Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable ...electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)-free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O
or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mg
) and 1.77 A mg
and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell. Computational modeling reveals that the interaction between platinum-cobalt nanoparticles and PGM-free sites improves ORR activity and durability.
The impact of metal/oxide interfaces on the catalytic properties of oxide-supported metal nanoparticles is a topic of longstanding interest in the field of heterogeneous catalysis. The significance ...of the metal/oxide interaction has been shown to vary according to both the inherent reactivity of the metal nanoparticle and the properties of the oxide support, with effects such as the metal
d-
band center, the nanoparticle shape, and the reducibility of the oxide believed to contribute to the overall system reactivity. In recent years, the water gas shift (WGS) reaction, wherein carbon monoxide and water are converted to carbon dioxide and hydrogen, has emerged as a model chemistry to probe the molecular-level details of how catalysis can be promoted in such environments, and this reaction is the focus of the present contribution. Using a combination of periodic Density Functional Theory calculations and microkinetic modeling, we present a comprehensive analysis of the WGS mechanism at the interface between a quasi-one dimensional platinum nanowire and an irreducible MgO support. The nanowire is lattice matched to the MgO support to remove spurious strain at the metal/oxide interface, and reactions both on the nanowire and at the three-phase boundary itself are considered in the mechanistic analysis. Additionally, to elucidate the consequences of adsorbate–adsorbate interactions on the WGS chemistry, an ab-initio thermodynamic analysis of CO coverage is performed, and the impact of the higher coverage CO states on the reaction chemistry is explicitly evaluated. These results are combined with detailed calculations of adsorbate entropies and dual-site microkinetic modeling to determine the kinetically significant features of the WGS reaction network which are subsequently, validated through experimental measurements of apparent reaction orders and activation barrier. The analysis demonstrates the important role that the metal/oxide interface plays in the reaction, with the water dissociation step being facile at the interface compared to the pure metal or oxide surfaces. Further, explicit consideration of CO interactions with other adsorbates at the metal/oxide interface is found to be central to correctly determining reaction mechanisms, rate determining steps, reaction orders, and effective activation barriers. These results are captured in a closed-form Langmuir–Hinshelwood model, derived from a simplified version of the complete microkinetic analysis, which reveals, among other results, that the celebrated carboxyl mechanism of Mavrikakis and coworkers is the governing pathway when accounting for reaction-relevant CO coverages.
In this article we describe efforts to improve performance and cycle life of cells containing Li1.2Ni0.15Mn0.55Co0.1O2-based positive and graphite-based negative electrodes. Initial work to identify ...high-performing materials, compositions, fabrication variables, and cycling conditions is conducted in coin cells. The resulting information is then used for the preparation of double-sided electrodes, assembly of pouch cells, and electrochemical testing. We report the cycling performance of cells with electrodes prepared under various conditions. Our data indicate that cells with positive electrodes containing 92 wt.% Li1.2Ni0.15Mn0.55Co0.1O2, 4 wt.% carbons (no graphite), and 4 wt.% PVdF (92–4–4) show ∼20% capacity fade after 1000 cycles in the 2.5–4.4 V range, significantly better than our baseline cells that show the same fade after only 450 cycles. Our analyses indicate that the major contributors to cell energy fade are capacity loss and impedance rise. Therefore incorporating approaches that minimize capacity fade and impedance rise, such as electrode coatings and electrolyte additives, can significantly enhance calendar and cycle life of this promising cell chemistry.
•Better component distribution and porosity control enhances electrode performance.•Carbons and binder in positive electrode are electrochemically active at high voltages.•Limit graphite content of high-voltage positive electrodes to improve performance.•Limit cycling window of high-capacity lithium-ion cells to improve calendar life.•Details of electrode fabrication and pouch cell assembly are provided.
The suitability of a battery for a given application depends on its metrics for energy (W h kg
−1
and/or W h L
−1
), power (W kg
−1
and/or W L
−1
), cost ($ per kWh), lifetime (cycles and/or years), ...and safety. This paper provides a data-driven perspective explaining how material properties, cell design decisions, and manufacturing costs influence and control these metrics. Insights drawn from the literature and past experience are supported by 200 000+ Monte Carlo simulations, which were conducted for lithium-ion batteries using the Battery Performance and Cost Model (BatPaC). A cell with optimal energy, power, and cost is best achieved with a high voltage and a low area specific impedance. If the focus is only on optimal energy and/or cost (
i.e.
, where power is less critical), cells also benefit from active materials with high specific capacities. For example, the energy metric of 500 W h kg
−1
can be met in cells with open circuit voltages less than 4 V only if the average specific capacity of the positive and negative materials is at least ∼500 mA h g
−1
. The values of other parameters (
e.g.
, thicknesses, densities, and material costs) are shown to have less influence on achieving cell metrics. It is suggested that the best way to achieve optimal energy, power, and/or cost while maintaining long lifetimes and safe operation is through modification of these other parameters to facilitate the stable operation of materials with high voltage, high capacity, and low area specific impedance. It is also shown that new negative active materials must produce cells with an area specific impedance less than 85 Ω cm
2
to be cost-competitive in all electric vehicles.
This perspective highlights the material properties, cell design decisions, and manufacturing costs with the biggest influence on the energy, power, cost, lifetime, and safety of a battery.
