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Record W3161093683 · doi:10.1002/cssc.202100550

Physicochemical and Electrochemical Properties of Water‐in‐Salt Electrolytes

2021· review· en· W3161093683 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemSusChem · 2021
Typereview
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteElectrochemistryElectrochemical windowAqueous solutionConductivityChemical engineeringMaterials scienceChemistryInorganic chemistryElectrodeIonic conductivityOrganic chemistry

Abstract

fetched live from OpenAlex

Aqueous electrolytes are attractive for applications in electrochemical technologies due to features like being eco-friendly, cost effective, and non-flammable. Very recently, superconcentrated aqueous electrolytes, such as so-called water-in-salt, water-in-bisalt, and hydrate melt, have received a significant attention for electrochemical energy storage due to enhanced stability and much wider electrochemical stability window. This Review focuses on the physicochemical properties of the highly concentrated electrolytes that are derived from several analysis techniques and simulation. A summary of most common features such as ions-water interactions, structure of species present in the electrolyte, conductivity, and viscosity of the electrolytes found in the literature are presented as well. In addition, this Review explains how these characteristics affect the electrochemical behavior of the electrolyte such as double layer structure and electrode/electrolyte interface leading to enhanced electrochemical stability of aqueous electrolytes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.278
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