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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 OpenAlexafffund
Haiyan Wang, Shida Liu, Haoqi Wang, Junnan Chao, Naoko Ellis, Wenli Duo, Xiaotao Bi, Kevin J. Smith

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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

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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2023
Admission routes2
Has abstractyes

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