MétaCan
Menu
Back to cohort

Adsorption Behavior of Chemical Contaminants by Molecular Simulation

2003· article· en· W1963620488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the IEST · 2003
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsAdsorptionWaferSiliconMolecular dynamicsMoleculeHydrogenSilicon oxideChemistryChemical engineeringOxideContaminationSurface energyBenzeneMaterials scienceInorganic chemistryPhysical chemistryOrganic chemistryNanotechnologyComputational chemistry

Abstract

fetched live from OpenAlex

The adsorption process of chemical contaminants on silicon wafers was investigated by using molecular mechanics, molecular dynamics, Monte Carlo method, and computer graphics. The adsorption energy of organic gaseous contaminants, such as DBP, DOP, and D5, on the hydrogen terminated silicon surface and its oxide surface, were estimated and compared. The adsorption energy of chemical contaminants was larger than that of low molecular weight species, such as benzene, H 2 O and NH 3 . The adsorption energy on the hydrogen terminated silicon surface was smaller than that on its oxide surface. The equilibrium adsorption amount on the silicon wafer was also calculated. It was indicated that the configuration of the adsorbed DBP molecules over its gas concentration of 1.9x10 3 ng/m 3 showed multi-layer adsorption. It was indicated that adsorption energy and the equilibrium adsorption amount is a good index to estimate the adsorption properties of organic molecules.

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.

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 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 score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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