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Record W2905034328 · doi:10.1039/c8ra08587k

Study of hydrogen explosion control measures by using <scp>l</scp>-phenylalanine for aluminum wet dust removal systems

2018· article· en· W2905034328 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

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsStillwater (Canada)
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsPhenylalanineAluminiumHydrogenChemistryChemical engineeringBiochemistryEngineeringAmino acidOrganic chemistry

Abstract

fetched live from OpenAlex

, essentially no hydrogen gas was produced. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) were used to characterize the aluminum particles before and after the reaction. This work shows that l-phenylalanine is a good inhibitor. The adsorption of l-phenylalanine on the aluminum particle surface obeys the Langmuir adsorption isotherm. Additionally, Fourier transform infrared (FTIR) analysis was conducted to explain the physicochemical mechanism of the l-phenylalanine inhibition of hydrogen production. l-Phenylalanine is an environmentally friendly inhibitor and hence can be used in wet dust removal systems for the treatment of aluminum dust, which can reduce the hydrogen fire and explosion risk.

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 score0.366
Threshold uncertainty score0.583

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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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