Mortality data are important tools for research requiring vital status information. We reviewed the major mortality databases and mortality ascertainment services available in the United States, ...including the National Death Index (NDI), the Social Security Administration (SSA) files, and the Department of Veterans Affairs databases.
The content, reliability, and accuracy of mortality sources are described and compared. We also describe how investigators can gain access to these resources and provide further contact information.
We reviewed the accuracy of major mortality sources. The sensitivity (i.e., the proportion of the true number of deaths) of the NDI ranged from 87.0% to 97.9%, whereas the sensitivity for the VA Beneficiary Identification and Records Locator System (BIRLS) ranged between 80.0% and 94.5%. The sensitivity of SSA files ranged between 83.0% and 83.6%. Sensitivity for the VA Patient Treatment File (PTF) was 33%.
While several national mortality ascertainment services are available for vital status (i.e., death) analyses, the NDI information demonstrated the highest sensitivity and, currently, it is the only source at the national level with a cause of death field useful for research purposes. Researchers must consider methods used to ascertain vital status as well as the quality of the information in mortality databases.
Although home-based health care has grown over the past decade, its effectiveness remains controversial. A prior trial of Veterans Affairs (VA) Team-Managed Home-Based Primary Care (TM/HBPC) found ...favorable outcomes, but the replicability of the model and generalizability of the findings are unknown.
To assess the impact of TM/HBPC on functional status, health-related quality of life (HR-QoL), satisfaction with care, and cost of care.
Multisite randomized controlled trial conducted from October 1994 to September 1998 in 16 VA medical centers with HBPC programs.
A total of 1966 patients with a mean age of 70 years who had 2 or more activities of daily living impairments or a terminal illness, congestive heart failure (CHF), or chronic obstructive pulmonary disease (COPD). Intervention Home-based primary care (n=981), including a primary care manager, 24-hour contact for patients, prior approval of hospital readmissions, and HBPC team participation in discharge planning, vs customary VA and private sector care (n=985).
Patient functional status, patient and caregiver HR-QoL and satisfaction, caregiver burden, hospital readmissions, and costs over 12 months.
Functional status as assessed by the Barthel Index did not differ for terminal (P=.40) or nonterminal (those with severe disability or who had CHF or COPD) (P=.17) patients by treatment group. Significant improvements were seen in terminal TM/HBPC patients in HR-QoL scales of emotional role function, social function, bodily pain, mental health, vitality, and general health. Team-Managed HBPC nonterminal patients had significant increases of 5 to 10 points in 5 of 6 satisfaction with care scales. The caregivers of terminal patients in the TM/HBPC group improved significantly in HR-QoL measures except for vitality and general health. Caregivers of nonterminal patients improved significantly in QoL measures and reported reduced caregiver burden (P=.008). Team-Managed HBPC patients with severe disability experienced a 22% relative decrease (0.7 readmissions/patient for TM/HBPC group vs 0.9 readmissions/patient for control group) in hospital readmissions (P=.03) at 6 months that was not sustained at 12 months. Total mean per person costs were 6.8% higher in the TM/HBPC group at 6 months ($19190 vs $17971) and 12.1% higher at 12 months ($31401 vs $28008).
The TM/HBPC intervention improved most HR-QoL measures among terminally ill patients and satisfaction among non-terminally ill patients. It improved caregiver HR-QoL, satisfaction with care, and caregiver burden and reduced hospital readmissions at 6 months, but it did not substitute for other forms of care. The higher costs of TM/HBPC should be weighed against these benefits.
Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery ...testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. A mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.
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•Unique Li-ion dataset comprised 6 metal oxide cathode chemistries and 300 batteries.•A single machine learning model accurately predicted cycle life across cathodes.•Useful predictions required as few as one preliminary cycle.•Broad feature sets are most accurate; categorically narrow sets are viable.•A diverse training set improved predictive performance for new cathode chemistries.
Early prediction of battery performance degradation trends can facilitate research of new materials and cell designs, rapid deployment of batteries in real-world applications, timely replacement of ...batteries in critical applications, and even the secondary use market. In this study, we design a convolutional neural network model to predict the entire battery capacity fade curve – a critical indicator of battery performance degradation – using first 100 cycles of data (∼ three weeks of testing). We use the discharge voltage-capacity curves as input to the model and automate the feature extraction process through the convolutional layers of the network. Our approach can predict the per cycle capacity fade rate and rollover cycle (knee point) in the capacity fade curve, which indicate the onset of rapid capacity decay. On the publicly available graphite/LiFePO4 battery dataset, optimized networks predict the capacity fade curves, rollover cycle, and end of life with 3.7% (worst-case), 19%, and 17% mean absolute percentage errors, respectively.
•A convolutional neural network to predict the entire battery capacity fade trend.•Capacity fade trend prediction using first 100 cycles of data.•Model training and validation using dataset of 178 graphite/LiFePO4 batteries.•A bilinear equation to describe capacity fade trend, fade rate, and knee point.
The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the ...batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. This paper compares battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.
•BEV prices are calculated using public data and BatPaC-predicted battery cost.•A BEV200 can break even with an equivalent ICEV in 6 years.•A BEV favorability index is defined to combine consumer and environmental factors.•Long-range BEV will be more favorable with economic incentives and policy supports.