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Record W4297904160 · doi:10.1002/cptc.202200193

Visible‐Light‐Driven Lignin Valorization into Value‐Added Chemicals and Sustainable Hydrogen Using Zn<sub>1‐<i>x</i></sub>Cd<sub><i>x</i></sub>S Solid Solutions as Photocatalyst

2022· article· en· W4297904160 on OpenAlexaff
Bruna Palma, Xi Cheng, Liyang Liu, Na Zhong, Scott Renneckar, Steve Larter, Md Golam Kibria, Jinguang Hu

Bibliographic record

VenueChemPhotoChem · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsPhotocatalysisLigninHydrogenBiomass (ecology)Hydrogen productionChemical engineeringMaterials scienceCarbon fibersSolid solutionSubstrate (aquarium)Band gapChemistryNanotechnologyCatalysisOrganic chemistryOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Abstract Biomass photorefinery for simultaneous production of value‐added chemicals and sustainable hydrogen holds promising perspective to achieve a negative carbon economy. However, insight into lignin valorization by the photorefinery approach is still lacking due to the extremely complex structure and component. In this work, we design a series of Zn 1‐ x Cd x S solid solutions as photocatalyst to reveal the photocatalytic mechanism for model component conversion. Bandgap engineering by changing the Zn/Cd ratio is proven to efficiently regulate the reaction pathway. Specifically, Zn 0.75 Cd 0.25 S (ZCS25) presented the best performance for hydrogen evolution (610±105 μmol h −1 g cat. −1 ) while Zn 0.5 Cd 0.5 S (ZCS50) demonstrated the best substrate conversion to monophenolic compounds. Besides, the well‐designed Zn 1‐x Cd x S solid solutions also show the ability to produce hydrogen and eliminate a methoxy group from real lignin by the photorefinery approach. This present work demonstrates a good example of biomass valorization by careful design of 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
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 score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 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

Citations10
Published2022
Admission routes1
Has abstractyes

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