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Record W3196899120 · doi:10.3390/catal11091075

On the Use of Dual Cell Density Monoliths

2021· article· en· W3196899120 on OpenAlexaff
Iván Cornejo, Gonzalo Garretón, Robert E. Hayes

Bibliographic record

VenueCatalysts · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Alberta
FundersFondo Nacional de Desarrollo Científico y Tecnológico
KeywordsPressure dropMaterials scienceMechanicsMonolithDrop (telecommunication)Chemical engineeringChemistryCatalysisMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Monolith-type substrates are extensively used in automotive catalytic converters and have gained popularity in several other industrial processes. Despite their advantages over traditional unstructured catalysts, such as large surface area and low pressure drop, novel monolith configurations have not been investigated in depth. In this paper, we use a detailed computational model at the reactor scale, which considers entrance length, turbulence dissipation and internal diffusion limitations, to investigate the impact of using a dual cell substrate on conversion efficiency, pressure drop, and flow distribution. The substrate is divided into two concentric regions, one at its core and one at its periphery, and a different cell density is given to each part. According to the results, a difference of 40% in apparent permeability is sufficient to lead to a large flow maldistribution, which impacts conversion efficiency and pressure drop. The two mentioned variables show a positive or negative correlation depending on what part of the substrate—core or ring—has the highest permeability. This and other results contribute relevant evidence for further monolith optimization.

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 score0.003
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.043
GPT teacher head0.242
Teacher spread0.200 · 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
Published2021
Admission routes1
Has abstractyes

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