以在三维结构和二维形状之间进行可逆转换. 研究表明,凝胶组成、固化气体环境和成型过程对水凝胶的自折叠过程起着重要的影响.     

ZnO/FLC nanocomposites with low driving voltage and non-volatile memory for information storage applications

Nanocomposites comprises of nano sized zinc oxide (ZnO, size ~10 nm) particles and ferroelectric liquid crystal (FLC) material have been prepared and studied. The concentration of the dopant (ZnO nanoparticles) in FLC matrix varied from 0.0 to 1 wt%. Distinct nanocomposites have been used to fabricate memory device and studied through dielectric spectroscopy, optical microscopy and electro-optical methods to explore electro-optic and memory parameters. Experimental results reveal that doping of ZnO nanoparticles in FLC leads to long lasting memory and low deriving voltage in nanocomposites. The achieved prolonged memory effect in nanocomposites may attribute to the charge transfer at the interfaces. Another reason, application of applied field may lead to ion trapping effect at ZnO surface which in turn minimize the depolarized field and enhance the memory effect in nanocomposites.