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Ionic liquids–an Overview

2011· article· en· W2093631404 on OpenAlexaff
H. Donald Brooke Jenkins

Bibliographic record

VenueScience Progress · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsSimon Fraser University
FundersUniversity of Warwick
KeywordsGlossaryScale (ratio)Ionic liquidComputer scienceNanotechnologyChemistryBiochemical engineeringEngineeringMaterials scienceOrganic chemistryPhilosophyPhysics

Abstract

fetched live from OpenAlex

A virtually unprecedented exponential burst of activity resulted following the publication, in 1998, of an article by Michael Freeman (Freemantle, M. Chemical & Engineering News, 1998, March 30, 32), which speculated on the role and contribution that ionic liquids (ILs) might make in the future on the development of clean technology. Up until that time only a handful of researchers were routinely engaged in the study of ILs but frenzied activity followed that continues until the present day. Scientists from all disciplines related to Chemistry have now embarked on studies, including theoreticians who are immersed in the aim of improving the "designer role" so that they can tailor ILs to deliver specified properties. This article, whilst not in any sense attempting to be exhaustive, highlights the main features which characterise ILs, presenting these in a form readily assimilated by newcomers to this area of research. An extensive glossary is featured in this article as well as a chronological list which charts the major areas of development. What follows consists of a number of sections briefly describing the role of lLs as solvents, hypergolic fuels, their use in some electrochemical devices such as solar cells and lithium batteries and their use in polymerisation reactions, followed by a concise summary of some of the other roles that they are capable of playing. The role of empirical, volume-based thermodynamics procedures, as well as large scale computational studies on ILs is also highlighted. These developments which are described are remarkable in that they have been achieved in less than a decade and a half although knowledge of these materials has existed for much longer.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.013

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 head0.098
GPT teacher head0.320
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations38
Published2011
Admission routes1
Has abstractyes

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