- 标题
- 摘要
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- 实验方案
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Bioelectrochemical Interface Engineering || Quantum Dots for Bioelectrochemical Applications
摘要： In this chapter, the structure, characteristics, and synthesis processes of QDs were summarized. The most common electrochemical methods for QDs were also represented by reviewing their potential applications in biosensor technology. Many specific applications have been realized by utilizing the unique characteristics of QDs. However, their limited commercial availability; requirements of demanding synthesis procedures, analysis of multicomponent complex samples, and in situ analysis; and lack of validation with real samples are other disadvantage of QDs. The design of QD-based biosensors is also complicated due to limitedly defined redox behavior of nanocrystals resulting in difficulty with probing their redox levels. Therefore, extensive investigations are needed on the redox properties of QDs, despite having a large amount of literature on their synthesis, properties, and applications. The interactions between the system parameters can be clarified by using the mathematical models. To solve the model equations analytically, it is introduced to equivalent systems having identical spectra and wave functions, and these forms have to satisfy the solvability conditions. 3D QDs can be modeled by an ODE accurately, when the dimension of the cross section is very small and the energy levels are low. To obtain high accuracy of the effective mass approximation model, sizes of the QDs should be 10–20 nm. Optimum design problems for QD systems generally have discrete search spaces and involve highly nonlinear terms. Therefore, the selection of any traditional optimization methods to solve optimization problems is not appropriate. In these circumstances, it is useful to perform modern optimization algorithms such as the GA, DE, and SA methods. By incorporating mathematical models and optimization approximations to QD-based bioloelectrochemical systems, their performances will excel far beyond the current state in the near future.
关键词： Optimization Algorithms,Mathematical Models,Bioelectrochemical Applications,Electrochemical Methods,Biosensor Technology,Quantum Dots
更新于2025-09-11 14:15:04
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Data from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms
摘要： This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions. These functions were obtained through the PeV curves of a mathematical modeling and represent photovoltaic module with several local maximums and a global maximum. In addition, data from a feedforward neural network are shown, which represent an approximation of the multimodal functions that were obtained with mathematical modeling. The modeling of multimodal functions, the architecture of the neural network and the use of the data were discussed in our previous work entitled “Search for Global Maxima in Multimodal Functions by Applying Numerical Optimization Algorithms: A Comparison Between Golden Section and Simulated Annealing” [1]. Data were obtained through simulations in a C code, which were exported to DAT files and subsequently organized into four Excel tables. Each table shows the voltage and power data for the five modules of the photovoltaic array, for multimodal functions and for the approximation of the multimodal functions implemented by the artificial neural network. In this way, a dataset that can be used to evaluate the performance of optimization algorithms and system identification techniques applied in multimodal functions was obtained.
关键词： Multimodal functions,Photovoltaic modules,Artificial neural networks,Partial shading,Optimization algorithms
更新于2025-09-11 14:15:04
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Ultra-fast imaging with optical encoding and compressive sensing
摘要： Serial time-encoded ampli?ed microscopy (STEAM) is an emerging technology which enables an ultra-fast phenomena to be captured at Ghz scan rate. The trade-off between high imaging speed and high spatial resolution remains a problem where the maximum scan rate is limited by the sampling rate of the digitizer and the temporal dispersion in the ?ber to avoid data blending. In this paper, we address these limitations using state-of-the-art optimization algorithms under compressive sensing framework and establish the data acquisition model based on our proposed experimental setup by considering the effect of individual optical components such as laser spectral pro?le, encoding mask patterns, dispersion of the ?ber and optical noise in the system. We introduce two methods of alternating direction method of multipliers with total variation regularization (ADMM-TV) and discrete wavelet hard thresholding (DWT-Hrd) for STEAM based imaging systems. Our results demonstrate that a 10GHz scan rate can be achieved compared to the conventional 1GHz microscopy imaging system while maintaining high image reconstruction quality in terms of structural similarity index measurement (SSIM). It is shown that among the two proposed optimization algorithms, ADMM-TV outperforms DWT-Hrd by 20% in SSIM measurements. Finally, it is shown that having 70-80% light transmission through the mask reveals the optimum results in terms of reconstruction quality.
关键词： optimization algorithms,?ber optics imaging,compressive sensing,optical encoding
更新于2025-09-09 09:28:46