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Record W2758191325 · doi:10.1021/acs.jpcc.7b08642

Specific Surface Area from Nitrogen Adsorption Data at 77 K Using the Zeta Adsorption Isotherm

2017· article· en· W2758191325 on OpenAlexafffund
S. Arman Ghaffarizadeh, Seyed Hadi Zandavi, C. A. Ward

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Space Agency
KeywordsAdsorptionZeta potentialSorption isothermStandard deviationNitrogenThermodynamicsSpecific surface areaConsistency (knowledge bases)Materials scienceSurface (topology)Analytical Chemistry (journal)ChemistryMathematicsChromatographyPhysical chemistryPhysicsNanotechnologyStatisticsOrganic chemistryGeometryNanoparticle

Abstract

fetched live from OpenAlex

The determination of the specific surface area (SSA) of solid powders has been a long-standing problem in surface science. We use the zeta adsorption isotherm and propose a method for determining the SSA of powders using the N 2 adsorption measurements at 77 K. The consistency of the results obtained is demonstrated using two α-alumina samples that have different total surface areas. When the proposed method is applied to convert the amount adsorbed per unit mass to the amount adsorbed per unit area, we show that there is no measurable difference between their adsorption isotherms. Also, we show the proposed method can be applied to six different powders having a variation in their specific surface areas of two orders of magnitude. The corresponding error can be obtained from a single equilibrium adsorption measurement, and the maximum standard deviation in the mean value of the SSA among six cases is less than 7%.

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)0.001
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 score0.009
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.304
Teacher spread0.237 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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