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Record W2441069467 · doi:10.1021/acs.jpcc.5b02673

Toward a Sodium–“Air” Battery: Revealing the Critical Role of Humidity

2015· article· en· W2441069467 on OpenAlexaff
Qian Sun, Hossein Yadegari, Mohammad Norouzi Banis, Jian Liu, Biwei Xiao, Xia Li, Craig Langford, Ruying Li, Xueliang Sun

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsRelative humidityBattery (electricity)ElectrochemistrySodiumHumidityOxygenLithium (medication)Materials scienceChemical engineeringChemistryElectrodeMetallurgyMeteorologyOrganic chemistry

Abstract

fetched live from OpenAlex

Room temperature sodium–air batteries have a similar design and concept as lithium–air batteries. Using ambient air instead of pure oxygen as oxygen source is challenging because the minor components in air could lead to various side reactions and influence the electrochemical reaction route. Although water is an innegligible component in air, its impact on Li– and Na–air batteries is often underestimated. In this study, the electrochemical behavior of Na–air batteries under different relative humidity (RH) has been systemically investigated by galvanic cycling and cyclic voltammetry tests, as well as the identification of corresponding discharge products by physical characterizations such as XRD, FT-IR, and SEM. The reaction mechanisms of Na–air batteries under humid conditions are revealed and discussed. Na–air batteries suffer from more severe impact from the water content in air than Li–air batteries. NaOH and its derivatives are found to form and are proven to be fatal to the cells under humid ambience. Understanding the reaction mechanisms occurred in sodium air batteries under dry and humid ambient is critical to design and develop sodium–air batteries of high performance and long durability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations83
Published2015
Admission routes1
Has abstractyes

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