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Record W2785607768

Developments into probing Ni catalyzed liquid phase biomass conversion

2015· article· en· W2785607768 on OpenAlexfundno aff
Stanislav Kasakov

Bibliographic record

VenuemediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · 2015
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryArgonne National LaboratoryBasic Energy SciencesOffice of ScienceUniversity of WashingtonCanadian Light SourceTechnische Universität MünchenBattelleU.S. Department of Energy
KeywordsHydrodeoxygenationHydrogenolysisCatalysisLigninChemistryOrganic chemistryDepolymerizationPhase (matter)PhenolDehydrationBiomass (ecology)EtherSelectivity
DOInot available

Abstract

fetched live from OpenAlex

Ni nanoparticles supported on sulfonated carbon have been developed for phenol hydrodeoxygenation in liquid phase. Through combination of kinetic and spectroscopic studies the dehydration was elucidated to be the rate determining reaction. While the hydrodeoxygenation was determined by hydrogenation and elimination reactions, the successful deconstruction of lignin was mainly driven by hydrogenolysis of ether bonds in lignin polymer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.012
GPT teacher head0.197
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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