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Record W2790734361 · doi:10.1002/cjce.23151

Changes in physicochemical properties of activated carbon during treatment with supercritical water

2018· article· en· W2790734361 on OpenAlexvenueno aff
Yuechao Zhang, Senlin Tian, Junjie Gu, Ping Ning, Yingjie Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsnot available
FundersAnalysis and Testing Foundation of Kunming University of Science and TechnologyNational Natural Science Foundation of China
KeywordsSupercritical fluidCatalysisActivated carbonFourier transform infrared spectroscopyAdsorptionDecompositionChemical engineeringDesorptionChemistrySpecific surface areaScanning electron microscopeMaterials scienceInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We investigated the changes in activated carbon (AC) physicochemical properties during treatment with supercritical water (SCW). Experimental results show that SCW treatment can increase the specific surface area and pore volume of AC. Scanning electron microscopy proves that the AC surface undergoes severe corrosion, resulting in the exposure of highly porous structures. In addition, the point‐of‐zero charge of AC increases after SCW treatment without the addition of HCl, H 2 SO 4 , and H 2 O 2 . This result is confirmed by the presence of C≡C and −OH functional moieties, as established through Fourier transform infrared spectroscopy. Temperature programmed desorption profiles suggest that the decomposition of AC in the SCW treatment produces pyrone and anhydride functional groups. According to the above results, SCW treatment increases the adsorption capacities of AC. In SCW catalytic processes, therefore, active components removed from the surface of AC‐supported catalysts are directly involved in reactions that enhance the catalytic activity.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.378

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 science0.0000.000
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 head0.010
GPT teacher head0.169
Teacher spread0.160 · 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
Published2018
Admission routes1
Has abstractyes

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