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Record W2167256546 · doi:10.1109/ectc.1991.163907

HCFC cleaning agents as alternatives for chlorofluorocarbons (CFCs) in the electronics industry

2002· article· en· W2167256546 on OpenAlexaboutno aff
B.C. Smiley, D.J. Heden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsMethanolCleaning agentProcess engineeringElectronicsComputer scienceEnvironmental scienceBiochemical engineeringManufacturing engineeringPulp and paper industryChemistryEngineeringElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The planned phase-out of chlorofluorocarbons (CFCs) set forth in the Montreal Protocol has required CFC-113 users to examine alternative technologies for cleaning printed wiring boards (PWBs). One class of alternatives is HCFCs or hydrochlorofluorocarbon-based cleaning agents. The authors discuss the effectiveness and ecology of two HCFC alternatives (HCFC-123 and HCFC-141b), presenting the advantages and tradeoffs of each. They also compare the properties and performance of a specific HCFC blend designed for electronics cleaning (consisting of 62.2% HCFC-141b, 35% HCFC-123, 2.5% methanol, and 0.3% stabilizer) to those of CFC-113/methanol, the current industry standard. This HCFC blend has exceeded the performance of CFC-113/methanol for removing ionic contamination and residual rosin. In addition, the authors describe the equipment and process design considerations for HCFC alternatives.>

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.290
Teacher spread0.249 · 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 designNot applicable
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

Citations0
Published2002
Admission routes1
Has abstractyes

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