Performance-limiting Factor Research Articles

Effect of Oxygen Diffusion Constraints on the Performance of Planar Solid Oxide Fuel Cells for Variable Oxygen Concentration

Performance enhancement of solid oxide fuel cell (SOFC) has been a very prominent research problem of the present era. One of the key performance-limiting factors is diffusional constraint encounte...

Model-guided design of a high performance and durability Ni nanofiber/ceria matrix solid oxide fuel cell electrode

Mixed ionic electronic conductors (MIECs) have attracted increasing attention as anode materials for solid oxide fuel cells (SOFCs) and they hold great promise for lowering the operation temperature of SOFCs. However, there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes. Using a newly-developed approach based on 3D-tomography and electrochemical impedance spectroscopy, here for the first time we quantify the contribution of the dual-phase boundary (DPB) relative to the three-phase boundary (TPB) reaction pathway on real MIEC electrodes. A new design strategy is developed for Ni/gadolinium doped ceria (CGO) electrodes (a typical MIEC electrode) based on the quantitative analyses and a novel Ni/CGO fiber–matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders. With only 11.5 vol% nickel, the designer Ni/CGO fiber–matrix electrode shows 32% and 67% lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively. The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.

Numerical modelling of the performance-limiting factors in CZGSe solar cells

Numerical models are proposed that are able to describe the current–voltage (I-V) behaviour of two Cu2ZnGeSe4 (CZGSe) solar cells measured under different illuminations at 300 K. In the model, the doping density of the CZGSe layer and the mobility of carriers are determined by capacitance-voltage (C-V) profiling and AC field Hall effect measurement, respectively. Some of the other parameters in the solar cells, including the series and the shunt resistance, the metal work function of the back contact, defect properties and absorption coefficient in the absorber layer, are determined using a differential evolution algorithm by fitting of the model with the experimentally measured I-V curves. Sensitivity analysis of our proposed model with SCAPS-1D demonstrates that the low metal work function, which results in a hole barrier at the back contact, the low shunt resistance and the high series resistance of the cell can explain the low fill factor and the low efficiency in these cells.

Electrochemical sensors for environmental gas analysis

Abstract The application of electrochemical sensors for measurement of concentration of pollutant gases in air in the part-per-billion (109) range is reviewed. Performance-limiting factors, particularly the effects of extremes and of relatively rapid changes in ambient temperature and humidity, are noted. Variations in composition of the electrolyte in the meniscus at the electrode–gas interface and instability of the solid–liquid–gas contact line, causing important variations in current due to background electrode reactions, are deduced and suggested as the reason for the performance limitations. Suggestions are made for mitigation through instrument design.

Effect of N-cyclic cationic groups in poly(phenylene oxide)-based catalyst ionomer membranes for anion exchange membrane fuel cells

Herein, as a binder (or catalyst ionomer) for AEMFCs, we investigated the effect of two different cationic copolymers based on poly(phenylene oxide) (PPO) with N-cyclic quaternary ammonium (QA) groups, including six-membered dimethyl piperidinium (DMP) and bis-six-membered azonia-spiro undecane (ASU). An earlier report on the same polymers for membranes in AEMFCs indicated the better electrochemical performance of PPO-ASU compared with PPO-DMP. Therefore, we would like to investigate these two polymers for catalyst ionomers. The outcome in this study using these two copolymers as catalyst ionomers indicates the opposite result; the electrochemical performance of the PPO-DMP ionomer is much better than the PPO-ASU ionomer. The commercial Fumion ionomer was used for the qualitative comparison. The density functional theory (DFT) calculation of the adsorption energy according to different orientations of the cationic groups on the catalyst surface shows that there is no difference between the adsorption energy of DMP and ASU cations, in compliance with the orientations of the cations. Although the PPO-ASU ionomer membrane has the highest hydroxide conductivity at 60 °C in liquid water, the hydrogen oxidation/reduction (HOR) activity of PPO-DMP and PPO-ASU showed similar values with the Fumion ionomer. While the PPO-DMP ionomer membrane shows relatively large fuel gas (hydrogen) permeability in dry and wet conditions, due to the chain flexibility and the presence of two methyl groups compared to the single methyl groups and lower flexibility of the PPO-ASU and Fumion ionomers. The electrochemical performance of a membrane electrode assembly (MEA) using the PPO-DMP ionomer exhibited an exceptional peak power density of 335 mW cm−2 compared to lower peak power densities the of PPO-ASU and Fumion ionomers under 60 °C and a fully humidified condition (H2/O2). The SEM images of MEAs after testing supports the conclusion that the PPO-DMP ionomer forms a uniform catalyst interface that is very well bound between the electrode and membrane, unlike the PPO-ASU and Fumion ionomers. The PPO-DMP ionomer membrane also showed better tensile strength and elongation at break than the PPO-ASU ionomer membrane. Therefore, we conclude that the well-prepared three-phase boundary structure played a critical role for the catalyst ionomer in each electrode, overcoming one of the critical performance-limiting factors.

