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Record W3023098712 · doi:10.1002/cssc.202001134

Photoelectrochemical Decomposition of Lignin Model Compound on a BiVO<sub>4</sub> Photoanode

2020· article· en· W3023098712 on OpenAlexafffund
Tengfei Li, Jia Yi Mo, David M. Weekes, Kevan E. Dettelbach, Ryan P. Jansonius, Glenn M. Sammis, Curtis P. Berlinguette

Bibliographic record

VenueChemSusChem · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsLigninDecompositionPhotoelectrochemistryNanotechnologyChemistryMaterials scienceChemical engineeringElectrochemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract The photoelectrochemical decomposition of lignin model compounds at a BiVO 4 photoanode is demonstrated with simulated sunlight and an applied bias of 2.0 V. These prototypical lignin model compounds are photoelectrochemically converted into the corresponding aryl aldehyde and phenol derivatives in a single step with conversion of up to ≈64 % over 20 h. Control experiments suggest that vanadium sites are electrocatalytically active, which precludes the need for a redox mediator in solution. This feature of the system is corroborated by a layer of V 2 O 5 deposited on BiVO 4 serving to boost the conversion by 10 %. Our methodology capitalizes on the reactive power of sunlight to drive reactions that have only been studied previously by electrochemical or catalytic methods. The use of a BiVO 4 photoanode to drive lignin model decomposition therefore provides a new platform to extract valuable aromatic chemical feedstocks using solar energy, electricity and biomass as the only inputs.

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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations31
Published2020
Admission routes2
Has abstractyes

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