Numerical Study of Circularly Slotted Highly Sensitive Plasmonic Biosensor : A Novel Approach

Funding Information: This work was supported by the Deanship of the Scientific Research ( DSR ), King Abdulaziz University , Jeddah, under grant No. ( DF-773-135-1441 ). The authors, therefore, gratefully acknowledge DSR technical and financial support.Peer reviewe

Toward a model-based predictive controller design in brain-computer interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS)

DATA MINING OF EXPERIMENTAL CORROSION DATA USING NEURAL NETWORK

The objective of this work is the mining of existing experimental databases on metals and alloys to predict the corrosion resistance and behavior of metals and alloys over extended periods of time. The data mining is aimed at establishing the conditions under which certain parameter sets (i.e. Ph, temperature, time of exposure, electrolyte composition, metal composition, metallographic characteristics, etc.) may impact the alloy's localized resistance characteristics. The data mining results allow them to categorize and prioritize those parameters for which the alloy may be at risk of general and/or localized corrosion attacks. it will also help us to understand, along with the information gained through theoretical models, the synergetic effects of those variables on electrochemical potentials and corrosion rates (i.e., pitting, crack, and crevice growth rates). To accomplish the objective corrosion-related data on corrosion allowable, as well as corrosion resistive, alloys was collected for both DC and AC corrosion experiments from studies of general and localized corrosion. Collected data was transformed according to the corrosion failure modes and variables. The transformed data was checked for consistency and missing values and cleansed, as per requirements. Data from multiple experiments, figures and tables that represent the same corrosion variables were integrated into a single database for further analysis. Neutral Network (NN) Backpropagation method was used to fit a preliminary model to the collected (mostly experimental) data. NN models were tested on available experimental data on corrosion allowable alloys to predict the life of the metals/alloys. NN models were also used to predict future corrosion rates for user-specified conditions and time frames. This work is part of a multi-university Corrosion Cooperative of the DOE-OCRWM Science and Technology Program established to enhance the understanding of corrosion processes and materials performance

DATA MINING OF EXPERIMENTAL CORROSION DATA USING NEURAL NETWORK

The objective of this work is the mining of existing experimental databases on metals and alloys to predict the corrosion resistance and behavior of metals and alloys over extended periods of time. The data mining is aimed at establishing the conditions under which certain parameter sets (i.e. Ph, temperature, time of exposure, electrolyte composition, metal composition, metallographic characteristics, etc.) may impact the alloy's localized resistance characteristics. The data mining results allow them to categorize and prioritize those parameters for which the alloy may be at risk of general and/or localized corrosion attacks. it will also help us to understand, along with the information gained through theoretical models, the synergetic effects of those variables on electrochemical potentials and corrosion rates (i.e., pitting, crack, and crevice growth rates). To accomplish the objective corrosion-related data on corrosion allowable, as well as corrosion resistive, alloys was collected for both DC and AC corrosion experiments from studies of general and localized corrosion. Collected data was transformed according to the corrosion failure modes and variables. The transformed data was checked for consistency and missing values and cleansed, as per requirements. Data from multiple experiments, figures and tables that represent the same corrosion variables were integrated into a single database for further analysis. Neutral Network (NN) Backpropagation method was used to fit a preliminary model to the collected (mostly experimental) data. NN models were tested on available experimental data on corrosion allowable alloys to predict the life of the metals/alloys. NN models were also used to predict future corrosion rates for user-specified conditions and time frames. This work is part of a multi-university Corrosion Cooperative of the DOE-OCRWM Science and Technology Program established to enhance the understanding of corrosion processes and materials performance

Parameter sensitivity analysis of pit initiation at single sulfide inclusions in stainless steel