Tunable 1/f Noise in CVD Bernal-Stacked Bilayer Graphene Transistors.

Low-frequency noise is a key performance-limiting factor in almost all electronic systems. Thanks to its excellent characteristics such as exceptionally high electron mobility, graphene has high potential for future low-noise electronic applications. Here, we present an experimental analysis of low-frequency noise in dual-gate graphene transistors based on chemical vapor-deposited Bernal-stacked bilayer graphene. The fabricated dual-gate bilayer graphene transistors adopt atomic layer-deposited Al2O3 and HfSiO as top-gate and back-gate dielectric, respectively. Our results reveal an obvious M-shape gate-dependent noise behavior which can be well described by a quantitative charge-noise model. The minimal area normalized noise spectral density at 10 Hz reaches as low as about 3 × 10-10 μm2·Hz-1 at room temperature, much lower than the best results reported previously for graphene devices. In addition, the observed noise level further decreases by more than 10 times at temperature of 20 K. Meanwhile, the noise spectral density amplitude can be tuned by more than 2 orders of magnitude at 20 K by dual-gate voltages.

Investigation of factors affecting the performance of a single-layer nanocomposite fuel cell

A large set of single-layer fuel cells utilizing lithium nickel zinc oxide (LNZ) as semiconducting material and doped ceria -based ionic conductors with varying composition were fabricated and characterized by both microscopic and macroscopic methods. Microstructural analysis showed that LiNiO, LiZnO and doped ceria formed crystalline phases, whereas alkali carbonates were in amorphous state. The ionic conductivity of the cell was found to be the most crucial performance-limiting factor. Using of catalytically active current collector improved the cell performance if the ionic conductivity through the cell was suitable. Controlling the porosity and optimizing the mass ratio of semiconductor and ionic conductor affected the cell performance as well. An optimized device achieved a power density of 300 mW/cm2 at 600 °C.

Understanding Thickness-Dependent Transport Kinetics in Nanosheet-Based Battery Electrodes

There is a growing need for thicker electrode designs to achieve high energy/power for ever-increasing power needs by electronic devices and electric automobiles. Though great efforts, such as structure optimization, have been devoted on fabricating thick electrodes, understanding of performance-limiting factors essential to electrode architecture design, has not been well established. In this study, the dependence of electrochemical behavior on electrode mass loading is comprehensively investigated in nanosheet-based electrodes. In particular, the effects of electrical conductivity and porosity are illustrated. In drop-casted electrodes, where nanosheets are highly stacked, ionic diffusion in the electrolyte has been determined to be the controlling step in electrodes with high thickness. To overcome the limitation of such sluggish ionic transport, a facile ice-templating strategy was employed to create vertically aligned channels, offering fast-diffusion pathways for the Li ion in the electrolyte. Impressively, the ice-templated electrodes exhibit a specific capacity of 144 mA h g–1 at 0.2 C and retain 83 mA h g–1 at 10 C with high mass loading ∼10 mg cm–2. The enhanced ion transport kinetics was verified by various electrochemical and structural characterizations. This work demonstrates the thickness scaling effect of nanosheet-based electrodes and highlights the importance of promoting ionic transport and electrolyte access for designing thick electrodes.

A New Indoor Positioning Algorithm of Cellular and Wi-Fi Networks

Fluctuation of the received signal strength (RSS) is the key performance-limiting factor for Wi-Fi indoor positioning schemes. In this study, the Manhattan distance was used in the weighted K-nearest neighbour (WKNN) algorithm to improve positioning accuracy. Reference point (RP) intervals were optimised to reduce the complexity of the system. Specifically, two new positioning schemes are proposed in this paper. Scheme 1 uses the cellular network to refine the fingerprint database, while Scheme 2 uses the cellular network positioning to locate the node a priori, then uses the Wi-Fi network to further improve accuracy. The experimental results showed that the average positioning error of Scheme 1 was 1·60 m, a reduction of 12% compared with the existing Wi-Fi fingerprinting schemes. In Scheme 2, when double cellular networks were used, RP usage was reduced by 64% and the calculating time was 0·24 s, a reduction of up to 69·5% compared with the Manhattan-WKNN algorithm. These proposed schemes are suitable for high accuracy and real-time positioning situations, respectively.

