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Record W3082900176 · doi:10.1149/ma2021-02181mtgabs

Rational Design of Cell Configurations for High-Performance Na-O<sub>2</sub> Batteries

2021· article· en· W3082900176 on OpenAlexaff
Xiaoting Lin, Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAnodeBattery (electricity)Materials scienceEnergy storageSodiumEnergy densityDegradation (telecommunications)Chemical engineeringProcess engineeringNanotechnologyEngineering physicsElectrodeChemistryElectrical engineeringMetallurgyPower (physics)EngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Growing environmental concerns and continuously surging demand for energy have stimulated extensive interests in exploring advanced energy storage systems. Na-O 2 batteries have recently attracted an extensive amount of attention due to their high theoretical energy density, which is 6-9 folds higher than that of the conventional Li-ion batteries [1] . Moreover, the high round-trip energy efficiency, as well as the natural abundance and low cost of sodium resources make Na-O 2 batteries promising for large-scale application [2] . However, the practical application of Na-O 2 batteries has been hindered by their poor cycling performance [3] . Parasitic reactions and continuous consumption of the metallic Na anode caused by O 2 /O 2 - crossover are one of the main causes of Na-O 2 battery failure [4] . Except for the Na degradation, the implementation of metallic Na anode in Na-O 2 battery is associated with dendrite formation [5] . To address these issues, we successfully designed novel Na-O 2 cell configurations. Firstly, a novel Na-O 2 cell using electrically connected carbon paper (CP) with Na metal as a protected anode is developed. The CP demonstrates great effectiveness in addressing the fatal issue of cell short circuit by facilitating the dense Na deposition within the 3D CP skeleton. On the other hand, the CP acts as a protective layer to alleviate the Na corrosion caused by O 2 /O 2 - crossover. Consequently, a significantly enhanced Na-O 2 cell cycling performance with a low charge overpotential can be achieved. Since the diffusion of O 2 /O 2 - from the cathode to anode mainly through the electrolyte, and a physical barrier to retain the O 2 /O 2 - on the cathode side would also be effective. Therefore, for the first time, we successfully developed a hybrid solid-electrolyte Na-O 2 battery based on solid-state electrolyte (SSE) and a protected Na anode. The dense structure of SSE effectively suppressed the O 2 /O 2 - crossing over, which simultaneously mitigate the Na corrosion and decrease the reversible capacity loss. More importantly, the SSE is chemical stable against the O 2 - radical, benefiting to achieving high-performance Na-O 2 batteries with high capacities and long cycle lives. In conclusion, the importance of addressing issues of Na dendrite growth and O 2 /O 2 - crossover were presented. Although more future work is needed to make Na-O 2 battery system commercially viable, the strategies developed here provide guidance to achieve Na-O 2 cells with longer lifespans and better cycling performance. Reference [1] H. Yadegari, et al., Advanced Materials, 28 (2016) 7065-7093. [2] X. Li, et al. Carbon Energy, 2019, 1:141-164. [3] X. Lin, et al. Chemistry of Materials, 2020, 32, 7, 3018-3027. [4] S. Wu, et al. Advanced Functional Materials, 2018, 28, 1706374. [5] X. Bi, et al., Chemical Communications, 51 (2015) 7665-7668.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.206
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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