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Record W4389096837 · doi:10.1080/01614940.2023.2275093

Thermochemical conversion of biomass to fuels and chemicals: a review of catalysts, catalyst stability, and reaction mechanisms

2023· review· en· W4389096837 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCatalysis Reviews · 2023
Typereview
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersMitacs
KeywordsCatalysisBiomass gasificationBiomass (ecology)Waste managementSyngasChemistrytar (computing)Natural gasChemical engineeringEnvironmental scienceEngineeringBiofuelOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Woody biomass can be converted into a synthesis gas, a mixture of H2, CO, and CO2, by gasification. Removal of refractory tars, produced during biomass gasification, requires highly active catalysts that can be applied in-situ during gasification or in secondary catalytic tar cracking reactors. Subsequently, the water-gas-shift (WGS) reaction adjusts the H2 to CO ratio, prior to the synthesis of the desired products (CH4 as renewable natural gas or RNG, alcohols, hydrocarbons, and olefins). Catalysts play a pivotal role in all processing steps, with recent advances in catalyst development discussed herein with an emphasis on catalyst stability, reaction mechanisms, and structure–activity relationships. The economic viability of biomass conversion to fuels and chemicals is also reviewed and shown to be dependent on end-product, location, and government incentives.

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.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.482
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.068
GPT teacher head0.332
Teacher spread0.264 · 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