Photoinduced Ultrafast Electron Transfer and Charge Transport in a PbI2/C60 Heterojunction

Charge transport and electron transfer in a PbI2/C60 heterojunction were investigated. The charge carrier mobilities and trap densities of PbI2 were extracted from current–voltage characteristics of hole- and electron-only devices. Femtosecond transient absorption spectroscopy was used to elucidate the photophysical processes occurring in the heterojunction. For a pristine PbI2 film, trap-limited bimolecular recombination was the dominant mechanism in the case of low pump fluence. We observed photoinduced ultrafast electron transfer from PbI2 to C60 with a rate constant of 0.45 ps–1. Semiclassical Marcus electron transfer theory was used to estimate the electronic coupling between the conduction band edge of PbI2 and the lowest unoccupied molecular orbital of C60. We also fabricated a solar cell using the PbI2/C60 heterojunction. Electron transfer and charge extract efficiencies in our cell were also deduced. The performance-limiting factors for solar cells based on PbI2 were discussed and a strategy to improve the device performance was developed. Our work is useful for ultraviolet-harvesting transparent solar cells and other photophysical and photochemical applications.

Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli.

A core goal of visual neuroscience is to predict human perceptual performance from natural signals. Performance in any natural task can be limited by at least three sources of uncertainty: stimulus variability, internal noise, and suboptimal computations. Determining the relative importance of these factors has been a focus of interest for decades but requires methods for predicting the fundamental limits imposed by stimulus variability on sensory-perceptual precision. Most successes have been limited to simple stimuli and simple tasks. But perception science ultimately aims to understand how vision works with natural stimuli. Successes in this domain have proven elusive. Here, we develop a model of humans based on an image-computable (images in, estimates out) Bayesian ideal observer. Given biological constraints, the ideal optimally uses the statistics relating local intensity patterns in moving images to speed, specifying the fundamental limits imposed by natural stimuli. Next, we propose a theoretical link between two key decision-theoretic quantities that suggests how to experimentally disentangle the impacts of internal noise and deterministic suboptimal computations. In several interlocking discrimination experiments with three male observers, we confirm this link and determine the quantitative impact of each candidate performance-limiting factor. Human performance is near-exclusively limited by natural stimulus variability and internal noise, and humans use near-optimal computations to estimate speed from naturalistic image movies. The findings indicate that the partition of behavioral variability can be predicted from a principled analysis of natural images and scenes. The approach should be extendable to studies of neural variability with natural signals.SIGNIFICANCE STATEMENT Accurate estimation of speed is critical for determining motion in the environment, but humans cannot perform this task without error. Different objects moving at the same speed cast different images on the eyes. This stimulus variability imposes fundamental external limits on the human ability to estimate speed. Predicting these limits has proven difficult. Here, by analyzing natural signals, we predict the quantitative impact of natural stimulus variability on human performance given biological constraints. With integrated experiments, we compare its impact to well-studied performance-limiting factors internal to the visual system. The results suggest that the deterministic computations humans perform are near optimal, and that behavioral responses to natural stimuli can be studied with the rigor and interpretability defining work with simpler stimuli.

Optimal interference range for minimum Bayes risk in binomial and Poisson wireless networks

Interference is the main performance-limiting factor in most wireless networks. Protocol interference model is extensively used in the design of wireless networks. However, the setting of interference range, a crucial part of the protocol interference model, is rather heuristic and remains an open problem. In this paper, we use the stochastic geometry and the direct approach to obtain the associated feasibility distributions. After that, we use the binary hypothesis testing to achieve the Bayes risk under binomial point process (BPP) and Poisson point process (PPP), respectively. According to the first derivative of the Bayes risk, we provide the equation to achieve the optimal interference range for minimum Bayes risk. We extend the method proposed by Wildman et al. to a more general situation. Furthermore, we show that for infinite PPP, those two methods converge to the same results. Several numerical results for wireless networks under BPP, finite PPP, and infinite PPP are given. Simulation results show that in the finite wireless network, the BPP method performs better than the PPP method.

