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Record W4229014509 · doi:10.1021/acssuschemeng.2c00309

Metal-Free Sulfonate/Sulfate-Functionalized Carbon Nitride for Direct Conversion of Glucose to Levulinic Acid

2022· article· en· W4229014509 on OpenAlexafffund
Pawan Kumar, Ali Shayesteh Zeraati, Soumyabrata Roy, Kristen A. Miller, Aiguo Wang, Lawrence B. Alemany, Tareq A. Al‐Attas, Dhwanil Trivedi, Pulickel M. Ajayan, Jinguang Hu, Md Golam Kibria

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence Fund
KeywordsLevulinic acidCatalysisChemistrySulfurDibenzothiopheneHeteroatomSelectivityGraphitic carbon nitrideSulfonic acidYield (engineering)Carbon nitrideInorganic chemistryCarbon fibersSulfonateOrganic chemistryMaterials sciencePhotocatalysis

Abstract

fetched live from OpenAlex

Metal-free heteroatom-doped carbonaceous materials such as carbon nitride (CN) with secondary/tertiary nitrogen-rich catalytic centers as well as chemical and thermal resilience can potentially serve as catalysts for many organic reactions. However, because of the stable alternate Csp2–Nsp2 configuration of N-linked heptazine units (C6N7), the chemical modification of CN via doping and functionalization has been a critical challenge. Herein, we report an exceptional 9.2% sulfur content in CN with sulfonate/sulfate functional groups (CNS) via a one-step in situ synthesis approach. When used as a catalyst for the dehydration/hydration of glucose, CNS catalysts demonstrate a relatively high yield and selectivity toward levulinic acid, LLA, (≈48% yield with 57% selectivity) production. CNS’s high activity of direct conversion of glucose to LLA can be attributed to the synergistic catalytic effects of multiple sulfur functionalities, better dispersibility, and microstructural porosity. The synthesized CNS catalysts offer an energy efficient direct LLA production route to bypass the multistep process of sugar to LLA conversion.

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)0.001
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 score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

Explore more

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