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Origins of hydrogen peroxide selectivity during oxygen reduction on organic mixed ionic-electronic conducting polymers
No full textElectrochemical reduction of atmospheric oxygen provides carbon emission-free pathways for the generation of electricity from chemical fuels and for the distributed production of green chemical oxidants like hydrogen peroxide. Recently, organic mixed ionic-electronic conducting polymers (OMIECs) have been reported as a new class of active electrode materials for the oxygen reduction reaction. This work sets out to identify the operative oxygen reduction mechanism of OMIECs through a multi-faceted experimental and theoretical approach. Using a combination of pH-dependent electrochemical characterization, operando UV-Vis and Raman spectroscopy, and ab initio calculations, we find that the n-type OMIEC, p(NDI-T2 P75), displays pH-dependent activity for the selective reduction of oxygen to the 2-electron hydrogen peroxide product. We use microkinetic simulations of the electrochemical behavior to rationalize our experimental observations through a polaron-mediated, non-adsorptive pathway involving chemical reduction of oxygen to the 1-electron superoxide intermediate followed by pH-dependent catalytic disproportionation to hydrogen peroxide. Finally, this pathway is applied to understand the experimental oxygen reduction reactivity across several n- and p-type OMIECs
METHOD FOR OPERATING AN ELECTRIC DRIVE UNIT, DATA PROCESSING DEVICE AND ELECTRIC DRIVE UNIT
No full textThe disclosure relates to a method for operating an electric drive unit (10). The electric drive unit (10) comprises an electric machine (12) with a stator (14) and a rotor (16). The stator (14) comprises a set of phase windings. Moreover, the electric drive unit (10) comprises an inverter (18) for controlling the operation of the electric machine (12) by providing AC signals to the phase windings. The inverter (18) is electrically coupled to the phase windings. The method comprises triggering an AC drive signal for each phase winding of a first sub-set of phase windings such that the rotor (16) is rotated via the phase windings of the first sub-set. Additionally, the method comprises triggering a noise compensation measure for at least one phase winding of a second sub-set of the phase windings for compensating an undesired signal effect. Moreover, a data processing device (20) and an electric drive unit (10) with such a data processing device (20) are presented
Machine learning for the free-form inverse design in nanophotonics
No full textIn the fast developing field of nanotechnology, nanophotonics has emerged as a revolutionary technology, controlling the complex interplay between light and matter at the nanoscale. Crucial to the advancement of this field is the ability to manipulate the propagation of electromagnetic waves by shaping the transmitted or reflected wavefront through the design of structures that are only a fraction of the wavelength in size. The design of these subwavelength structures forming a quasicontinuous material is complex and usually exceeds human intuition.In this thesis, we present our approach utilizing deep neural networks for both the prediction of optical properties of nanostructures and their inverse design. First, we demonstrate the feasibility of employing neural networks to accurately predict the optical properties of nanostructured materials, with a particular emphasis on metasurfaces. Given the increasing need for miniaturization together with smaller optical losses, the emergence of metasurfaces---single layers of phase-modifying nanostructures---has heralded a significant advancement, enabling unprecedented manipulation of light beyond the capabilities of traditional materials.Subsequently, we present methodologies to achieve inverse design of metasurfaces, guided by specific desired optical attributes and fabrication constraints. This is achieved through neural network design and the generation of training data, labeled with corresponding optical characteristics and manufacturability constraints. We implemented a conditional generative adversarial network comprised of five synergistic neural networks. This approach effectively mitigates challenges related to design non-uniqueness, mode collapse, and experimental feasibility.Additionally, our research explores various techniques to optimize and stabilize neural network training and introduces novel network graph compositions, contributing to a versatile generator model capable of conceiving multiple metasurface unit cells with predefined, interconnected properties. This thesis thus provides insights and methodologies for inverse design, contributing to the continued evolution of nanophotonic design and applications
Polaritonic linewidth asymmetry in the strong and ultrastrong coupling regime
