MétaCan
Menu
Back to cohort
Record W357019876

Green Solvents for Precision Cleaning

2013· article· en· W357019876 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNASA STI Repository (National Aeronautics and Space Administration) · 2013
Typearticle
Languageen
FieldComputer Science
TopicChemical and Environmental Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmentally friendlyAerospaceWaste managementFlammable liquidEnvironmental scienceProcess engineeringSolventMaterials scienceChemistryEngineeringOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Aerospace machinery used in liquid oxygen (LOX) fuel systems must be precision cleaned to achieve a very low level of non-volatile residue (< 1 mg0.1 m2), especially flammable residue. Traditionally chlorofluorocarbons (CFCs) have been used in the precision cleaning of LOX systems, specifically CFC 113 (C2Cl3F3). CFCs have been known to cause the depletion of ozone and in 1987, were banned by the Montreal Protocol due to health, safety and environmental concerns. This has now led to the development of new processes in the precision cleaning of aerospace components. An ideal solvent-replacement is non-flammable, environmentally benign, non-corrosive, inexpensive, effective and evaporates completely, leaving no residue. Highlighted is a green precision cleaning process, which is contaminant removal using supercritical carbon dioxide as the environmentally benign solvent. In this process, the contaminant is dissolved in carbon dioxide, and the parts are recovered at the end of the cleaning process completely dry and ready for use. Typical contaminants of aerospace components include hydrocarbon greases, hydraulic fluids, silicone fluids and greases, fluorocarbon fluids and greases and fingerprint oil. Metallic aerospace components range from small nuts and bolts to much larger parts, such as butterfly valves 18 in diameter. A fluorinated grease, Krytox, is investigated as a model contaminant in these preliminary studies, and aluminum coupons are employed as a model aerospace component. Preliminary studies are presented in which the experimental parameters are optimized for removal of Krytox from aluminum coupons in a stirred-batch process. The experimental conditions investigated are temperature, pressure, exposure time and impeller speed. Temperatures of 308 - 423 K, pressures in the range of 8.3 - 41.4 MPa, exposure times between 5 - 60 min and impeller speeds of 0 - 1000 rpm were investigated. Preliminary results showed up to 86 cleaning efficiency with the moderate processing conditions of 323 K, 13.8 MPa, 30 min and 750 rpm.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.376

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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