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Record W2913005935 · doi:10.1002/aenm.201803354

Zwitterions for Organic/Perovskite Solar Cells, Light‐Emitting Devices, and Lithium Ion Batteries: Recent Progress and Perspectives

2019· article· en· W2913005935 on OpenAlexaff
Amjad Islam, Jianguo Li, Muhammad Pervaiz, Zheng‐Hong Lu, Mohini Sain, Lihui Chen, Xinhua Ouyang

Bibliographic record

VenueAdvanced Energy Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersFujian Agriculture and Forestry UniversityNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsZwitterionMaterials sciencePerovskite (structure)Lithium (medication)OLEDElectrolyteOrganic solar cellNanotechnologyElectrodeChemical engineeringPolymerChemistryLayer (electronics)Organic chemistryMoleculeComposite material

Abstract

fetched live from OpenAlex

Abstract Zwitterions, a class of materials that contain covalently bonded cations and anions, have been extensively studied in the past decades owing to their special features, such as excellent solubility in polar solvents, for solution processing and dipole formation for the transfer of carriers and ions. Recently, zwitterions have been developed as electrode modifiers for organic solar cells (OSCs), perovskite solar cells (PVSCs), and organic light‐emitting devices (OLEDs), as well as electrolyte additives for lithium ion batteries (LIBs). With the rapid advances of zwitterionic materials, high‐performance devices have been constructed with enhanced efficiencies by introducing them as interface layers and electrolyte additives. In this review, recent progress in OSCs, PVSCs, OLEDs, and LIBs by using zwitterions is highlighted. The authors also elaborate the role of various zwitterionic materials as interfacial layers and additives for highly efficient OSCs, PVSCs, OLEDs, and LIBs. This article presents an overview of device performance of zwitterionic materials. The structure–property relationship is also discussed. Finally, the prospects of zwitterion materials are also addressed.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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

Citations102
Published2019
Admission routes1
Has abstractyes

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