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Record W4286001405 · doi:10.26434/chemrxiv-2022-k20zm

Cement clinker production in an electrolyser

2022· preprint· en· W4286001405 on OpenAlexafffund
Zishuai Zhang, Benjamin A. W. Mowbray, Colin Parkyn, Chris Waizenegger, Aubry S. R. Williams, Eric W. Lees, Shaoxuan Ren, Ryan P. Jansonius, Curtis P. Berlinguette

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
FundersNatural Resources CanadaCanada Research Chairs
KeywordsCementTonneClinker (cement)Waste managementLimeMaterials scienceCarbon dioxideEnvironmental scienceMetallurgyPortland cementPulp and paper industryChemistryEngineering

Abstract

fetched live from OpenAlex

The manufacture of cement from limestone is the single largest industrial source of CO2(g) emissions into the atmosphere. We report here an electrochemical flow reactor (electrolyser) that continuously converts limestone (CaCO3(s)) into Ca(OH)2(s) at a high rate of product formation (486 mg h-1 at 100 mA cm-2). The Ca(OH)2(s) product (slaked lime) is a chemical precursor to cement clinker, the main component of Portland cement, and other cement varieties. This three-compartment electrolyser operates with ~100% current efficiency at a cell voltage of 2.9 V and generates pure O2(g), H2(g), and CO2(g) streams that can be utilized downstream without purification. To demonstrate this feature, we feed the CO2(g) released from limestone directly to a second electrolyser that valorizes CO2(g) into higher value carbon-containing products (e.g., CO). A life-cycle analysis indicates that the proposed electrochemical process can decrease CO2 emissions per tonne of cement by 75% and achieve cost-parity with incumbent cement manufacturing processes with a carbon tax of $50/tonne CO2.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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
Published2022
Admission routes2
Has abstractyes

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