Get PDFThe intriguing properties of polaritons resulting from strong and ultrastrong light–matter coupling have been extensively investigated. However, most research has focused on spectroscopic characteristics of polaritons, such as their eigenfrequencies and Rabi splitting. Here, we study the decay rates of a plasmon–microcavity system in the strong and ultrastrong coupling regimes experimentally and numerically. We use a classical scattering matrix approach, approximating our plasmonic system with an effective Lorentz model, to obtain the decay rates through the imaginary part of the complex quasinormal mode eigenfrequencies. Our classical model automatically includes all the interaction terms necessary to account for ultrastrong coupling without dealing with the rotating-wave approximation and the diamagnetic term. We find an asymmetry in polaritonic decay rates, which deviate from the expected average of the uncoupled system’s decay rates at zero detuning. Although this phenomenon has been previously observed in exciton–polaritons and attributed to their disorder, we observe it even in our homogeneous system. As the coupling strength of the plasmon–microcavity system increases, the asymmetry also increases and can become so significant that the lower (upper) polariton decay rate reduction (increase) goes beyond the uncoupled decay rates,\ua0γ\ua0−\ua0<\ua0γ\ua00,c\ua0<\ua0γ\ua0+. Furthermore, our findings demonstrate that polaritonic linewidth asymmetry is a generic phenomenon that persists even in the case of bulk polaritons
Distributed optimization for the optimal control of electric vehicle fleets
Get PDFOwing to their absence of tailpipe emissions and their independence from fossil fuels, Electric Vehicles (EVs) are currently experiencing a rapid deployment in an attempt to curb global greenhouse gas emissions. EV operation represents a technical challenge, however, as new control algorithms need to be developed to address their limited driving range and their longer charging times. Optimization-based control techniques offer a promising way to plan EV operation over a prediction horizon while including key operational constraints, but they can be prohibitively slow for real-time applications as they rely on solving computationally hard optimization problems. One way to address the computational complexity of these approaches is by deploying adapted decomposition methods with which the computational load of solving these optimization problems can be distributed across the vehicles involved, where most computations can then be carried out in parallel.This thesis presents decomposition-based solution procedures for optimal control problems involving groups of EVs. In particular, the problems covered in this work are (i) the cooperative eco-driving control of a platoon of electric trucks, (ii) the eco-driving and operational control of an electric bus line, and (iii) the operational control and charging scheduling of an electric bus network. Even though their particular objective functions and constraints may differ, the coupling structures of these problems, i.e. how each vehicle\u27s influence on the others is organized, share some similarities.The platoon control problem is formulated as a Nonlinear Program (NLP) and solved with second-order optimization methods. The Riccati recursion is used as part of a decomposition scheme that exploits the chain-like coupling structure of a truck platoon and makes it possible to fully distribute all computations. Similarly, the bus line problem is formulated as an NLP. A primal decomposition scheme where the NLP is split into a master problem and independent bus subproblems is presented. The hierarchical control architecture obtained makes it possible to distribute most of the computations. Finally, the bus network problem is formulated as a Mixed-integer Linear Program (MILP). A dual decomposition scheme based on Lagrangian relaxation is deployed to relax the coupling constraints between the different bus lines
Brain stimulation-on-a-chip: a neuromodulation platform for brain slices
Get PDFElectrical stimulation of ex vivo brain tissue slices has been a method used to understand mechanisms imparted by transcranial direct current stimulation (tDCS), but there are significant direct current electric field (dcEF) dosage and electrochemical by-product concerns in conventional experimental setups that may impact translational findings. Therefore, we developed an on-chip platform with fluidic, electrochemical, and magnetically-induced spatial control. Fluidically, the chamber geometrically confines precise dcEF delivery to the enclosed brain slice and allows for tissue recovery in order to monitor post-stimulation effects. Electrochemically, conducting hydrogel electrodes mitigate stimulation-induced faradaic reactions typical of commonly-used metal electrodes. Magnetically, we applied ferromagnetic substrates beneath the tissue and used an external permanent magnet to enable in situ rotational control in relation to the dcEF. By combining the microfluidic chamber with live-cell calcium imaging and electrophysiological recordings, we showcased the potential to study the acute and lasting effects of dcEFs with the potential of providing multi-session stimulation. This on-chip bioelectronic platform presents a modernized yet simple solution to electrically stimulate explanted tissue by offering more environmental control to users, which unlocks new opportunities to conduct thorough brain stimulation mechanistic investigations
Rethinking Long-Tailed Visual Recognition with Dynamic Probability Smoothing and Frequency Weighted Focusing
