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Record W4387865169 · doi:10.1002/cssc.202300795

Impact of Temperature an Order of Magnitude Larger Than Electrical Potential in Lignin Electrolysis with Nickel

2023· article· en· W4387865169 on OpenAlexafffund
Andrew Carkner, Ingy Tageldin, Jiashuai Han, Ali Seifitokaldani, Jan Kopyscinski

Bibliographic record

VenueChemSusChem · 2023
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsMcGill University
FundersTechnische Universität MünchenMcGill University
KeywordsVanillinGuaiacolLigninSyringaldehydeChemistryVanillic acidCatalysisChemical engineeringBiomass (ecology)Inorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lignin, a major component of plant biomass, is a promising sustainable alternative carbon‐based feedstock to petroleum as a source of valuable aromatic compounds such as vanillin. However, lignin upgrading reactions are poorly understood due to its complex and variable molecular structure. This work focuses on electrocatalytic lignin upgrading, which is efficient and sustainable at moderate temperatures and pressures and does not require stoichiometric reagents. We used a meta‐analysis of published lignin conversion and product yield data to define the operating range, to select the catalyst, and then performed electrocatalytic experiments. We quantified the impact of temperature and electrical potential on the formation rate of valuable products (vanillic acid, acetovanillone, guaiacol, vanillin, and syringaldehyde). We found that increasing temperature increases their formation rate by an order of magnitude more than increasing electrical potential. For example, increasing temperature from 21 to 180 °C increases the vanillin formation rate by +16.5 mg⋅L −1 ⋅h −1 ±1.7 mg⋅L −1 ⋅h −1 , while increasing electrical potential from 0 to 2 V increases the vanillin formation rate by −0.6 mg⋅L −1 ⋅h −1 ±1.4 mg⋅L −1 ⋅h −1 .

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 categoriesnone
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 score0.004
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2023
Admission routes2
Has abstractyes

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