Suppressed Deep Traps and Bandgap Fluctuations in Cu2CdSnS4 Solar Cells with ≈8% Efficiency

AbstractThe identification of performance‐limiting factors is a crucial step in the development of solar cell technologies. Cu2ZnSn(S,Se)4‐based solar cells have shown promising power conversion efficiencies in recent years, but their performance remains inferior compared to other thin‐film solar cells. Moreover, the fundamental material characteristics that contribute to this inferior performance are unclear. In this paper, the performance‐limiting role of deep‐trap‐level‐inducing 2CuZn+SnZn defect clusters is revealed by comparing the defect formation energies and optoelectronic characteristics of Cu2ZnSnS4 and Cu2CdSnS4. It is shown that these deleterious defect clusters can be suppressed by substituting Zn with Cd in a Cu‐poor compositional region. The substitution of Zn with Cd also significantly reduces the bandgap fluctuations, despite the similarity in the formation energy of the CuZn+ZnCu and CuCd+CdCu antisites. Detailed investigation of the Cu2CdSnS4 series with varying Cu/[Cd+Sn] ratios highlights the importance of Cu‐poor composition, presumably via the presence of VCu, in improving the optoelectronic properties of the cation‐substituted absorber. Finally, a 7.96% efficient Cu2CdSnS4 solar cell is demonstrated, which shows the highest efficiency among fully cation‐substituted absorbers based on Cu2ZnSnS4.

Kernel mapping for mitigating nonlinear impairments in optical short-reach communications.

Nonlinear impairments induced by the opto-electronic components are one of the fundamental performance-limiting factors in high-speed optical short-reach communications, significantly hindering capacity improvement. This paper proposes to employ a kernel mapping function to map the signals in a Hilbert space to its inner product in a reproducing kernel Hilbert space, which has been successfully demonstrated to mitigate nonlinear impairments in optical short-reach communication systems. The operation principle is derived. An intensity modulation/direct detection system with 1.5-µm vertical cavity surface emitting laser and 10-km 7-core fiber achieving 540.68-Gbps (net-rate 505.31-Gbps) has been carried out. The experimental results reveal that the kernel mapping based schemes are able to realize comparable transmission performance as the Volterra filtering scheme even with a high order.

Towards Bottom-up Engineered Electrode Microstructures for Redox Flow Batteries

Redox flow batteries (RFBs) are promising rechargeable electrochemical devices for grid-scale energy storage, but further cost reductions are needed for widespread implementation. While research efforts have primarily focused on molecular discovery, there has been significantly less attention paid to the engineering aspects (i.e. transport, reactor design) of the battery. Of particular importance are the porous electrodes used in the electrochemical stack. Today’s electrodes largely draw from the fuel cell material set, but, within a RFB, the porous electrode must perform a number of different roles including providing active surfaces for electrochemical reactions, facilitating uniform liquid electrolyte distribution, and supporting low pressure drops. Accordingly, before purpose-built electrodes can be developed for RFB applications, performance-limiting factors for the present materials set must be quantified. Unambiguous analysis is challenging in an operating RFB due to the complex coupling of transport and reactions, which vary as a function of state of charge during cell cycling. Deconvoluting the role of electrode properties on flow battery performance requires the development of diagnostic techniques that enable electrode characterization under well-controlled but application-relevant conditions. To this end, we systematically evaluate the performance of several different carbon paper, felt, and cloth electrodes using the single-electrolyte cell configuration [1] and a model organic redox couple (TEMPO/TEMPO+) [2]. Using polarization and electrochemical impedance spectroscopy, we quantify the impact of electrode microstructure on battery performance. To further understand the role of electrode microstructure on the performance, we use tomography, and numerical simulation in tandem with the aforementioned electrochemical diagnostics. We find that woven electrodes, which feature a bimodal pore size distribution, provide an outstanding electrochemical performance and low pressure drop [3]. Inspired on these learnings, our most recent work focuses on developing bottom-up synthetic methods to fabricate porous electrodes with engineered microstructures, with a particular interest in bimodal pore size distributions and porosity gradients across the electrode thickness. In this talk, I will discuss our most recent experimental strategies to engineer electrodes with highly-controlled microstructures tailored for specific RFB chemistries. Darling et al., J. Electrochem. Soc., 161, A1381 (2014).D. Milshtein et al., J. Power Sources, 327, 151 (2016).Forner-Cuenca et al., Under Review at Electrochem. Soc. Acknowledgments We gratefully acknowledge the financial support of the Swiss National Science Foundation (P2EZP2_172183) and the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the United States Department of Energy.

