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Record W4384704243 · doi:10.37871/jbres1774

Strategy for Climate Crisis: Introducing Innovative System for CO2 Fixation and Storage

2023· article· en· W4384704243 on OpenAlexaff
Kenji Sorimachi, Toshinori Tsukada, Wataru Kobayashi, Hossam A. Gabbar

Bibliographic record

VenueJournal of Biomedical Research & Environmental Sciences · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsElectrolysisRenewable energyProcess engineeringElectricityEnergy storageEnvironmental scienceCarbon fixationCarbon fibersWaste managementComputer scienceCarbon dioxideChemistryPower (physics)EngineeringElectrical engineeringElectrodeThermodynamics

Abstract

fetched live from OpenAlex

The so-called Paris Agreement was reached at the United Nations Climate Change Conference (COP20) in 2015. This agreement was based on the requirement to keep the increase in the mean global temperature below 2°C relative to the temperature prior to the industrial revolution, and preferably less than 1.5°C. At present, this goal is challenging based solely on the development of carbon-neutral energy systems. The concept of the carbon-neutral society by 2050 seems to be far too late. Herein, we propose an innovative system based on simple chemical reactions using NaOH and CaCl2 via the electrolysis of NaCl or seawater. The generated H2 from the electrolysis of NaCl can be used as a clean energy source for fuel batteries, supplying electricity for the operation of the system. When other renewable energy sources power the system, H2 can be generated as a clean energy alternative. Furthermore, this system produces stable and harmless CaCO3 as a final product, along with NaCl, which can be reused as an electrolysis starting material. The proposed system provides a safe and inexpensive approach for simultaneous CO2 fixation and storage.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.052
GPT teacher head0.342
Teacher spread0.290 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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