Sensitivity analysis methods were used in conjunction with a mathematical model for corrosion pit initiation in the vicinity of MnS inclusions in stainless steel to investigate the relationship between physicochemical parameters and the potential and concentration distributions. The finite difference method with central differences was used to calculate sensitivities. The mathematical model of pit initiation included 20 species plus the potential and 13 physicochemical parameters including rate constants for chemical and electrochemical surface reactions and equilibrium constants for homogeneous reactions. It was found that the potential and concentration profiles are most sensitive to the Tafel slope of the rate of electrochemical dissolution of sulfur-containing inclusions and least sensitive to changes in the equilibrium coefficients of the homogeneous reactions. The rate constant for the electrochemical reaction for dissolution of sulfide inclusions was also found to be significant. The procedure provides a first step toward selecting the most important parameters, designing critical experiments, and selecting the hypothesis that best fits experimental data. Pitting corrosion of stainless steel ͑SS͒ is a localized phenomenon that may initiate at various types of surface sites including sulfide inclusions. Interest in pitting corrosion is high because it is often a first step leading to crevice corrosion, corrosion fatigue, stress-corrosion cracking, and failure of coatings. The various mechanisms by which initiation occurs, a subject of longstanding interest, have increasingly been investigated with the aid of mathematical models. While there are various modeling approaches, we focus here on an approach where the underlying physical phenomena associated with the mechanisms are expressed by continuum equations for reaction, transport, and equilibration among species. Although numerical simulation of complex corrosion systems can provide useful insight, there is also the need for additional numerical methods. In this work we focus on the assessment of uncertainty. For example, the validation of models by comparison with experimental data requires specification of the hypothesis of corrosion mechanism ͑of which the literature provides multiple reasonable choices͒ as well as values for the system parameters ͑some of which may be difficult or impossible to measure directly͒. Various kinds of uncertainty therefore arise. The motivation for the present work is to apply numerical analysis tools to identify the most sensitive parameters associated with one particular hypothesis of mechanism. Such tools may find use in addressing questions such as: What properties of a system are responsible for its observed behavior? What is the most promising experiment to refute or confirm a model? Which of several hypotheses best agrees with experimental data from heterogeneous sources? The role of sulfide inclusions has been widely investigated. Sulfide inclusions play an important role in the initiation of pitting corrosion. Various researchers have studied initiation of pitting corrosion with a range of experimental techniques to clarify various events during early stages of sulfide inclusion dissolution, ͑e.g., Ref. 1-3͒ and pit growth. Sulfur-containing species have been detected during dissolution of sulfide inclusions, 4-8 and pH measurements have been taken at various locations during sulfide dissolution. 11,12 In the present work we consider in detail a mathematical model of one particular mechanism developed to simulate pit initiation at a single MnS inclusion in SS within an electrochemical microcell. 13 The model examined the hypotheses that pit initiation occurs by depassivation of SS as a result of accumulation of thiosulfate ions above a critical concentration in the presence of chloride, and that the rate of inclusion dissolution was catalyzed by chloride. The model was used to predict the variation of potential in time and distance during the pit initiation and also to predict the dependence of pitting potential on the chloride concentration for a single inclusion. The emphasis in the present work is to apply numerical procedures for assessment of parameter sensitivity and to demonstrate their use with one hypothesis of mechanism. In this work, the system of coupled nonlinear equations reported previously 13 was solved numerically using a finite difference method whose solution at each time step involved the solution of 120,771 algebraic equations for a chloride-containing system. Parameters in the simulation model included diffusion coefficients of 20 different species, chemical/electrochemical rate constants, and equilibrium constants. The numerical values of many of these parameters reported in literature vary widely. However, it is well known that the dynamic behavior of a complex chemical transport system is often specified by the values of only a subset of all the parameters 14 and that variations in the other parameters have a small effect. As a simple example of this, consider five first-order reactions in series where the rate constants for the last four reactions are a factor of ten larger than the rate constant for the first reaction. The concentration vs. time plots for all chemical compounds in the system are very sensitive to changes in the rate constant for the slowest reaction but are relatively insensitive to variations in the rate constants of the fast reactions. A similar effect occurs in complex reaction networks, which have many reactions in parallel, in series, and intimately coupled. In such systems, experimental effort is reduced by focusing only on determining the values for the most critical parameters. Parameter sensitivity analysis is used to determine which of the parameters are the most significant by determining the effect of perturbing the parameters on the process outputs. 14 Such analysis aids in selecting those parameters to be estimated for further analysis using simulation and/or experimental data and in designing future experiments. Parameter sensitivity analysis is a well-developed are

Plasmonic Micro-Channel Assisted Photonic Crystal Fiber Based Highly Sensitive Sensor for Multi-Analyte Detection

A dual-channel propagation controlled photonic crystal fiber (PCF)-based plasmonic sensor was presented to detect multiple analytes simultaneously. Plasmonic micro-channels were placed on the outer surface of the PCF, which facilitates an easy sensing mechanism. The sensor was numerically investigated by the finite element method (FEM) with the perfectly matched layer (PML) boundary conditions. The proposed sensor performances were analyzed based on optimized sensor parameters, such as confinement loss, resonance coupling, resolution, sensitivity, and figure of merit (FOM). The proposed sensor showed a maximum wavelength sensitivity (WS) of 25,000 nm/refractive index unit (RIU) with a maximum sensor resolution (SR) of 4.0 × 10−6 RIU for channel 2 (Ch-2), and WS of 3000 nm/RIU with SR of 3.33 × 10−5 RIU for channel 1 (Ch-1). To the best of our knowledge, the proposed sensor exhibits the highest WS compared with the previously reported multi-analyte based PCF surface plasmon resonance (SPR) sensors. The proposed sensor could detect the unknown analytes within the refractive index (RI) range of 1.32 to 1.39 in the visible to near infrared region (550 to 1300 nm). In addition, the proposed sensor offers the maximum Figure of Merit (FOM) of 150 and 500 RIU−1 with the limit of detection (LOD) of 1.11 × 10−8 RIU2/nm and 1.6 × 10−10 RIU2/nm for Ch-1 and Ch-2, respectively. Due to its highly sensitive nature, the proposed multi-analyte PCF SPR sensor could be a prominent candidate in the field of biosensing to detect biomolecule interactions and chemical sensing