MétaCan
Menu
Back to cohort
Record W2806763737 · doi:10.1002/celc.201800572

Surface Adsorption of Polyethylene Glycol to Suppress Dendrite Formation on Zinc Anodes in Rechargeable Aqueous Batteries

2018· article· en· W2806763737 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemElectroChem · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrolyteAnodeMaterials scienceAqueous solutionDendrite (mathematics)PEG ratioPolyethylene glycolZincChemical engineeringBattery (electricity)Galvanic anodeCapacity lossElectrochemistryElectrodeInorganic chemistryChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Aqueous metal batteries routinely suffer from the dendritic growth at the anode, leading to significant capacity fading and ultimately, battery failure from short‐circuit. Herein, we utilize polyethylene glycol to regulate dendrite growth and improve the long‐term cycling stability of an aqueous rechargeable lithium/zinc battery. PEG200 in the electrolyte decreases the corrosion and chronoamperometric current densities of the zinc electrode up to four‐fold. Batteries with pre‐grown dendrites also perform significantly better when PEG is present in the electrolyte (41.4 mAh g −1 vs. 7.9 mAh g −1 after 1000 cycles). X‐ray diffraction and electron microscopy studies show that dendrites in the PEG‐containing electrolyte have been inhibited, leading to much smaller/smoother surface features than those of the control. The facile preparation process of the aqueous electrolyte combined with low cost and vast performance improvement in batteries of all sizes indicates high upscaling viability.

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.

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 score0.005
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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