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Record W2770904715 · doi:10.1002/chem.201704440

Highly Sustainable Zinc Anodes for a Rechargeable Hybrid Aqueous Battery

2017· article· en· W2770904715 on OpenAlexafffund
Kyung Eun Kate Sun, Tuan K.A. Hoang, The Nam Long Doan, Yan Yu, Pu Chen

Bibliographic record

VenueChemistry - A European Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersMitacs
KeywordsZincMaterials scienceAnodeGalvanic anodeElectroplatingCorrosionElectrolyteAqueous solutionElectrochemistryBoric acidBattery (electricity)MetallurgyElectrodeChemical engineeringCathodeInorganic chemistryCathodic protectionChemistryNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract The synthesis of novel zinc electrodes has been successfully implemented by using the electroplating method with the aid of inorganic additives in the electroplating solution. The selected inorganic additives are indium sulfate, tin oxide, and boric acid. From X‐ray diffraction results, these synthesized zinc electrodes prefer (002) and/or (103) crystallographic orientations, representing basal morphology and high resistance to dendrite growth. The corrosion rates of these electroplated zinc samples decrease as much as 11 times smaller than the corrosion rate on zinc foil when the zinc materials are in contact with the aqueous electrolyte of a rechargeable hybrid aqueous battery (ReHAB). The ReHABs employing these anodes exhibit up to a threefold decrease in float charge current density after a seven‐day constant‐voltage charging at 2.1 V versus Zn2+/Zn. Furthermore, the capacity retention is up to 15 % higher than the performance of battery containing commercial Zn after 1000 cycles of charge–discharge. The significant advancements are attributed to the careful preparation of the anode, which contains appropriate crystallographic orientation and morphology.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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 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

Citations168
Published2017
Admission routes2
Has abstractyes

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