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Record W2962812409 · doi:10.1021/acscatal.9b02113

Opportunities and Challenges for Catalysis in Carbon Dioxide Utilization

2019· article· en· W2962812409 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.

Bibliographic record

VenueACS Catalysis · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsCanada Energy Regulator
FundersBasic Energy SciencesOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsCarbon dioxideElectrochemical reduction of carbon dioxideCarbon monoxideMethaneCatalysisChemistryCarbon dioxide removalCarbon dioxide in Earth's atmosphereCarbon-neutral fuelArtificial photosynthesisCarbon dioxide reformingFormic acidEnvironmental scienceSyngasBiochemical engineeringOrganic chemistryPhotocatalysisEngineering

Abstract

fetched live from OpenAlex

The environmental and societal consequences of the increasing levels of carbon dioxide in our atmosphere are among the most significant challenges society currently faces. Carbon dioxide utilization, in which carbon dioxide is either used directly or converted into more valuable products, is likely to be one component of a broad strategy to reduce carbon dioxide emissions, a challenge that will require both technological and policy changes. Catalysis is crucial to the successful conversion of carbon dioxide into value-added products. Here, we provide a review on chemical and biological systems for carbon dioxide conversion directed toward the readers of ACS Catalysis, which focuses on providing a general perspective on the field, rather than technical details. We discuss both challenges related to the conversion of carbon dioxide into specific products such as carbon monoxide, formic acid, methanol, methane, ethylene, fuels, carboxylic acids, and polymers as well as general challenges for the field. We also compare and contrast different methods for carbon dioxide conversion, for example homogeneous versus heterogeneous catalysis or photosynthetic versus nonphotosynthetic biological conversion, and highlight areas where one approach may have advantages over another. In a concluding section, we identify problems related to carbon dioxide conversion that will need to be addressed for technology to be both viable and reduce carbon dioxide emissions.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.073
GPT teacher head0.261
Teacher spread0.188 · 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