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Record W4401903854 · doi:10.1002/anie.202406398

Engineering Polyheptazine and Polytriazine Imides for Photocatalysis

2024· review· en· W4401903854 on OpenAlexaff
Liquan Jing, Zheng Li, Zhangxin Chen, Rengui Li, Jinguang Hu

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typereview
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhotocatalysisImideCharacterization (materials science)Materials scienceNanotechnologyTriazineSemiconductorChemistryCatalysisOrganic chemistryPolymer chemistryOptoelectronics

Abstract

fetched live from OpenAlex

As organic semiconductor materials gain increasing prominence in the realm of photocatalysis, two carbon-nitrogen materials, poly (heptazine imide) (PHI) and poly (triazine imide) (PTI), have garnered extensive attention and applications owing to their unique structure properties. This review elaborates on the distinctive physical and chemical features of PHI and PTI, emphasizing their formation mechanisms and the ensuing properties. Furthermore, it elucidates the intricate correlation between the energy band structures and various photocatalytic reactions. Additionally, the review outlines the primary synthetic strategies for constructing PHI and PTI, along with characterization techniques for their identification. It also summarizes the primary strategies for enhancing the photocatalytic performance of PHI and PTI, whose advantages in various photocatalytic applications are discussed. Finally, it highlights the promising prospects and challenges of PHI and PTI as photocatalysts.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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

Citations40
Published2024
Admission routes1
Has abstractyes

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