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Record W2501277617 · doi:10.1021/bk-2014-1173.ch004

Soft-Templated Mesoporous Carbons: Chemistry and Structural Characteristics

2014· book-chapter· en· W2501277617 on OpenAlexaff
Dipendu Saha, Renju Zacharia, Amit K. Naskar

Bibliographic record

VenueACS symposium series · 2014
Typebook-chapter
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMesoporous materialCarbon fibersAmphiphileMicroporous materialNanotechnologyMaterials scienceAdsorptionChemistryChemical engineeringOrganic chemistryCopolymerCatalysisComposite materialPolymer

Abstract

fetched live from OpenAlex

Soft-templated mesoporous carbon is a relatively newer variety of synthetic nanoporous carbon. This type of carbon is templated by the micellar actions of amphiphilic surfactants with the phenolic type of carbon precursors. The role of surfactants is similar to that of silica in the case of hard-templating, i.e., to dictate the structural characteristics and mesoporosity of the carbons. A wide variety of amphiphilic surfactants along with synthetic and natural precursors were employed for use as a carbon source. Despite being termed as mesoporous carbon, these materials usually contain a large fraction of micropore distribution which is clearly observed in the type IV nature of nitrogen adsorption-desorption plot. Because of the unique mesopore feature of such carbon, it has found wide application in several unique fields of science and technology. This particular review briefly discusses the synthesis protocols of mesoporous carbons, including the types of carbon precursors and surfactants along with their critical role in dictating the structural characteristics. It also introduces the role of physical and chemical activation in enhancing the porosity and the key structural features of soft-templated mesoporous carbons.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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