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Record W4393232877 · doi:10.1039/bk9781837672035-00106

Chemical transformations using GaN-based catalysts

2024· book-chapter· en· W4393232877 on OpenAlexaff
Jing‐Tan Han, Lida Tan, Hui Su, Chao‐Jun Li

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
Fundersnot available
KeywordsCatalysisMaterials scienceChemical engineeringChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Gallium nitride (GaN), a wide bandgap III–V semiconductor, has been extensively applied in lighting, electronics, and radiofrequency devices over the last few decades. With the distinct properties of fast charge mobility, high stability, tunable wide bandgap, and ionicity structure, GaN-based catalysts have drawn considerable attention in chemical synthesis recently. In this chapter, the recent progress and critical breakthrough of GaN-based catalysis in synthesis are reviewed, with a focus on mechanistic understanding. The reactions are categorized as water splitting, direct methane activation, direct methanol activation, organic synthesis, carbon dioxide reduction, and nitrogen gas reduction. Lastly, the challenges and future possible improvement of GaN-based catalysis are discussed, to encourage more interdisciplinary advances in the development of novel catalysts for sustainable chemical transformations.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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