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Record W2747147268 · doi:10.1002/cnma.201700183

Acquiring an Efficient Warm‐CO<sub>2</sub> Sorbent from Advanced Pyrolysis of Magnesium Oxalate

2017· article· en· W2747147268 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

VenueChemNanoMat · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsSorbentFlue gasAdsorptionPyrolysisMagnesiumPorosityMaterials scienceSpecific surface areaOxalateChemical engineeringNitrogenSolventSalt (chemistry)MineralogyInorganic chemistryChemistryMetallurgyComposite materialOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract For the first time, porous MgO with a Brunauer–Emmett–Teller (BET) surface area over 200 m 2 g −1 can be simply obtained from pyrolysis of common magnesium oxalate without any solvent or additive. The obtained porous MgO had a large surface area of 278.5 m 2 g −1 and captured 30.8 mg g −1 of CO 2 in the instantaneous adsorption at 473 K, comparable with many MgO‐based sorbents prepared through complex procedures. Use of a U‐pipe furnace along with the specific “throughout” sweeping mode of nitrogen carrier gas enable this efficient warm‐CO 2 sorbent to be fabricated in a simple way. Factors including contacting modes between precursor salt and flowing gas, flow rate, type of gas and salt were carefully studied, and related to the pore structure and adsorption character of the MgO samples. Apart from the capability of trapping CO 2 mixed with SO 2 and NO at 473 K, the MgO sample showed a high ratio of exposed strong basic sites (70.5 %), which provides a useful solid strong base for control of CO 2 in flue gas.

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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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