No full textDeep learning models trained on long-tailed (LT) datasets often exhibit bias towards head classes with high frequency. This paper highlights the limitations of existing solutions that combine class- and instance-level re-weighting loss in a naive manner. Specifically, we demonstrate that such solutions result in overfitting the training set, significantly impacting the rare classes. To address this issue, we propose a novel loss function that dynamically reduces the influence of outliers and assigns class-dependent focusing parameters. We also introduce a new long-tailed dataset, ICText-LT, featuring various image qualities and greater realism than artificially sampled datasets. Our method has proven effective, outperforming existing methods through superior quantitative results on CIFAR-LT, Tiny ImageNet-LT, and our new ICText-LT datasets. The source code and new dataset are available at \url{https://github.com/nwjun/FFDS-Loss
Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins
Get PDFHemodialysis (HD) removes nitrogenous waste products from patients’ blood through a semipermeable membrane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learning technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments
Design and Validation of a Concurrent Dual-Band 1.84/2.65 GHz GaN Doherty Power Amplifier
No full textThis paper introduces a dual-band Do-herty power amplifier (DB-DPA), using symmetrical GaN HEMT devices. The load modulation network incorporates a wideband Doherty combiner followed by a Chebyshev transformer as a post-matching network. The operating frequencies of the proposed DB-DPA are 1.84 and 2.65 GHz with a bandwidth of 70 MHz in each band. The DB-DPA showcases satisfactory performance with a peak output power of 47.4 dBm for 1.84 GHz and 47.1 dBm for 2.65 GHz. Furthermore, a drain efficiency of 50.4-64.5% and 44.4-64.4% is measured between 6-dB output back-off (OBO) level to peak power level, respectively, for 1.84 GHz and 2.65 GHz bands. Concurrent dual-band performance of the proposed DB-DPA is demonstrated using 20-MHz signals with a peak-to-average power ratio of 6 dB. For an average output power of 40 dBm, the DB-DPA yields 46.4% efficiency with an adjacent channel leakage ratio (ACLR) better than -51 dBc
Statistical Analysis of Hardware Impairments in Communication Systems
Get PDFThis thesis delves into the study of hardware impairments, the inevitable limiting factors in radio frequency (RF) communication systems, and their substantial influence on system performance. It addresses the incongruity between the often stringent requirements imposed by standards and the innate imperfections of analog circuits present in real-world RF electronics. Key hardware impairments, namely In-phase and Quadrature-phase Imbalance (IQI), phase noise, power amplifier (PA) nonlinearities, and antenna array perturbations, are studied, offering comprehensive overviews of their individual characteristics and their interactions with each other.The thesis also focuses on the effects of PA nonlinearity on widely used signal transmission optimizations, namely pulse shaping and matched filtering techniques. The nonlinear behavior of the PA, which can lead to signal distortions and inter-symbol interference, is shown to impact these techniques, posing considerable challenges to reliable communication. Moreover, a noticeable gap exists in the academic literature concerning an accurate analysis of the effect of PA nonlinearity on pulse shaping and matched filters. Such a gap underscores the need for a rigorous investigation into how these transmission optimizations are influenced by the PA\u27s nonlinearity. As the first contribution of this thesis to the literature, in Paper A, a detailed study is conducted to comprehensively address these effects, bridging the existing knowledge gap and providing new insights into the interplay between PA nonlinearity, pulse shaping, and matched filter.As a complementary study to Paper A, Paper B delves deeper into the intricacies of PA nonlinearity, examining the impact of various waveforms and modulation orders. Within this exploration, a waveform factor is formulated, elucidating the interplay between waveform statistics, amplifier-specific parameters, and signal power in determining the total distortion power. Furthermore, a theoretical closed form for the nonlinear distortion, both in-band and out-of-band, is derived. This derivation reveals a notable finding: the distortion approximately distributes evenly between the in-band and out-of-band portions of the spectrum, offering a nuanced understanding of how PA nonlinearity manifests across adjacent frequencies.Moreover, in paper C, the effects of IQI and phase noise, in conjunction with PA nonlinearity, are analyzed, which can lead to a scrambled effect that further degrades the received signal quality. An additive noise modeling technique is introduced as a novel approach to effectively represent these combined effects. This technique facilitates accurate tracking of system performance under varying hardware impairment conditions, serving as a valuable tool to understand the collective impact on the system and develop mitigation strategies.The thesis further contributes to the field by providing statistical models for beam pattern variations due to antenna array perturbations in Paper D. The perturbations are random variations in phase, gain, and antenna element positions. These models enable an accurate projection of system performance under various conditions, helping to bridge the gap between theoretical design and practical implementation in RF communication systems.This comprehensive investigation of hardware impairments in communication systems paves the way for more resilient design strategies, enhancing the robustness and reliability of future RF communication systems