Nonlinear dynamics of a single-gap terahertz split-ring resonator under electromagnetic radiation.

Research into metasurfaces is developing rapidly and is topical due to their importance and applications in various fields such as communications, cryptography, and sensing, to name a few. These materials are artificially engineered to manipulate/control electromagnetic (EM) waves, in order to present a particular functionality. In this regard, nonlinear metasurfaces may present particular functionalities that remain to be discovered. In this paper, we numerically investigate the dynamic behaviors caused by the motion of charge carriers under the intense EM field at the gap of a single nonlinear split-ring resonator (NSRR) in the terahertz (THz) frequency range. We derive the mathematical model that is used to examine the excitation properties of the NSRR and then demonstrate various tuning regions. Analysis of the two-dimensional parameter space reveals that the NSRR exhibits periodic, chaotic patterns as the amplitude of the excitation field and the loss parameter vary. However, this chaotic behavior disappears when the loss parameter is very large. The period doubling that confirms the transition between the periodic and chaotic modes is explored using the bifurcation diagram. The sensitivity of the initial conditions is examined on three dynamic region plots. Our results correctly demonstrate that the NSRR exhibits the attractive phenomenon of multistability. The coexistence of two stable states is studied and confirmed on the basin of attractions for a fixed set of amplitude or loss parameters. The energy balance of the proposed model is well analyzed on the dynamic states and parameters to characterize the different oscillation regimes. The study of the multistability in the work represents an important first step toward the development of photonic memory devices in the THz frequency range.

Microbial fuel cell soft sensor for real-time toxicity detection and monitoring.

Real-time toxicity detection and monitoring using a microbial fuel cell (MFC) is often based on observing current or voltage changes. Other methods of obtaining more information on the internal state of the MFC, such as electrochemical impedance spectroscopy (EIS), are invasive, disruptive, time consuming, and may affect long-term MFC performance. This study proposes a soft sensor approach as a non-invasive real-time method for evaluating the internal state of an MFC biosensor during toxicity monitoring. The proposed soft sensor approach is based on estimating the equivalent circuit model (ECM) parameters in real time. A flow-through MFC biosensor was operated at several combinations of carbon source (acetate) and toxicant (copper) concentrations. The ECM parameters, such as internal resistance, capacitance, and open-circuit voltage, were estimated in real time using a numerical parameter estimation procedure. The soft sensor approach proved to be an adequate replacement for EIS measurements in quantifying changes in the biosensor internal parameters. The approach also provided additional information, which could lead to earlier detection of the toxicity onset.

Harvesting Energy from Multiple Microbial Fuel Cells with a High-Conversion Efficiency Power Management System.

Direct electricity production from waste biomass in a microbial fuel cell (MFC) offers the advantage of producing renewable electricity at a high Coulombic efficiency. However, low MFC voltage (below 0.5 V) necessitates the simultaneous operation of multiple MFCs controlled by a power management system (PMS) adapted for operating bioelectrochemical systems with complex nonlinear dynamics. This work describes a novel PMS designed for efficient energy harvesting from multiple MFCs. The PMS includes a switched-capacitor-based converter, which ensures operation of each MFC at its maximum power point (MPP) by regulating the output voltage around half of its open-circuit voltage. The open-circuit voltage of each MFC is estimated online regardless of MFC internal parameter knowledge. The switched-capacitor-based converter is followed by an upconverter, which increases the output voltage to a required level. Advantages of the proposed PMS include online MPP tracking for each MFC and high (up to 85%) power conversion efficiency. Also, the PMS prevents voltage reversal by disconnecting an MFC from the circuit whenever its voltage drops below a predefined threshold. The effectiveness of the proposed PMS is verified through simulations and experimental runs.

Maximizing power production in a stack of microbial fuel cells using multiunit optimization method.

This study demonstrates real-time maximization of power production in a stack of two continuous flow microbial fuel cells (MFCs). To maximize power output, external resistances of two air-cathode membraneless MFCs were controlled by a multiunit optimization algorithm. Multiunit optimization is a recently proposed method that uses multiple similar units to optimize process performance. The experiment demonstrated fast convergence toward optimal external resistance and algorithm stability during external perturbations (e.g., temperature variations). Rate of the algorithm convergence was much faster than in traditional maximum power point tracking algorithms (MPPT), which are based on temporal perturbations. A power output of 81-84 mW/L(A) (A = anode volume) was achieved in each MFC.

Online optimization of surface plasmon resonance-based biosensor experiments for improved throughput and confidence.

The emergence of surface plasmon resonance-based optical biosensors has facilitated the identification of kinetic parameters for various macromolecular interactions. Normally, these parameters are determined from experiments with arbitrarily chosen periods of macromolecule and buffer injections, and varying macromolecule concentrations. Since the choice of these variables is arbitrary, such experiments may not provide the required confidence in identified kinetic parameters expressed in terms of standard errors. In this work, an iterative optimization approach is used to determine the above-mentioned variables so as to reduce the experimentation time, while treating the required standard errors as constraints. It is shown using multiple experimental and simulated data that the desired confidence can be reached with much shorter experiments than those generally performed by biosensor users.