MétaCan
Menu
Back to cohort
Record W2118832227 · doi:10.1351/pac200476071435

Near-infrared absorbing organic materials

2004· article· en· W2118832227 on OpenAlexafffund
Zhenyu Wang, Jinlong Zhang, Xiaoyang Wu, Maria Birau, Guomin Yu, Hongan Yu, Yujin Qi, Xue Meng, Jian Gao, Ethan Todd, Ning Song, Andrew M. R. Beaudin, Gaëtan LeClair

Bibliographic record

VenuePure and Applied Chemistry · 2004
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryElectrochromismNear-infrared spectroscopyValence (chemistry)LanthanidePolymerElectrochromic devicesInfraredMetalNanotechnologyOptoelectronicsOrganic chemistryOpticsElectrodePhysical chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Organic solids and polymers that absorb in the near-infrared (NIR) region (1000–2000 nm) represent a class of emerging materials and show a great potential for use in photonics and telecommunications. The radical anions of stacked aromatic imides, fused phorphyrin arrays, polythiophenes, sandwich-type lanthanide bisphthalocyanines, semiquinones, and mixed-valence dinuclear metal complexes are a few known examples of NIR-absorbing organic materials. Most of these NIR-absorbing materials are also electro- chemically active or electrochromic (EC). This brief review covers several types of NIR-absorbing organic materials and discusses their potential for applications in EC variable optical attenuators (VOAs).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.007

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 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

Citations75
Published2004
Admission routes2
Has abstractyes

Explore more

Same venuePure and Applied ChemistrySame topicConducting polymers and applicationsFrench-language works237,207