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Record W2967219840 · doi:10.1002/anie.201909222

Building a Bridge from Papermaking to Solar Fuels

2019· article· en· W2967219840 on OpenAlexaff
Zaiyong Jiang, Xinhan Zhang, Wei Sun, Deren Yang, Paul N. Duchesne, Yugang Gao, Zeyan Wang, Tingjiang Yan, Zhimin Yuan, Guihua Yang, Xingxiang Ji, Jiachuan Chen, Baibiao Huang, Geoffrey A. Ozin

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsPapermakingBlack liquorGrapheneMaterials scienceRenewable energyCarbon blackSolar energyRaw materialUltravioletOptoelectronicsLigninNanotechnologyChemistryComposite materialOrganic chemistryNatural rubber

Abstract

fetched live from OpenAlex

Abstract Black liquor, an industrial waste product of papermaking, is primarily used as a low‐grade combustible energy source. Despite its high lignin content, the potential utility of black liquor as a feedstock in products manufacturing, remains to be exploited. Demonstrated here in is the use of black liquor as a primary feed‐stock for synthesizing graphene quantum dots that exhibit both up‐conversion and photoluminescence when excited using visible/near‐infrared radiation, thereby enabling the photosensitization of ultraviolet‐absorbing TiO 2 nanosheets. In addition, these graphene quantum dots can trap photo‐generated electrons to realize the effective separation of electron‐hole pairs. Together, these two processes facilitate the solar‐powered generation of H 2 from H 2 O, and CO from H 2 O–CO 2 , using broadband solar radiation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.061
Threshold uncertainty score0.999

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)0.0040.001

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations35
Published2019
Admission routes1
Has abstractyes

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