MétaCan
Menu
Back to cohort
Record W2807499406 · doi:10.3390/ma11060936

Environmental Screening of Electrode Materials for a Rechargeable Aluminum Battery with an AlCl3/EMIMCl Electrolyte

2018· article· en· W2807499406 on OpenAlexaff
Linda Ager‐Wick Ellingsen, Alex Holland, Jean‐François Drillet, Willi Peters, Martin Eckert, Carlos Concepcion, O. Ruiz, J. Colin, Etienne Knipping, Qiaoyan Pan, R.G.A. Wills, Guillaume Majeau‐Bettez

Bibliographic record

VenueMaterials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsPolytechnique Montréal
FundersEngineering and Physical Sciences Research CouncilHorizon 2020 Framework Programme
KeywordsElectrolyteBattery (electricity)ElectrodeMaterials scienceAluminiumNanotechnologyInorganic chemistryMetallurgyChemistryPhysics

Abstract

fetched live from OpenAlex

Recently, rechargeable aluminum batteries have received much attention due to their low cost, easy operation, and high safety. As the research into rechargeable aluminum batteries with a room-temperature ionic liquid electrolyte is relatively new, research efforts have focused on finding suitable electrode materials. An understanding of the environmental aspects of electrode materials is essential to make informed and conscious decisions in aluminum battery development. The purpose of this study was to evaluate and compare the relative environmental performance of electrode material candidates for rechargeable aluminum batteries with an AlCl₃/EMIMCl (1-ethyl-3-methylimidazolium chloride) room-temperature ionic liquid electrolyte. To this end, we used a lifecycle environmental screening framework to evaluate 12 candidate electrode materials. We found that all of the studied materials are associated with one or more drawbacks and therefore do not represent a "silver bullet" for the aluminum battery. Even so, some materials appeared more promising than others did. We also found that aluminum battery technology is likely to face some of the same environmental challenges as Li-ion technology but also offers an opportunity to avoid others. The insights provided here can aid aluminum battery development in an environmentally sustainable direction.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 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

Citations34
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMaterialsSame topicAdvancements in Battery MaterialsFrench-language works237,207