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Record W2036886001 · doi:10.1177/1082013203035261

Mass Transfer Flux at Solid-Liquid Contacting Interface

2003· article· en· W2036886001 on OpenAlexafffund
Junling Shi, Marc Le Maguer

Bibliographic record

VenueFood Science and Technology International · 2003
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMass transferCountercurrent exchangeIsothermal processShrinkageChemistryMass transfer coefficientProcess (computing)Matrix (chemical analysis)Component (thermodynamics)Materials scienceMechanicsThermodynamicsChromatographyComposite material

Abstract

fetched live from OpenAlex

When cellular materials are immersed in a solution of high concentration, the mass transfer is considered a multi-component transport process in which simultaneous and countercurrent flows occur in the biological tissue. The mass transfer process of each component in the solid-liquid system is affected not only by operational parameters, but also by the presence of other components. The main driving force for the mass transfer phenomena is attributed to the concentration gradient between external and internal solid material, and the interaction among fluxes and tissue matrix. Matrix deformation and tissue shrinkage are important characteristics influencing mass transfer. The coupled mass transfer fluxes across the interface in an isothermal solid-liquid system were analysed by means of a film model as a function of the concentration gradients, based on the generalised multi-component mass transfer theories. Several possible situations of cell structure changes are discussed, and the effect of structural shrinkage on mass transfer is modelled in this study. The mass transfer model and parameters proposed in the present study is applicable to the unit operation such as osmotic treatment of cellular porous biomaterial.

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)0.001
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 score0.033
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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

Citations11
Published2003
Admission routes2
Has abstractyes

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