Maximum Efficiencies and Performance-Limiting Factors of Inorganic and Hybrid Perovskite Solar Cells

The Shockley and Queisser limit, a well-known efficiency limit for a solar cell, is based on unrealistic physical assumptions and its maximum limit is seriously overestimated. To understand the power loss mechanisms of record-efficiency cells, a more rigorous approach is necessary. Here, we have established a new formalism that can accurately predict absolute performance limits of solar cells in conventional thin film form. In particular, we have estimated the maximum efficiencies of 13 well-studied solar cell materials [GaAs, InP, CdTe, a-Si:H, CuInSe2, CuGaSe2, CuInGaSe2, Cu2ZnSnSe4, Cu2ZnSnS4, Cu2ZnSn(S,Se)4, Cu2ZnGeSe4, CH3NH3PbI3, HC(NH2)2PbI3] in a 1-um-thick physical limit. Our calculation shows that over 30% efficiencies can be achieved for absorber layers with sharp absorption edges (GaAs, InP, CdTe, CuInGaSe2, Cu2ZnGeSe4). Nevertheless, many record-efficiency polycrystalline solar cells, including hybrid perovskites, are limited by open-circuit voltage and fill-factor losses. We show that the maximum conversion efficiencies described here present new alternative limits that can predict the power generation of real-world solar cells.

Consistent Device Simulation Model Describing Perovskite Solar Cells in Steady-State, Transient, and Frequency Domain.

A variety of experiments on vacuum-deposited methylammonium lead iodide perovskite solar cells are presented, including JV curves with different scan rates, light intensity-dependent open-circuit voltage, impedance spectra, intensity-modulated photocurrent spectra, transient photocurrents, and transient voltage step responses. All these experimental data sets are successfully reproduced by a charge drift-diffusion simulation model incorporating mobile ions and charge traps using a single set of parameters. While previous modeling studies focused on a single experimental technique, we combine steady-state, transient, and frequency-domain simulations and measurements. Our study is an important step toward quantitative simulation of perovskite solar cells, leading to a deeper understanding of the physical effects in these materials. The analysis of the transient current upon voltage turn-on in the dark reveals that the charge injection properties of the interfaces are triggered by the accumulation of mobile ionic defects. We show that the current rise of voltage step experiments allow for conclusions about the recombination at the interface. Whether one or two mobile ionic species are used in the model has only a minor influence on the observed effects. A delayed current rise observed upon reversing the bias from +3 to -3 V in the dark cannot be reproduced yet by our drift-diffusion model. We speculate that a reversible chemical reaction of mobile ions with the contact material may be the cause of this effect, thus requiring a future model extension. A parameter variation is performed in order to understand the performance-limiting factors of the device under investigation.

The Relationship between Change of Direction Tests in Elite Youth Soccer Players.

Change of direction (COD) is a performance-limiting factor in team sports. However, there are no exact definitions describing which physical abilities limit COD performance in soccer. Nevertheless, different COD tests are used or have been recommended as being equally effective in the professional practice of measuring COD performance. Therefore, the aim of this study was to evaluate the relationship between different COD tests, and to test the independence and generalizability of these COD tests in soccer. As such, 27 elite youth soccer players were randomly recruited and were tested in different COD tests (i.e., Illinois agility test (IAT), T agility test (TT), 505 agility test (505), Gewandtheitslauf (GewT), triangle test (Tri-t), and square test (SQT)). Bivariate Pearson correlation analysis was used to assess the relationships between the COD tests. The Benjamini–Hochberg method was used to control for the false discovery rate of the study at 0.05. This investigation calculated explained variances of 10% to 55% between performances in the different COD tests. This suggested that the tests covered different aspects or task-specific characteristics of the COD. Therefore, coaches and sport scientists should review and select different tests with a logical validity, based on the requirement profiles of the corresponding sport.

Impact of ionomer adsorption on alkaline hydrogen oxidation activity and fuel cell performance

Summary The adsorption of the ionomer components significantly impacts the activity of hydrogen oxidation reaction (HOR) catalysts under high pH conditions. Two specific adsorptions, i.e., (i) cation-hydroxide-water co-adsorption and (ii) phenyl group adsorption, on the surface of platinum group metal HOR catalysts were identified as the performance-limiting factors for anion-exchange membrane fuel cells (AEMFCs). Here, we review the main characteristics of the surface adsorptions, their impact on the performance of HOR half-cell and AEMFC, and the mitigation strategies. This review emphasizes important aspects in the design of electrocatalysts and ionomers for improved AEMFC performance.

Predictive Model for the Electrical Transport within Nanowire Networks.

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.