Dynamics and control of thin film surface microstructure in a complex deposition process

Ni, Dong; Christofides, Panagiotis D.

doi:10.1016/j.ces.2004.11.005

Cited by 23 publications

(15 citation statements)

References 28 publications

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“…These partial differential equations are simplified by neglecting certain terms, to obtain a Langevin equation with a multidimensional Gaussian stochastic process noise term (Kardar, Parisi, & Zhang, 1986). Other authors derive similar models (Ni & Christofides, 2005). As a first step, we will try some of these models, but we will also use a cellular automata model (Prieto-Langarica, Kojouharov, & Chen-Charpentier, 2013), to model the adsorption of molecules into the surface.…”

Section: Methodsmentioning

confidence: 99%

Ionic silicon improves endothelial cells' survival under toxic oxidative stress by overexpressing angiogenic markers and antioxidant enzymes

Monte

Cebe

Ripperger

et al. 2018

J Tissue Eng Regen Med

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Oxidative stress, induced by harmful levels of reactive oxygen species, is a common occurrence that impairs proper bone defect vascular healing through the impairment of endothelial cell function. Ionic silicon released from silica-based biomaterials, can upregulate hypoxia-inducible factor-1α (HIF-1α). Yet it is unclear whether ionic Si can restore endothelial cell function under oxidative stress conditions. Therefore, we hypothesized that ionic silicon can help improve human umbilical vein endothelial cells' (HUVECs') survival under toxic oxidative stress. In this study, we evaluated the ionic jsilicon effect on HUVECs viability, proliferation, migration, gene expression, and capillary tube formation under normal conditions and under harmful hydrogen peroxide levels. We demonstrated that 0.5-mM Si significantly enhanced angiogenesis in HUVECs under normal condition (p < 0.05). HUVECs exposed to 0.5-mM Si presented a morphological change, even without the bed of Matrigel, and formed significantly more tube-like structures than the control (p < 0.001). In addition, 0.5-mM Si enhanced cell viability in HUVECs under harmful H O levels. HIF-1α, vascular endothelial growth factor-A, and vascular endothelial growth factor receptor-2 were overexpressed more than twofold in silicon-treated HUVECs, under normal and toxic H O conditions. Moreover, the HUVECs were treated with 0.5-mM Si overexpressed superoxide dismutase-1 (SOD-1), catalase-1 (Cat-1), and nitric oxide synthase-3 (NOS3) under normal and oxidative stress environment (p < 0.01). A computational model was used for explaining the antioxidant effect of Si in endothelial cells and human periosteum cells by SOD-1 enhancement. In conclusion, we demonstrated that 0.5-mM Si can recover the HUVECs' viability under oxidative stress conditions by reducing cell death and upregulating expression of angiogenic and antioxidant factors.

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Section: Methodsmentioning

confidence: 99%

Ionic silicon improves endothelial cells' survival under toxic oxidative stress by overexpressing angiogenic markers and antioxidant enzymes

Monte

Cebe

Ripperger

et al. 2018

J Tissue Eng Regen Med

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show abstract

“…Then, there exist µ * > 0 and ε * > 0 such that if x f 0 2 + x s0 2 ≤ µ * and ε ∈ (0, ε * ], r f (∞) 2 , r s (∞) 2 and r(∞) 2 satisfy: (18) 45th IEEE CDC, San Diego, USA, Dec. [13][14][15]2006 WeB09.1 (20) where x f 0 and x s0 are the initial conditions for x f and x s in Eq.14, respectively, and λ cαi , λ cβ i (i = 1, 2, ··· , m) are the eigenvalues of Λ cs in the system of Eq.14.…”

Section: Analysis Of the Closed-loop Infinite-dimensional Systemmentioning

confidence: 99%

“…A methodology for feedback control of surface roughness using kMC models was developed in [12], [13] and was applied to control surface roughness in a GaAs deposition process [15]. Moreover, kMC models were also used to study the dynamics of complex deposition processes including multiple species with both short-range and longrange interactions and to perform predictive control design to control final surface roughness in [20].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Feedback Control of Surface Roughness Using a Stochastic PDE

Lou

Christofides

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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In this work, we develop a method for nonlinear feedback control of the roughness of a one-dimensional surface whose evolution is described by the stochastic KuramotoSivashinsky equation (KSE), a fourth-order nonlinear stochastic partial differential equation. We initially formulate the stochastic KSE into a system of infinite nonlinear stochastic ordinary differential equations by using modal decomposition. A finitedimensional approximation of the stochastic KSE is then derived that captures the dominant mode contribution to the surface roughness. A nonlinear feedback controller is then designed based on the finite-dimensional approximation to control the surface roughness. An analysis of the closed-loop nonlinear infinite-dimensional system is performed to characterize the closed-loop performance enforced by the nonlinear feedback controller in the closed-loop infinite-dimensional system. The effectiveness of the proposed nonlinear controller and the advantages of the nonlinear controller over a linear controller resulting from the linearization of the nonlinear controller around the zero solution are demonstrated through numerical simulations.

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“…The method was successfully applied to control surface roughness in a gallium arsenide (GaAs) deposition process model and to control complex deposition processes including multiple components with both short-range and long-range interactions (Ni and Christofides, 2005a). Furthermore, a method for computationally efficient optimization of thin film growth using coupled PDE and kMC models was developed (Varshney and Armaou, 2005).…”

Section: Introductionmentioning

confidence: 99%

Model parameter estimation and feedback control of surface roughness in a sputtering process

Lou

Christofides

2008

Chemical Engineering Science

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Dynamics and control of thin film surface microstructure in a complex deposition process

Cited by 23 publications

References 28 publications

Ionic silicon improves endothelial cells' survival under toxic oxidative stress by overexpressing angiogenic markers and antioxidant enzymes

Ionic silicon improves endothelial cells' survival under toxic oxidative stress by overexpressing angiogenic markers and antioxidant enzymes

Nonlinear Feedback Control of Surface Roughness Using a Stochastic PDE

Model parameter estimation and feedback control of surface roughness in a sputtering process

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