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Record W4210793927 · doi:10.26434/chemrxiv-2021-0mjmt

A Low-cost Sulfate-based All Iron Redox Flow Battery

2021· preprint· en· W4210793927 on OpenAlexaff
Sicen Yu, Xiujun Yue, John Holoubek, Xing Xing, Eric Pan, Tod A. Pascal, Ping Liu

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsBishop's University
FundersUniversity of California, San DiegoAdvanced Research Projects AgencyAdvanced Research Projects Agency - EnergyU.S. Department of Energy
KeywordsSeparator (oil production)Microporous materialElectrolyteEnergy storageCathodeAnodeRedoxMembraneChemical engineeringChemistryMaterials scienceInorganic chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Redox flow batteries (RFBs) are promising choices for stationary electric energy storage. Nevertheless, commercialization is impeded by high-cost electrolyte and membrane materials. Here, we report a low-cost all-iron RFB that features inexpensive FeSO4 electrolytes, microporous membrane along with a glass fiber separator. The addition of 0.1 м 1-ethyl-3-methylimidazolium chloride (EMIC) overcomes the low solubility of FeSO4 in water which is raised to 2.2 м. DFT calculations demonstrate that EMI+ can strengthen the interaction between sulfate anions and water molecules. This electrolyte composition also allows both anode and cathode reactions to operate without actively maintaining a pH gradient between them, thus eliminating the need for expensive ion exchange membranes. The all-iron RFB demonstrates stable operation at a current density of 20 mA cm−2 for more than 800 cycles via a simple, periodic regeneration process. Furthermore, the system cost of FeSO4/EMIC RFBs is projected to be $ 50 per kWh due to its low-cost active materials and the inexpensive microporous membrane. This low-cost, high-concentration all-iron RFB is a promising stationary energy‐storage system for storing renewable energy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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.

Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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