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Record W1486992877 · doi:10.13182/nt02-a3342

A Study of the Kinetics of I<sub>2</sub>Deposition on Stainless Steel Sampling Lines

2002· article· en· W1486992877 on OpenAlexaff
Greg J. Evans, Tutun Nugraha

Bibliographic record

VenueNuclear Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelative humidityChemisorptionAdsorptionDesorptionDeposition (geology)ChemistryCorrosionIodineAnalytical Chemistry (journal)HumidityMaterials scienceMetallurgyEnvironmental chemistryMeteorology

Abstract

fetched live from OpenAlex

In this study, deposition of I2(g) on stainless steel tubing was investigated. The purpose was to quantify the rate of iodine deposition and desorption, as well as to elucidate the underlying mechanisms. The parameters included I2 gas phase concentration (10–7 to 10–11 M), relative humidity (<25 to 100%), tube surface temperature (23 to 90°C), and steel type (SS-304L and SS-316L). Gaseous I2 was found to deposit through both physical and chemical adsorption with deposition velocities ranging from 5 × 10–3 to 1.0 cm/s. At concentrations below 10–9 M, I2 rapidly deposited and was easily desorbed, consistent with physical adsorption. At concentrations above 10–9 M and low relative humidity (<25%), both adsorption and desorption were slow, consistent with a slow chemisorption process. At high relative humidity (>75%), rapid chemisorption with pitting corrosion occurred. Under some conditions, adsorption became inhibited resulting in an apparent maximum surface loading. At high iodine concentration, high relative humidity, and tube temperatures of 40 or 60°C, no such inhibition occurred, resulting in rapid and continuous iodine adsorption.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2002
Admission routes1
Has abstractyes

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