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Record W4404109600 · doi:10.1002/chem.202403273

Recent Advances in NIR‐Switchable Multi‐Redox Systems Based on Organic Molecules

2024· review· en· W4404109600 on OpenAlexaff
Takashi Harimoto, Yusuke Ishigaki

Bibliographic record

VenueChemistry - A European Journal · 2024
Typereview
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsScience North
FundersPrecursory Research for Embryonic Science and TechnologyJapan Society for the Promotion of Science
KeywordsElectrochromismRedoxAbsorption (acoustics)NanotechnologyMaterials scienceNear-infrared spectroscopyElectrochromic devicesSmall moleculeElectrochemistryOrganic moleculesMoleculeChemistryElectrodeOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Electrochromic systems capable of switching absorption in the near-infrared (NIR) region (750-2500 nm) are attractive from the viewpoint of applications for material and life science, and thus several examples have been reported to date. In general, the development of organic-based systems is needed to reduce the environmental impact and improve biocompatibility. Although extending the switchable spectral range is crucial for the application of organic electrochromic molecules, the switching of NIR absorption based on redox interconversion is still a challenging issue regarding reversibility and durability during interconversion. To overcome this potential instability, the introduction of heteroatoms into the molecular backbone and/or π-extension could be useful strategies in terms of effective delocalization of charge and spin in the corresponding redox states. In this review, we focus on redox-active well-defined small molecules that enable ON/OFF switching of NIR absorption based on precise control of the redox states, and present recent studies on their intrinsic electrochemical and spectroscopic properties and/or structural characterization of their charged states.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.314
Teacher spread0.268 · 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 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

Citations14
Published2024
Admission routes1
Has abstractyes

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